Merge branch 'master' into wfh/rm_context_api

core: Fix test_stream_error_callback (#30228 )
Fixes #29436 --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2026-02-18 12:34:25 +00:00 · 2025-03-31 10:47:38 -04:00 · 2025-03-31 10:37:22 -04:00 · 2025-03-31 10:16:15 -04:00 · 2025-03-31 10:08:02 -04:00 · 2025-03-31 10:05:23 -04:00
238 changed files with 28888 additions and 24927 deletions
--- a/cookbook/Gemma_LangChain.ipynb
+++ b/cookbook/Gemma_LangChain.ipynb
@@ -60,7 +60,7 @@
    "id": "CI8Elyc5gBQF"
   },
   "source": [
-    "Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint it ready, you need to copy its number."
+    "Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint is ready, you need to copy its number."
   ]
  },
  {
--- a/cookbook/img-to_img-search_CLIP_ChromaDB.ipynb
+++ b/cookbook/img-to_img-search_CLIP_ChromaDB.ipynb
@@ -358,7 +358,7 @@
   "id": "6e5cd014-db86-4d6b-8399-25cae3da5570",
   "metadata": {},
   "source": [
-    "## Helper function to plot retrived similar images"
+    "## Helper function to plot retrieved similar images"
   ]
  },
  {
--- a/docs/api_reference/templates/runnable_non_pydantic.rst
+++ b/docs/api_reference/templates/runnable_non_pydantic.rst
@@ -7,7 +7,7 @@

 .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃

-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
+    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_config <langchain_core.runnables.base.Runnable.with_config>`, :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.

   {% block attributes %}
   {% if attributes %}
--- a/docs/api_reference/templates/runnable_pydantic.rst
+++ b/docs/api_reference/templates/runnable_pydantic.rst
@@ -19,6 +19,6 @@

    .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃

-        The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
+        The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_config <langchain_core.runnables.base.Runnable.with_config>`, :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.

 .. example_links:: {{ objname }}
--- a/docs/cassettes/chat_token_usage_tracking_05f22a1d-b021-490f-8840-f628a07459f2.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_05f22a1d-b021-490f-8840-f628a07459f2.msgpack.zlib
@@ -1 +0,0 @@
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--- a/docs/cassettes/chat_token_usage_tracking_07f0c872-6b6c-4fed-a129-9b5a858505be.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_07f0c872-6b6c-4fed-a129-9b5a858505be.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_0b1523d8-127e-4314-82fa-bd97aca37f9a.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_0b1523d8-127e-4314-82fa-bd97aca37f9a.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_1837c807-136a-49d8-9c33-060e58dc16d2.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_1837c807-136a-49d8-9c33-060e58dc16d2.msgpack.zlib
@@ -1 +0,0 @@
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--- a/docs/cassettes/chat_token_usage_tracking_3950d88b-8bfb-4294-b75b-e6fd421e633c.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_3950d88b-8bfb-4294-b75b-e6fd421e633c.msgpack.zlib
--- a/docs/cassettes/chat_token_usage_tracking_4728f55a-24e1-48cd-a195-09d037821b1e.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_4728f55a-24e1-48cd-a195-09d037821b1e.msgpack.zlib
@@ -0,0 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_67117f2b-ce68-4c1e-9556-2d3849f90e1b.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_67117f2b-ce68-4c1e-9556-2d3849f90e1b.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_9c82ff80-ec4e-4049-b019-5f0bbd7df82a.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_9c82ff80-ec4e-4049-b019-5f0bbd7df82a.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_b04a4486-72fd-48ce-8f9e-5d281b441195.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_b04a4486-72fd-48ce-8f9e-5d281b441195.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_b39bf807-4125-4db4-bbf7-28a46afff6b4.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_b39bf807-4125-4db4-bbf7-28a46afff6b4.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/chat_token_usage_tracking_c00c9158-7bb4-4279-88e6-ea70f46e6ac2.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_c00c9158-7bb4-4279-88e6-ea70f46e6ac2.msgpack.zlib
@@ -1 +0,0 @@
-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
--- a/docs/cassettes/chat_token_usage_tracking_fe945078-ee2d-43ba-8cdf-afb2f2f4ecef.msgpack.zlib
+++ b/docs/cassettes/chat_token_usage_tracking_fe945078-ee2d-43ba-8cdf-afb2f2f4ecef.msgpack.zlib
@@ -0,0 +1 @@
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
--- a/docs/docs/how_to/chat_token_usage_tracking.ipynb
+++ b/docs/docs/how_to/chat_token_usage_tracking.ipynb
@@ -16,7 +16,7 @@
    "\n",
    "Tracking [token](/docs/concepts/tokens/) usage to calculate cost is an important part of putting your app in production. This guide goes over how to obtain this information from your LangChain model calls.\n",
    "\n",
-    "This guide requires `langchain-anthropic` and `langchain-openai >= 0.1.9`."
+    "This guide requires `langchain-anthropic` and `langchain-openai >= 0.3.11`."
   ]
  },
  {
@@ -38,19 +38,9 @@
    "\n",
    "OpenAI's Chat Completions API does not stream token usage statistics by default (see API reference\n",
    "[here](https://platform.openai.com/docs/api-reference/completions/create#completions-create-stream_options)).\n",
-    "To recover token counts when streaming with `ChatOpenAI`, set `stream_usage=True` as\n",
+    "To recover token counts when streaming with `ChatOpenAI` or `AzureChatOpenAI`, set `stream_usage=True` as\n",
    "demonstrated in this guide.\n",
    "\n",
-    "For `AzureChatOpenAI`, set `stream_options={\"include_usage\": True}` when calling\n",
-    "`.(a)stream`, or initialize with:\n",
-    "\n",
-    "```python\n",
-    "AzureChatOpenAI(\n",
-    "    ...,\n",
-    "    model_kwargs={\"stream_options\": {\"include_usage\": True}},\n",
-    ")\n",
-    "```\n",
-    "\n",
    ":::"
   ]
  },
@@ -67,7 +57,7 @@
    "\n",
    "A number of model providers return token usage information as part of the chat generation response. When available, this information will be included on the `AIMessage` objects produced by the corresponding model.\n",
    "\n",
-    "LangChain `AIMessage` objects include a [usage_metadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.usage_metadata) attribute. When populated, this attribute will be a [UsageMetadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html) dictionary with standard keys (e.g., `\"input_tokens\"` and `\"output_tokens\"`).\n",
+    "LangChain `AIMessage` objects include a [usage_metadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.usage_metadata) attribute. When populated, this attribute will be a [UsageMetadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html) dictionary with standard keys (e.g., `\"input_tokens\"` and `\"output_tokens\"`). They will also include information on cached token usage and tokens from multi-modal data.\n",
    "\n",
    "Examples:\n",
    "\n",
@@ -92,9 +82,9 @@
    }
   ],
   "source": [
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain.chat_models import init_chat_model\n",
    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "llm = init_chat_model(model=\"gpt-4o-mini\")\n",
    "openai_response = llm.invoke(\"hello\")\n",
    "openai_response.usage_metadata"
   ]
@@ -132,37 +122,6 @@
    "anthropic_response.usage_metadata"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "6d4efc15-ba9f-4b3d-9278-8e01f99f263f",
-   "metadata": {},
-   "source": [
-    "### Using AIMessage.response_metadata\n",
-    "\n",
-    "Metadata from the model response is also included in the AIMessage [response_metadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.response_metadata) attribute. These data are typically not standardized. Note that different providers adopt different conventions for representing token counts:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f156f9da-21f2-4c81-a714-54cbf9ad393e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OpenAI: {'completion_tokens': 9, 'prompt_tokens': 8, 'total_tokens': 17}\n",
-      "\n",
-      "Anthropic: {'input_tokens': 8, 'output_tokens': 12}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'OpenAI: {openai_response.response_metadata[\"token_usage\"]}\\n')\n",
-    "print(f'Anthropic: {anthropic_response.response_metadata[\"usage\"]}')"
-   ]
-  },
  {
   "cell_type": "markdown",
   "id": "b4ef2c43-0ff6-49eb-9782-e4070c9da8d7",
@@ -207,7 +166,7 @@
    }
   ],
   "source": [
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "llm = init_chat_model(model=\"gpt-4o-mini\")\n",
    "\n",
    "aggregate = None\n",
    "for chunk in llm.stream(\"hello\", stream_usage=True):\n",
@@ -318,7 +277,7 @@
    "    punchline: str = Field(description=\"answer to resolve the joke\")\n",
    "\n",
    "\n",
-    "llm = ChatOpenAI(\n",
+    "llm = init_chat_model(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    stream_usage=True,\n",
    ")\n",
@@ -326,10 +285,10 @@
    "# chat model and appends a parser.\n",
    "structured_llm = llm.with_structured_output(Joke)\n",
    "\n",
-    "async for event in structured_llm.astream_events(\"Tell me a joke\", version=\"v2\"):\n",
+    "async for event in structured_llm.astream_events(\"Tell me a joke\"):\n",
    "    if event[\"event\"] == \"on_chat_model_end\":\n",
    "        print(f'Token usage: {event[\"data\"][\"output\"].usage_metadata}\\n')\n",
-    "    elif event[\"event\"] == \"on_chain_end\":\n",
+    "    elif event[\"event\"] == \"on_chain_end\" and event[\"name\"] == \"RunnableSequence\":\n",
    "        print(event[\"data\"][\"output\"])\n",
    "    else:\n",
    "        pass"
@@ -350,17 +309,18 @@
   "source": [
    "## Using callbacks\n",
    "\n",
-    "There are also some API-specific callback context managers that allow you to track token usage across multiple calls. They are currently only implemented for the OpenAI API and Bedrock Anthropic API, and are available in `langchain-community`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64e52d21",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
+    ":::info Requires ``langchain-core>=0.3.49``\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "LangChain implements a callback handler and context manager that will track token usage across calls of any chat model that returns `usage_metadata`.\n",
+    "\n",
+    "There are also some API-specific callback context managers that maintain pricing for different models, allowing for cost estimation in real time. They are currently only implemented for the OpenAI API and Bedrock Anthropic API, and are available in `langchain-community`:\n",
+    "\n",
+    "- [get_openai_callback](https://python.langchain.com/api_reference/community/callbacks/langchain_community.callbacks.manager.get_openai_callback.html)\n",
+    "- [get_bedrock_anthropic_callback](https://python.langchain.com/api_reference/community/callbacks/langchain_community.callbacks.manager.get_bedrock_anthropic_callback.html)\n",
+    "\n",
+    "Below, we demonstrate the general-purpose usage metadata callback manager. We can track token usage through configuration or as a context manager."
   ]
  },
  {
@@ -368,41 +328,84 @@
   "id": "6f043cb9",
   "metadata": {},
   "source": [
-    "### OpenAI\n",
+    "### Tracking token usage through configuration\n",
    "\n",
-    "Let's first look at an extremely simple example of tracking token usage for a single Chat model call."
+    "To track token usage through configuration, instantiate a `UsageMetadataCallbackHandler` and pass it into the config:"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
   "id": "b04a4486-72fd-48ce-8f9e-5d281b441195",
   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'gpt-4o-mini-2024-07-18': {'input_tokens': 8,\n",
+       "  'output_tokens': 10,\n",
+       "  'total_tokens': 18,\n",
+       "  'input_token_details': {'audio': 0, 'cache_read': 0},\n",
+       "  'output_token_details': {'audio': 0, 'reasoning': 0}},\n",
+       " 'claude-3-5-haiku-20241022': {'input_tokens': 8,\n",
+       "  'output_tokens': 21,\n",
+       "  'total_tokens': 29,\n",
+       "  'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain_core.callbacks import UsageMetadataCallbackHandler\n",
+    "\n",
+    "llm_1 = init_chat_model(model=\"openai:gpt-4o-mini\")\n",
+    "llm_2 = init_chat_model(model=\"anthropic:claude-3-5-haiku-latest\")\n",
+    "\n",
+    "callback = UsageMetadataCallbackHandler()\n",
+    "result_1 = llm_1.invoke(\"Hello\", config={\"callbacks\": [callback]})\n",
+    "result_2 = llm_2.invoke(\"Hello\", config={\"callbacks\": [callback]})\n",
+    "callback.usage_metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a290085-e541-4233-afe4-637ec5032bfd",
+   "metadata": {},
+   "source": [
+    "### Tracking token usage using a context manager\n",
+    "\n",
+    "You can also use `get_usage_metadata_callback` to create a context manager and aggregate usage metadata there:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4728f55a-24e1-48cd-a195-09d037821b1e",
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Tokens Used: 27\n",
-      "\tPrompt Tokens: 11\n",
-      "\tCompletion Tokens: 16\n",
-      "Successful Requests: 1\n",
-      "Total Cost (USD): $2.95e-05\n"
+      "{'gpt-4o-mini-2024-07-18': {'input_tokens': 8, 'output_tokens': 10, 'total_tokens': 18, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}, 'claude-3-5-haiku-20241022': {'input_tokens': 8, 'output_tokens': 21, 'total_tokens': 29, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}\n"
     ]
    }
   ],
   "source": [
-    "from langchain_community.callbacks.manager import get_openai_callback\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain_core.callbacks import get_usage_metadata_callback\n",
    "\n",
-    "llm = ChatOpenAI(\n",
-    "    model=\"gpt-4o-mini\",\n",
-    "    temperature=0,\n",
-    "    stream_usage=True,\n",
-    ")\n",
+    "llm_1 = init_chat_model(model=\"openai:gpt-4o-mini\")\n",
+    "llm_2 = init_chat_model(model=\"anthropic:claude-3-5-haiku-latest\")\n",
    "\n",
-    "with get_openai_callback() as cb:\n",
-    "    result = llm.invoke(\"Tell me a joke\")\n",
-    "    print(cb)"
+    "with get_usage_metadata_callback() as cb:\n",
+    "    llm_1.invoke(\"Hello\")\n",
+    "    llm_2.invoke(\"Hello\")\n",
+    "    print(cb.usage_metadata)"
   ]
  },
  {
@@ -410,61 +413,7 @@
   "id": "c0ab6d27",
   "metadata": {},
   "source": [
-    "Anything inside the context manager will get tracked. Here's an example of using it to track multiple calls in sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "05f22a1d-b021-490f-8840-f628a07459f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54\n"
-     ]
-    }
-   ],
-   "source": [
-    "with get_openai_callback() as cb:\n",
-    "    result = llm.invoke(\"Tell me a joke\")\n",
-    "    result2 = llm.invoke(\"Tell me a joke\")\n",
-    "    print(cb.total_tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "c00c9158-7bb4-4279-88e6-ea70f46e6ac2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tokens Used: 27\n",
-      "\tPrompt Tokens: 11\n",
-      "\tCompletion Tokens: 16\n",
-      "Successful Requests: 1\n",
-      "Total Cost (USD): $2.95e-05\n"
-     ]
-    }
-   ],
-   "source": [
-    "with get_openai_callback() as cb:\n",
-    "    for chunk in llm.stream(\"Tell me a joke\"):\n",
-    "        pass\n",
-    "    print(cb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8186e7b",
-   "metadata": {},
-   "source": [
-    "If a chain or agent with multiple steps in it is used, it will track all those steps."
+    "Either of these methods will aggregate token usage across multiple calls to each model. For example, you can use it in an [agent](https://python.langchain.com/docs/concepts/agents/) to track token usage across repeated calls to one model:"
   ]
  },
  {
@@ -474,138 +423,63 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "%pip install -qU langchain langchain-aws wikipedia"
+    "%pip install -qU langgraph"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
-   "id": "5d1125c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent, load_tools\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You're a helpful assistant\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
-    "    ]\n",
-    ")\n",
-    "tools = load_tools([\"wikipedia\"])\n",
-    "agent = create_tool_calling_agent(llm, tools, prompt)\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "3950d88b-8bfb-4294-b75b-e6fd421e633c",
+   "execution_count": 20,
+   "id": "fe945078-ee2d-43ba-8cdf-afb2f2f4ecef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
+      "What's the weather in Boston?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  get_weather (call_izMdhUYpp9Vhx7DTNAiybzGa)\n",
+      " Call ID: call_izMdhUYpp9Vhx7DTNAiybzGa\n",
+      "  Args:\n",
+      "    location: Boston\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: get_weather\n",
      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `wikipedia` with `{'query': 'hummingbird scientific name'}`\n",
+      "It's sunny.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
+      "The weather in Boston is sunny.\n",
      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mPage: Hummingbird\n",
-      "Summary: Hummingbirds are birds native to the Americas and comprise the biological family Trochilidae. With approximately 366 species and 113 genera, they occur from Alaska to Tierra del Fuego, but most species are found in Central and South America. As of 2024, 21 hummingbird species are listed as endangered or critically endangered, with numerous species declining in population.\n",
-      "Hummingbirds have varied specialized characteristics to enable rapid, maneuverable flight: exceptional metabolic capacity, adaptations to high altitude, sensitive visual and communication abilities, and long-distance migration in some species. Among all birds, male hummingbirds have the widest diversity of plumage color, particularly in blues, greens, and purples. Hummingbirds are the smallest mature birds, measuring 7.5–13 cm (3–5 in) in length. The smallest is the 5 cm (2.0 in) bee hummingbird, which weighs less than 2.0 g (0.07 oz), and the largest is the 23 cm (9 in) giant hummingbird, weighing 18–24 grams (0.63–0.85 oz). Noted for long beaks, hummingbirds are specialized for feeding on flower nectar, but all species also consume small insects.\n",
-      "They are known as hummingbirds because of the humming sound created by their beating wings, which flap at high frequencies audible to other birds and humans. They hover at rapid wing-flapping rates, which vary from around 12 beats per second in the largest species to 80 per second in small hummingbirds.\n",
-      "Hummingbirds have the highest mass-specific metabolic rate of any homeothermic animal. To conserve energy when food is scarce and at night when not foraging, they can enter torpor, a state similar to hibernation, and slow their metabolic rate to 1⁄15 of its normal rate. While most hummingbirds do not migrate, the rufous hummingbird has one of the longest migrations among birds, traveling twice per year between Alaska and Mexico, a distance of about 3,900 miles (6,300 km).\n",
-      "Hummingbirds split from their sister group, the swifts and treeswifts, around 42 million years ago. The oldest known fossil hummingbird is Eurotrochilus, from the Rupelian Stage of Early Oligocene Europe.\n",
-      "\n",
-      "Page: Rufous hummingbird\n",
-      "Summary: The rufous hummingbird (Selasphorus rufus) is a small hummingbird, about 8 cm (3.1 in) long with a long, straight and slender bill. These birds are known for their extraordinary flight skills, flying 2,000 mi (3,200 km) during their migratory transits. It is one of nine species in the genus Selasphorus.\n",
-      "\n",
-      "\n",
-      "\n",
-      "Page: Allen's hummingbird\n",
-      "Summary: Allen's hummingbird (Selasphorus sasin) is a species of hummingbird that breeds in the western United States. It is one of seven species in the genus Selasphorus.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `wikipedia` with `{'query': 'fastest bird species'}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mPage: List of birds by flight speed\n",
-      "Summary: This is a list of the fastest flying birds in the world. A bird's velocity is necessarily variable; a hunting bird will reach much greater speeds while diving to catch prey than when flying horizontally. The bird that can achieve the greatest airspeed is the peregrine falcon (Falco peregrinus), able to exceed 320 km/h (200 mph) in its dives. A close relative of the common swift, the white-throated needletail (Hirundapus caudacutus), is commonly reported as the fastest bird in level flight with a reported top speed of 169 km/h (105 mph). This record remains unconfirmed as the measurement methods have never been published or verified. The record for the fastest confirmed level flight by a bird is 111.5 km/h (69.3 mph) held by the common swift.\n",
-      "\n",
-      "Page: Fastest animals\n",
-      "Summary: This is a list of the fastest animals in the world, by types of animal.\n",
-      "\n",
-      "Page: Falcon\n",
-      "Summary: Falcons () are birds of prey in the genus Falco, which includes about 40 species. Falcons are widely distributed on all continents of the world except Antarctica, though closely related raptors did occur there in the Eocene.\n",
-      "Adult falcons have thin, tapered wings, which enable them to fly at high speed and change direction rapidly. Fledgling falcons, in their first year of flying, have longer flight feathers, which make their configuration more like that of a general-purpose bird such as a broad wing. This makes flying easier while learning the exceptional skills required to be effective hunters as adults.\n",
-      "The falcons are the largest genus in the Falconinae subfamily of Falconidae, which itself also includes another subfamily comprising caracaras and a few other species. All these birds kill with their beaks, using a tomial \"tooth\" on the side of their beaks—unlike the hawks, eagles, and other birds of prey in the Accipitridae, which use their feet.\n",
-      "The largest falcon is the gyrfalcon at up to 65 cm in length.  The smallest falcon species is the pygmy falcon, which measures just 20 cm.  As with hawks and owls, falcons exhibit sexual dimorphism, with the females typically larger than the males, thus allowing a wider range of prey species.\n",
-      "Some small falcons with long, narrow wings are called \"hobbies\" and some which hover while hunting are called \"kestrels\".\n",
-      "As is the case with many birds of prey, falcons have exceptional powers of vision; the visual acuity of one species has been measured at 2.6 times that of a normal human. Peregrine falcons have been recorded diving at speeds of 320 km/h (200 mph), making them the fastest-moving creatures on Earth; the fastest recorded dive attained a vertical speed of 390 km/h (240 mph).\u001b[0m\u001b[32;1m\u001b[1;3mThe scientific name for a hummingbird is Trochilidae. The fastest bird species in level flight is the common swift, which holds the record for the fastest confirmed level flight by a bird at 111.5 km/h (69.3 mph). The peregrine falcon is known to exceed speeds of 320 km/h (200 mph) in its dives, making it the fastest bird in terms of diving speed.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "Total Tokens: 1675\n",
-      "Prompt Tokens: 1538\n",
-      "Completion Tokens: 137\n",
-      "Total Cost (USD): $0.0009745000000000001\n"
+      "Total usage: {'gpt-4o-mini-2024-07-18': {'input_token_details': {'audio': 0, 'cache_read': 0}, 'input_tokens': 125, 'total_tokens': 149, 'output_tokens': 24, 'output_token_details': {'audio': 0, 'reasoning': 0}}}\n"
     ]
    }
   ],
   "source": [
-    "with get_openai_callback() as cb:\n",
-    "    response = agent_executor.invoke(\n",
-    "        {\n",
-    "            \"input\": \"What's a hummingbird's scientific name and what's the fastest bird species?\"\n",
-    "        }\n",
-    "    )\n",
-    "    print(f\"Total Tokens: {cb.total_tokens}\")\n",
-    "    print(f\"Prompt Tokens: {cb.prompt_tokens}\")\n",
-    "    print(f\"Completion Tokens: {cb.completion_tokens}\")\n",
-    "    print(f\"Total Cost (USD): ${cb.total_cost}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebc9122b-050b-4006-b763-264b0b26d9df",
-   "metadata": {},
-   "source": [
-    "### Bedrock Anthropic\n",
+    "from langgraph.prebuilt import create_react_agent\n",
    "\n",
-    "The `get_bedrock_anthropic_callback` works very similarly:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1837c807-136a-49d8-9c33-060e58dc16d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tokens Used: 96\n",
-      "\tPrompt Tokens: 26\n",
-      "\tCompletion Tokens: 70\n",
-      "Successful Requests: 2\n",
-      "Total Cost (USD): $0.001888\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_aws import ChatBedrock\n",
-    "from langchain_community.callbacks.manager import get_bedrock_anthropic_callback\n",
    "\n",
-    "llm = ChatBedrock(model_id=\"anthropic.claude-v2\")\n",
+    "# Create a tool\n",
+    "def get_weather(location: str) -> str:\n",
+    "    \"\"\"Get the weather at a location.\"\"\"\n",
+    "    return \"It's sunny.\"\n",
    "\n",
-    "with get_bedrock_anthropic_callback() as cb:\n",
-    "    result = llm.invoke(\"Tell me a joke\")\n",
-    "    result2 = llm.invoke(\"Tell me a joke\")\n",
-    "    print(cb)"
+    "\n",
+    "callback = UsageMetadataCallbackHandler()\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "agent = create_react_agent(\"openai:gpt-4o-mini\", tools)\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"What's the weather in Boston?\"}]},\n",
+    "    stream_mode=\"values\",\n",
+    "    config={\"callbacks\": [callback]},\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()\n",
+    "\n",
+    "\n",
+    "print(f\"\\nTotal usage: {callback.usage_metadata}\")"
   ]
  },
  {
--- a/docs/docs/how_to/custom_chat_model.ipynb
+++ b/docs/docs/how_to/custom_chat_model.ipynb
@@ -247,6 +247,7 @@
    "            additional_kwargs={},  # Used to add additional payload to the message\n",
    "            response_metadata={  # Use for response metadata\n",
    "                \"time_in_seconds\": 3,\n",
+    "                \"model_name\": self.model_name,\n",
    "            },\n",
    "            usage_metadata={\n",
    "                \"input_tokens\": ct_input_tokens,\n",
@@ -309,7 +310,10 @@
    "\n",
    "        # Let's add some other information (e.g., response metadata)\n",
    "        chunk = ChatGenerationChunk(\n",
-    "            message=AIMessageChunk(content=\"\", response_metadata={\"time_in_sec\": 3})\n",
+    "            message=AIMessageChunk(\n",
+    "                content=\"\",\n",
+    "                response_metadata={\"time_in_sec\": 3, \"model_name\": self.model_name},\n",
+    "            )\n",
    "        )\n",
    "        if run_manager:\n",
    "            # This is optional in newer versions of LangChain\n",
--- a/docs/docs/how_to/qa_citations.ipynb
+++ b/docs/docs/how_to/qa_citations.ipynb
@@ -127,7 +127,7 @@
   "id": "c89e2045-9244-43e6-bf3f-59af22658529",
   "metadata": {},
   "source": [
-    "Now that we've got a [model](/docs/concepts/chat_models/), [retriver](/docs/concepts/retrievers/) and [prompt](/docs/concepts/prompt_templates/), let's chain them all together. Following the how-to guide on [adding citations](/docs/how_to/qa_citations) to a RAG application, we'll make it so our chain returns both the answer and the retrieved Documents. This uses the same [LangGraph](/docs/concepts/architecture/#langgraph) implementation as in the [RAG Tutorial](/docs/tutorials/rag)."
+    "Now that we've got a [model](/docs/concepts/chat_models/), [retriever](/docs/concepts/retrievers/) and [prompt](/docs/concepts/prompt_templates/), let's chain them all together. Following the how-to guide on [adding citations](/docs/how_to/qa_citations) to a RAG application, we'll make it so our chain returns both the answer and the retrieved Documents. This uses the same [LangGraph](/docs/concepts/architecture/#langgraph) implementation as in the [RAG Tutorial](/docs/tutorials/rag)."
   ]
  },
  {
--- a/docs/docs/how_to/query_multiple_queries.ipynb
+++ b/docs/docs/how_to/query_multiple_queries.ipynb
@@ -270,7 +270,7 @@
   "source": [
    "## Retrieval with query analysis\n",
    "\n",
-    "So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asyncronously - this will let us loop over the queries and not get blocked on the response time."
+    "So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asynchronously - this will let us loop over the queries and not get blocked on the response time."
   ]
  },
  {
--- a/docs/docs/how_to/summarize_map_reduce.ipynb
+++ b/docs/docs/how_to/summarize_map_reduce.ipynb
@@ -24,7 +24,7 @@
    "\n",
    "Note that the map step is typically parallelized over the input documents. This strategy is especially effective when understanding of a sub-document does not rely on preceeding context. For example, when summarizing a corpus of many, shorter documents.\n",
    "\n",
-    "[LangGraph](https://langchain-ai.github.io/langgraph/), built on top of `langchain-core`, suports [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) workflows and is well-suited to this problem:\n",
+    "[LangGraph](https://langchain-ai.github.io/langgraph/), built on top of `langchain-core`, supports [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) workflows and is well-suited to this problem:\n",
    "\n",
    "- LangGraph allows for individual steps (such as successive summarizations) to be streamed, allowing for greater control of execution;\n",
    "- LangGraph's [checkpointing](https://langchain-ai.github.io/langgraph/how-tos/persistence/) supports error recovery, extending with human-in-the-loop workflows, and easier incorporation into conversational applications.\n",
--- a/docs/docs/integrations/chat/ai21.ipynb
+++ b/docs/docs/integrations/chat/ai21.ipynb
@@ -1,318 +1,316 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "4cebeec0",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: AI21 Labs\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatAI21\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "This notebook covers how to get started with AI21 chat models.\n",
-    "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
-    "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/) \n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatAI21](https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://python.langchain.com/api_reference/ai21/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    "\n",
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
-    "\n",
-    "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "if \"AI21_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"AI21_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98e22f31-8acc-42d6-916d-415d1263c56e",
-   "metadata": {},
-   "source": [
-    "### Installation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9699cd9-58f2-450e-aa64-799e66906c0f",
-   "metadata": {},
-   "source": [
-    "!pip install -qU langchain-ai21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4828829d3da430ce",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "4cebeec0",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: AI21 Labs\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatAI21\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "This notebook covers how to get started with AI21 chat models.\n",
+        "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
+        "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/)\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatAI21](https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://python.langchain.com/api_reference/ai21/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
+        "\n",
+        "\n",
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "### Credentials\n",
+        "\n",
+        "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "if \"AI21_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"AI21_API_KEY\"] = getpass()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98e22f31-8acc-42d6-916d-415d1263c56e",
+      "metadata": {},
+      "source": [
+        "### Installation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f9699cd9-58f2-450e-aa64-799e66906c0f",
+      "metadata": {},
+      "source": [
+        "!pip install -qU langchain-ai21"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4828829d3da430ce",
+      "metadata": {
+        "collapsed": false,
+        "jupyter": {
+          "outputs_hidden": false
+        }
+      },
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_ai21 import ChatAI21\n",
+        "\n",
+        "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2bdc5d68-2a19-495e-8c04-d11adc86d3ae",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "10a30f84-b531-4fd5-8b5b-91512fbdc75b",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "39353473fce5dd2e",
+      "metadata": {
+        "collapsed": false,
+        "jupyter": {
+          "outputs_hidden": false
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "39c0ccd229927eab",
+      "metadata": {},
+      "source": "# Tool Calls / Function Calling"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2bf6b40be07fe2d4",
+      "metadata": {},
+      "source": "This example shows how to use tool calling with AI21 models:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a181a28df77120fb",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "from langchain_ai21.chat_models import ChatAI21\n",
+        "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
+        "from langchain_core.tools import tool\n",
+        "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+        "\n",
+        "if \"AI21_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"AI21_API_KEY\"] = getpass()\n",
+        "\n",
+        "\n",
+        "@tool\n",
+        "def get_weather(location: str, date: str) -> str:\n",
+        "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
+        "    if location == \"New York\" and date == \"2024-12-05\":\n",
+        "        return \"25 celsius\"\n",
+        "    elif location == \"New York\" and date == \"2024-12-06\":\n",
+        "        return \"27 celsius\"\n",
+        "    elif location == \"London\" and date == \"2024-12-05\":\n",
+        "        return \"22 celsius\"\n",
+        "    return \"32 celsius\"\n",
+        "\n",
+        "\n",
+        "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
+        "\n",
+        "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
+        "\n",
+        "chat_messages = [\n",
+        "    SystemMessage(\n",
+        "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
+        "        \"to assist with various tasks and provide accurate information\"\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "human_messages = [\n",
+        "    HumanMessage(\n",
+        "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
+        "    ),\n",
+        "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
+        "    HumanMessage(content=\"OK, thank you.\"),\n",
+        "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
+        "]\n",
+        "\n",
+        "\n",
+        "for human_message in human_messages:\n",
+        "    print(f\"User: {human_message.content}\")\n",
+        "    chat_messages.append(human_message)\n",
+        "    response = llm_with_tools.invoke(chat_messages)\n",
+        "    chat_messages.append(response)\n",
+        "    if response.tool_calls:\n",
+        "        tool_call = response.tool_calls[0]\n",
+        "        if tool_call[\"name\"] == \"get_weather\":\n",
+        "            weather = get_weather.invoke(\n",
+        "                {\n",
+        "                    \"location\": tool_call[\"args\"][\"location\"],\n",
+        "                    \"date\": tool_call[\"args\"][\"date\"],\n",
+        "                }\n",
+        "            )\n",
+        "            chat_messages.append(\n",
+        "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
+        "            )\n",
+        "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
+        "            print(f\"Assistant: {llm_answer.content}\")\n",
+        "    else:\n",
+        "        print(f\"Assistant: {response.content}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e79de691-9dd6-4697-b57e-59a4a3cc073a",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
+      ]
    }
-   },
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_ai21 import ChatAI21\n",
-    "\n",
-    "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bdc5d68-2a19-495e-8c04-d11adc86d3ae",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10a30f84-b531-4fd5-8b5b-91512fbdc75b",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39353473fce5dd2e",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "39c0ccd229927eab",
-   "metadata": {},
-   "source": "# Tool Calls / Function Calling"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bf6b40be07fe2d4",
-   "metadata": {},
-   "source": "This example shows how to use tool calling with AI21 models:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a181a28df77120fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_ai21.chat_models import ChatAI21\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "\n",
-    "if \"AI21_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"AI21_API_KEY\"] = getpass()\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str, date: str) -> str:\n",
-    "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
-    "    if location == \"New York\" and date == \"2024-12-05\":\n",
-    "        return \"25 celsius\"\n",
-    "    elif location == \"New York\" and date == \"2024-12-06\":\n",
-    "        return \"27 celsius\"\n",
-    "    elif location == \"London\" and date == \"2024-12-05\":\n",
-    "        return \"22 celsius\"\n",
-    "    return \"32 celsius\"\n",
-    "\n",
-    "\n",
-    "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
-    "\n",
-    "chat_messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
-    "        \"to assist with various tasks and provide accurate information\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "human_messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
-    "    HumanMessage(content=\"OK, thank you.\"),\n",
-    "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
-    "]\n",
-    "\n",
-    "\n",
-    "for human_message in human_messages:\n",
-    "    print(f\"User: {human_message.content}\")\n",
-    "    chat_messages.append(human_message)\n",
-    "    response = llm_with_tools.invoke(chat_messages)\n",
-    "    chat_messages.append(response)\n",
-    "    if response.tool_calls:\n",
-    "        tool_call = response.tool_calls[0]\n",
-    "        if tool_call[\"name\"] == \"get_weather\":\n",
-    "            weather = get_weather.invoke(\n",
-    "                {\n",
-    "                    \"location\": tool_call[\"args\"][\"location\"],\n",
-    "                    \"date\": tool_call[\"args\"][\"date\"],\n",
-    "                }\n",
-    "            )\n",
-    "            chat_messages.append(\n",
-    "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
-    "            )\n",
-    "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
-    "            print(f\"Assistant: {llm_answer.content}\")\n",
-    "    else:\n",
-    "        print(f\"Assistant: {response.content}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e79de691-9dd6-4697-b57e-59a4a3cc073a",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/docs/docs/integrations/chat/anthropic.ipynb
--- a/docs/docs/integrations/chat/azure_chat_openai.ipynb
+++ b/docs/docs/integrations/chat/azure_chat_openai.ipynb
@@ -1,349 +1,347 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Azure OpenAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# AzureChatOpenAI\n",
-    "\n",
-    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
-    "\n",
-    "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
-    "\n",
-    ":::info Azure OpenAI vs OpenAI\n",
-    "\n",
-    "Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). OpenAI also provides its own model APIs. To access OpenAI services directly, use the [ChatOpenAI integration](/docs/integrations/chat/openai/).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access AzureOpenAI models you'll need to create an Azure account, create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, get an Azure OpenAI API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python) to create your deployment and generate an API key. Once you've done this set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT environment variables:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"AZURE_OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your AzureOpenAI API key: \"\n",
-    "    )\n",
-    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain AzureOpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions.\n",
-    "- Replace `azure_deployment` with the name of your deployment,\n",
-    "- You can find the latest supported `api_version` here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import AzureChatOpenAI\n",
-    "\n",
-    "llm = AzureChatOpenAI(\n",
-    "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
-    "    api_version=\"2023-06-01-preview\",  # or your api version\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-bea4b46c-e3e1-4495-9d3a-698370ad963d-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Azure OpenAI\n",
+        "---"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-cbc44038-09d3-40d4-9da2-c5910ee636ca-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# AzureChatOpenAI\n",
+        "\n",
+        "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
+        "\n",
+        "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
+        "\n",
+        ":::info Azure OpenAI vs OpenAI\n",
+        "\n",
+        "Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). OpenAI also provides its own model APIs. To access OpenAI services directly, use the [ChatOpenAI integration](/docs/integrations/chat/openai/).\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access AzureOpenAI models you'll need to create an Azure account, create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, get an Azure OpenAI API key, and install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python) to create your deployment and generate an API key. Once you've done this set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT environment variables:"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## Specifying model version\n",
-    "\n",
-    "Azure OpenAI responses contain `model_name` response metadata property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the specific version of the model, which is set on the deployment in Azure. E.g. it does not distinguish between `gpt-35-turbo-0125` and `gpt-35-turbo-0301`. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
-    "\n",
-    "To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04b36e75-e8b7-4721-899e-76301ac2ecd9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2ca02d23-60d0-43eb-8d04-070f61f8fefd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Cost (USD): $0.000063\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.callbacks import get_openai_callback\n",
-    "\n",
-    "with get_openai_callback() as cb:\n",
-    "    llm.invoke(messages)\n",
-    "    print(\n",
-    "        f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\"\n",
-    "    )  # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e1b07ae2-3de7-44bd-bfdc-b76f4ba45a35",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"AZURE_OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your AzureOpenAI API key: \"\n",
+        "    )\n",
+        "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Cost (USD): $0.000074\n"
-     ]
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain AzureOpenAI integration lives in the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions.\n",
+        "- Replace `azure_deployment` with the name of your deployment,\n",
+        "- You can find the latest supported `api_version` here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import AzureChatOpenAI\n",
+        "\n",
+        "llm = AzureChatOpenAI(\n",
+        "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
+        "    api_version=\"2023-06-01-preview\",  # or your api version\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-bea4b46c-e3e1-4495-9d3a-698370ad963d-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore la programmation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-cbc44038-09d3-40d4-9da2-c5910ee636ca-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## Specifying model version\n",
+        "\n",
+        "Azure OpenAI responses contain `model_name` response metadata property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the specific version of the model, which is set on the deployment in Azure. E.g. it does not distinguish between `gpt-35-turbo-0125` and `gpt-35-turbo-0301`. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
+        "\n",
+        "To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "04b36e75-e8b7-4721-899e-76301ac2ecd9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "2ca02d23-60d0-43eb-8d04-070f61f8fefd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Total Cost (USD): $0.000063\n"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_community.callbacks import get_openai_callback\n",
+        "\n",
+        "with get_openai_callback() as cb:\n",
+        "    llm.invoke(messages)\n",
+        "    print(\n",
+        "        f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\"\n",
+        "    )  # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e1b07ae2-3de7-44bd-bfdc-b76f4ba45a35",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Total Cost (USD): $0.000074\n"
+          ]
+        }
+      ],
+      "source": [
+        "llm_0301 = AzureChatOpenAI(\n",
+        "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
+        "    api_version=\"2023-06-01-preview\",  # or your api version\n",
+        "    model_version=\"0301\",\n",
+        ")\n",
+        "with get_openai_callback() as cb:\n",
+        "    llm_0301.invoke(messages)\n",
+        "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
    }
-   ],
-   "source": [
-    "llm_0301 = AzureChatOpenAI(\n",
-    "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
-    "    api_version=\"2023-06-01-preview\",  # or your api version\n",
-    "    model_version=\"0301\",\n",
-    ")\n",
-    "with get_openai_callback() as cb:\n",
-    "    llm_0301.invoke(messages)\n",
-    "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\")"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/bedrock.ipynb
+++ b/docs/docs/integrations/chat/bedrock.ipynb
@@ -1,380 +1,378 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: AWS Bedrock\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatBedrock\n",
-    "\n",
-    "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
-    "\n",
-    "For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
-    "\n",
-    "For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Bedrock integration lives in the `langchain-aws` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-aws"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_aws import ChatBedrock\n",
-    "\n",
-    "llm = ChatBedrock(\n",
-    "    model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-    "    model_kwargs=dict(temperature=0),\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-fdb07dc3-ff72-430d-b22b-e7824b15c766-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: AWS Bedrock\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Voici la traduction en français :\n",
-      "\n",
-      "J'aime la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-5ad005ce-9f31-4670-baa0-9373d418698a-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatBedrock\n",
+        "\n",
+        "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
+        "\n",
+        "For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
+        "\n",
+        "For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)."
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## Bedrock Converse API\n",
-    "\n",
-    "AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
-    "\n",
-    "We recommend using `ChatBedrockConverse` for users who do not need to use custom models.\n",
-    "\n",
-    "You can use it like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ae728e59-94d4-40cf-9d24-25ad8723fc59",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", response_metadata={'ResponseMetadata': {'RequestId': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 21 Aug 2024 17:23:49 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 672}}, id='run-77ee9810-e32b-45dc-9ccb-6692253b1f45-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_aws import ChatBedrockConverse\n",
-    "\n",
-    "llm = ChatBedrockConverse(\n",
-    "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "llm.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
-   "metadata": {},
-   "source": [
-    "### Streaming\n",
-    "\n",
-    "Note that `ChatBedrockConverse` emits content blocks while streaming:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7794b32e-d8de-4973-bf0f-39807dc745f0",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "content=[] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': 'Vo', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': 'ici', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' tra', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': 'duction', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' en', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' français', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' :', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': '\\n\\nJ', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': \"'\", 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': 'a', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': 'ime', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': ' programm', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': 'ation', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'type': 'text', 'text': '.', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[{'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[] response_metadata={'stopReason': 'end_turn'} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-      "content=[] response_metadata={'metrics': {'latencyMs': 713}} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8' usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in llm.stream(messages):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
-   "metadata": {},
-   "source": [
-    "An output parser can be used to filter to text, if desired:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Bedrock integration lives in the `langchain-aws` package:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|Vo|ici| la| tra|duction| en| français| :|\n",
-      "\n",
-      "J|'|a|ime| la| programm|ation|.||||"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-aws"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_aws import ChatBedrock\n",
+        "\n",
+        "llm = ChatBedrock(\n",
+        "    model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
+        "    model_kwargs=dict(temperature=0),\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-fdb07dc3-ff72-430d-b22b-e7824b15c766-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Voici la traduction en français :\n",
+            "\n",
+            "J'aime la programmation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-5ad005ce-9f31-4670-baa0-9373d418698a-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## Bedrock Converse API\n",
+        "\n",
+        "AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
+        "\n",
+        "We recommend using `ChatBedrockConverse` for users who do not need to use custom models.\n",
+        "\n",
+        "You can use it like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "ae728e59-94d4-40cf-9d24-25ad8723fc59",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", response_metadata={'ResponseMetadata': {'RequestId': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 21 Aug 2024 17:23:49 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 672}}, id='run-77ee9810-e32b-45dc-9ccb-6692253b1f45-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_aws import ChatBedrockConverse\n",
+        "\n",
+        "llm = ChatBedrockConverse(\n",
+        "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "llm.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
+      "metadata": {},
+      "source": [
+        "### Streaming\n",
+        "\n",
+        "Note that `ChatBedrockConverse` emits content blocks while streaming:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "7794b32e-d8de-4973-bf0f-39807dc745f0",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "content=[] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': 'Vo', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': 'ici', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' tra', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': 'duction', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' en', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' français', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' :', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': '\\n\\nJ', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': \"'\", 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': 'a', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': 'ime', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': ' programm', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': 'ation', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'type': 'text', 'text': '.', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[{'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[] response_metadata={'stopReason': 'end_turn'} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
+            "content=[] response_metadata={'metrics': {'latencyMs': 713}} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8' usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50}\n"
+          ]
+        }
+      ],
+      "source": [
+        "for chunk in llm.stream(messages):\n",
+        "    print(chunk)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
+      "metadata": {},
+      "source": [
+        "An output parser can be used to filter to text, if desired:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "|Vo|ici| la| tra|duction| en| français| :|\n",
+            "\n",
+            "J|'|a|ime| la| programm|ation|.||||"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_core.output_parsers import StrOutputParser\n",
+        "\n",
+        "chain = llm | StrOutputParser()\n",
+        "\n",
+        "for chunk in chain.stream(messages):\n",
+        "    print(chunk, end=\"|\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
+        "\n",
+        "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
    }
-   ],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "\n",
-    "chain = llm | StrOutputParser()\n",
-    "\n",
-    "for chunk in chain.stream(messages):\n",
-    "    print(chunk, end=\"|\")"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
-    "\n",
-    "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/cerebras.ipynb
+++ b/docs/docs/integrations/chat/cerebras.ipynb
@@ -1,424 +1,422 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Cerebras\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatCerebras\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#).\n",
-    "\n",
-    "At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n",
-    "\n",
-    "With Cerebras as your inference provider, you can:\n",
-    "- Achieve unprecedented speed for AI inference workloads\n",
-    "- Build commercially with high throughput\n",
-    "- Effortlessly scale your AI workloads with our seamless clustering technology\n",
-    "\n",
-    "Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models.\n",
-    "\n",
-    "Want to experience the power of Cerebras? Check out our [website](https://cerebras.ai) for more resources and explore options for accessing our technology through the Cerebras Cloud or on-premise deployments!\n",
-    "\n",
-    "For more information about Cerebras Cloud, visit [cloud.cerebras.ai](https://cloud.cerebras.ai/). Our API reference is available at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cerebras) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatCerebras](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#) | [langchain-cerebras](https://python.langchain.com/api_reference/cerebras/index.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cerebras?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cerebras?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅  | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "```bash\n",
-    "pip install langchain-cerebras\n",
-    "```\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai/) and add it to your environment variables:\n",
-    "```\n",
-    "export CEREBRAS_API_KEY=\"your-api-key-here\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ce19c2d6",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter your Cerebras API key:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"CEREBRAS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Cerebras integration lives in the `langchain-cerebras` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-cerebras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea69675d",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21155898",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Je adore le programmation.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 35, 'total_tokens': 42}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e5d66faf-019c-4ac6-9265-71093b13202d-0', usage_metadata={'input_tokens': 35, 'output_tokens': 7, 'total_tokens': 42})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Cerebras\n",
+        "---"
      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren!\\n\\n(Literally: I love programming!)', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 30, 'total_tokens': 44}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e1d2ebb8-76d1-471b-9368-3b68d431f16a-0', usage_metadata={'input_tokens': 30, 'output_tokens': 14, 'total_tokens': 44})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatCerebras\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#).\n",
+        "\n",
+        "At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n",
+        "\n",
+        "With Cerebras as your inference provider, you can:\n",
+        "- Achieve unprecedented speed for AI inference workloads\n",
+        "- Build commercially with high throughput\n",
+        "- Effortlessly scale your AI workloads with our seamless clustering technology\n",
+        "\n",
+        "Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models.\n",
+        "\n",
+        "Want to experience the power of Cerebras? Check out our [website](https://cerebras.ai) for more resources and explore options for accessing our technology through the Cerebras Cloud or on-premise deployments!\n",
+        "\n",
+        "For more information about Cerebras Cloud, visit [cloud.cerebras.ai](https://cloud.cerebras.ai/). Our API reference is available at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cerebras) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatCerebras](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#) | [langchain-cerebras](https://python.langchain.com/api_reference/cerebras/index.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cerebras?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cerebras?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅  | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "```bash\n",
+        "pip install langchain-cerebras\n",
+        "```\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai/) and add it to your environment variables:\n",
+        "```\n",
+        "export CEREBRAS_API_KEY=\"your-api-key-here\"\n",
+        "```"
      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ec73a0e",
-   "metadata": {},
-   "source": [
-    "## Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "46fd21a7",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OH BOY! Let me tell you all about LIONS!\n",
-      "\n",
-      "Lions are the kings of the jungle! They're really big and have beautiful, fluffy manes around their necks. The mane is like a big, golden crown!\n",
-      "\n",
-      "Lions live in groups called prides. A pride is like a big family, and the lionesses (that's what we call the female lions) take care of the babies. The lionesses are like the mommies, and they teach the babies how to hunt and play.\n",
-      "\n",
-      "Lions are very good at hunting. They work together to catch their food, like zebras and antelopes. They're super fast and can run really, really fast!\n",
-      "\n",
-      "But lions are also very sleepy. They like to take long naps in the sun, and they can sleep for up to 20 hours a day! Can you imagine sleeping that much?\n",
-      "\n",
-      "Lions are also very loud. They roar really loudly to talk to each other. It's like they're saying, \"ROAR! I'm the king of the jungle!\"\n",
-      "\n",
-      "And guess what? Lions are very social. They like to play and cuddle with each other. They're like big, furry teddy bears!\n",
-      "\n",
-      "So, that's lions! Aren't they just the coolest?"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "system = \"You are an expert on animals who must answer questions in a manner that a 5 year old can understand.\"\n",
-    "human = \"I want to learn more about this animal: {animal}\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "for chunk in chain.stream({\"animal\": \"Lion\"}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f67b6132",
-   "metadata": {},
-   "source": [
-    "## Async"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a3a45baf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ice', response_metadata={'token_usage': {'completion_tokens': 2, 'prompt_tokens': 36, 'total_tokens': 38}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-7434bdde-1bec-44cf-827b-8d978071dfe8-0', usage_metadata={'input_tokens': 36, 'output_tokens': 2, 'total_tokens': 38})"
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "ce19c2d6",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter your Cerebras API key:  ········\n"
+          ]
+        }
+      ],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"CEREBRAS_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"human\",\n",
-    "            \"Let's play a game of opposites. What's the opposite of {topic}? Just give me the answer with no extra input.\",\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "chain = prompt | llm\n",
-    "await chain.ainvoke({\"topic\": \"fire\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f9d9945",
-   "metadata": {},
-   "source": [
-    "## Async Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c7448e0f",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In the distant reaches of the cosmos, there existed a peculiar phenomenon known as the \"Eclipse of Eternity,\" a swirling vortex of darkness that had been shrouded in mystery for eons. It was said that this blackhole, born from the cataclysmic collision of two ancient stars, had been slowly devouring the fabric of space-time itself, warping the very essence of reality. As the celestial bodies of the galaxy danced around it, they began to notice a strange, almost imperceptible distortion in the fabric of space, as if the blackhole's gravitational pull was exerting an influence on the very course of events itself.\n",
-      "\n",
-      "As the centuries passed, astronomers from across the galaxy became increasingly fascinated by the Eclipse of Eternity, pouring over ancient texts and scouring the cosmos for any hint of its secrets. One such scholar, a brilliant and reclusive astrophysicist named Dr. Elara Vex, became obsessed with unraveling the mysteries of the blackhole. She spent years pouring over ancient texts, deciphering cryptic messages and hidden codes that hinted at the existence of a long-lost civilization that had once thrived in the heart of the blackhole itself. According to legend, this ancient civilization had possessed knowledge of the cosmos that was beyond human comprehension, and had used their mastery of the universe to create the Eclipse of Eternity as a gateway to other dimensions.\n",
-      "\n",
-      "As Dr. Vex delved deeper into her research, she began to experience strange and vivid dreams, visions that seemed to transport her to the very heart of the blackhole itself. In these dreams, she saw ancient beings, their faces twisted in agony as they were consumed by the void. She saw stars and galaxies, their light warped and distorted by the blackhole's gravitational pull. And she saw the Eclipse of Eternity itself, its swirling vortex of darkness pulsing with an otherworldly energy that seemed to be calling to her. As the dreams grew more vivid and more frequent, Dr. Vex became convinced that she was being drawn into the heart of the blackhole, and that the secrets of the universe lay waiting for her on the other side."
-     ]
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Cerebras integration lives in the `langchain-cerebras` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-cerebras"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ea69675d",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "21155898",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Je adore le programmation.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 35, 'total_tokens': 42}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e5d66faf-019c-4ac6-9265-71093b13202d-0', usage_metadata={'input_tokens': 35, 'output_tokens': 7, 'total_tokens': 42})"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren!\\n\\n(Literally: I love programming!)', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 30, 'total_tokens': 44}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e1d2ebb8-76d1-471b-9368-3b68d431f16a-0', usage_metadata={'input_tokens': 30, 'output_tokens': 14, 'total_tokens': 44})"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0ec73a0e",
+      "metadata": {},
+      "source": [
+        "## Streaming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "46fd21a7",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "OH BOY! Let me tell you all about LIONS!\n",
+            "\n",
+            "Lions are the kings of the jungle! They're really big and have beautiful, fluffy manes around their necks. The mane is like a big, golden crown!\n",
+            "\n",
+            "Lions live in groups called prides. A pride is like a big family, and the lionesses (that's what we call the female lions) take care of the babies. The lionesses are like the mommies, and they teach the babies how to hunt and play.\n",
+            "\n",
+            "Lions are very good at hunting. They work together to catch their food, like zebras and antelopes. They're super fast and can run really, really fast!\n",
+            "\n",
+            "But lions are also very sleepy. They like to take long naps in the sun, and they can sleep for up to 20 hours a day! Can you imagine sleeping that much?\n",
+            "\n",
+            "Lions are also very loud. They roar really loudly to talk to each other. It's like they're saying, \"ROAR! I'm the king of the jungle!\"\n",
+            "\n",
+            "And guess what? Lions are very social. They like to play and cuddle with each other. They're like big, furry teddy bears!\n",
+            "\n",
+            "So, that's lions! Aren't they just the coolest?"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "system = \"You are an expert on animals who must answer questions in a manner that a 5 year old can understand.\"\n",
+        "human = \"I want to learn more about this animal: {animal}\"\n",
+        "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "\n",
+        "for chunk in chain.stream({\"animal\": \"Lion\"}):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f67b6132",
+      "metadata": {},
+      "source": [
+        "## Async"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "a3a45baf",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ice', response_metadata={'token_usage': {'completion_tokens': 2, 'prompt_tokens': 36, 'total_tokens': 38}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-7434bdde-1bec-44cf-827b-8d978071dfe8-0', usage_metadata={'input_tokens': 36, 'output_tokens': 2, 'total_tokens': 38})"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"human\",\n",
+        "            \"Let's play a game of opposites. What's the opposite of {topic}? Just give me the answer with no extra input.\",\n",
+        "        )\n",
+        "    ]\n",
+        ")\n",
+        "chain = prompt | llm\n",
+        "await chain.ainvoke({\"topic\": \"fire\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f9d9945",
+      "metadata": {},
+      "source": [
+        "## Async Streaming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "c7448e0f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "In the distant reaches of the cosmos, there existed a peculiar phenomenon known as the \"Eclipse of Eternity,\" a swirling vortex of darkness that had been shrouded in mystery for eons. It was said that this blackhole, born from the cataclysmic collision of two ancient stars, had been slowly devouring the fabric of space-time itself, warping the very essence of reality. As the celestial bodies of the galaxy danced around it, they began to notice a strange, almost imperceptible distortion in the fabric of space, as if the blackhole's gravitational pull was exerting an influence on the very course of events itself.\n",
+            "\n",
+            "As the centuries passed, astronomers from across the galaxy became increasingly fascinated by the Eclipse of Eternity, pouring over ancient texts and scouring the cosmos for any hint of its secrets. One such scholar, a brilliant and reclusive astrophysicist named Dr. Elara Vex, became obsessed with unraveling the mysteries of the blackhole. She spent years pouring over ancient texts, deciphering cryptic messages and hidden codes that hinted at the existence of a long-lost civilization that had once thrived in the heart of the blackhole itself. According to legend, this ancient civilization had possessed knowledge of the cosmos that was beyond human comprehension, and had used their mastery of the universe to create the Eclipse of Eternity as a gateway to other dimensions.\n",
+            "\n",
+            "As Dr. Vex delved deeper into her research, she began to experience strange and vivid dreams, visions that seemed to transport her to the very heart of the blackhole itself. In these dreams, she saw ancient beings, their faces twisted in agony as they were consumed by the void. She saw stars and galaxies, their light warped and distorted by the blackhole's gravitational pull. And she saw the Eclipse of Eternity itself, its swirling vortex of darkness pulsing with an otherworldly energy that seemed to be calling to her. As the dreams grew more vivid and more frequent, Dr. Vex became convinced that she was being drawn into the heart of the blackhole, and that the secrets of the universe lay waiting for her on the other side."
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"human\",\n",
+        "            \"Write a long convoluted story about {subject}. I want {num_paragraphs} paragraphs.\",\n",
+        "        )\n",
+        "    ]\n",
+        ")\n",
+        "chain = prompt | llm\n",
+        "\n",
+        "async for chunk in chain.astream({\"num_paragraphs\": 3, \"subject\": \"blackholes\"}):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatCerebras features and configurations head to the API reference: https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.13"
    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"human\",\n",
-    "            \"Write a long convoluted story about {subject}. I want {num_paragraphs} paragraphs.\",\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "async for chunk in chain.astream({\"num_paragraphs\": 3, \"subject\": \"blackholes\"}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatCerebras features and configurations head to the API reference: https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
@@ -1,264 +1,262 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "30373ae2-f326-4e96-a1f7-062f57396886",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Cloudflare Workers AI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f679592d",
-   "metadata": {},
-   "source": [
-    "# ChatCloudflareWorkersAI\n",
-    "\n",
-    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare_workersai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| ChatCloudflareWorkersAI | langchain-community| ❌ | ❌ | ✅ | ❌  | ❌ |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- To access Cloudflare Workers AI models you'll need to create a Cloudflare account, get an account number and API key, and install the `langchain-community` package.\n",
-    "\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "\n",
-    "Head to [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/) to sign up to Cloudflare Workers AI and generate an API key."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a524cff",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "71b53c25",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "777a8526",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain ChatCloudflareWorkersAI integration lives in the `langchain-community` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54990998",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "629ba46f",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec13c2d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI\n",
-    "\n",
-    "llm = ChatCloudflareWorkersAI(\n",
-    "    account_id=\"my_account_id\",\n",
-    "    api_token=\"my_api_token\",\n",
-    "    model=\"@hf/nousresearch/hermes-2-pro-mistral-7b\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "119b6732",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2438a906",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-11-07 15:55:14 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to French. Translate the user sentence.\\nrole: user, content: I love programming.', 'tools': None}\n"
-     ]
+      "cell_type": "raw",
+      "id": "30373ae2-f326-4e96-a1f7-062f57396886",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Cloudflare Workers AI\n",
+        "---"
+      ]
    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='{\\'result\\': {\\'response\\': \\'Je suis un assistant virtuel qui peut traduire l\\\\\\'anglais vers le français. La phrase que vous avez dite est : \"J\\\\\\'aime programmer.\" En français, cela se traduit par : \"J\\\\\\'adore programmer.\"\\'}, \\'success\\': True, \\'errors\\': [], \\'messages\\': []}', additional_kwargs={}, response_metadata={}, id='run-838fd398-8594-4ca5-9055-03c72993caf6-0')"
+      "cell_type": "markdown",
+      "id": "f679592d",
+      "metadata": {},
+      "source": [
+        "# ChatCloudflareWorkersAI\n",
+        "\n",
+        "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
+        "\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare_workersai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| ChatCloudflareWorkersAI | langchain-community| ❌ | ❌ | ✅ | ❌  | ❌ |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "- To access Cloudflare Workers AI models you'll need to create a Cloudflare account, get an account number and API key, and install the `langchain-community` package.\n",
+        "\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "\n",
+        "Head to [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/) to sign up to Cloudflare Workers AI and generate an API key."
      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1b4911bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'result': {'response': 'Je suis un assistant virtuel qui peut traduire l\\'anglais vers le français. La phrase que vous avez dite est : \"J\\'aime programmer.\" En français, cela se traduit par : \"J\\'adore programmer.\"'}, 'success': True, 'errors': [], 'messages': []}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "111aa5d4",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b2a14282",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-11-07 15:55:24 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to German.\\nrole: user, content: I love programming.', 'tools': None}\n"
-     ]
    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"{'result': {'response': 'role: system, content: Das ist sehr nett zu hören! Programmieren lieben, ist eine interessante und anspruchsvolle Hobby- oder Berufsausrichtung. Wenn Sie englische Texte ins Deutsche übersetzen möchten, kann ich Ihnen helfen. Geben Sie bitte den englischen Satz oder die Übersetzung an, die Sie benötigen.'}, 'success': True, 'errors': [], 'messages': []}\", additional_kwargs={}, response_metadata={}, id='run-0d3be9a6-3d74-4dde-b49a-4479d6af00ef-0')"
+      "cell_type": "markdown",
+      "id": "4a524cff",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "71b53c25",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "777a8526",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain ChatCloudflareWorkersAI integration lives in the `langchain-community` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "54990998",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "629ba46f",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ec13c2d9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI\n",
+        "\n",
+        "llm = ChatCloudflareWorkersAI(\n",
+        "    account_id=\"my_account_id\",\n",
+        "    api_token=\"my_api_token\",\n",
+        "    model=\"@hf/nousresearch/hermes-2-pro-mistral-7b\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "119b6732",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "2438a906",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2024-11-07 15:55:14 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to French. Translate the user sentence.\\nrole: user, content: I love programming.', 'tools': None}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='{\\'result\\': {\\'response\\': \\'Je suis un assistant virtuel qui peut traduire l\\\\\\'anglais vers le français. La phrase que vous avez dite est : \"J\\\\\\'aime programmer.\" En français, cela se traduit par : \"J\\\\\\'adore programmer.\"\\'}, \\'success\\': True, \\'errors\\': [], \\'messages\\': []}', additional_kwargs={}, response_metadata={}, id='run-838fd398-8594-4ca5-9055-03c72993caf6-0')"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "1b4911bd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'result': {'response': 'Je suis un assistant virtuel qui peut traduire l\\'anglais vers le français. La phrase que vous avez dite est : \"J\\'aime programmer.\" En français, cela se traduit par : \"J\\'adore programmer.\"'}, 'success': True, 'errors': [], 'messages': []}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "111aa5d4",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "b2a14282",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2024-11-07 15:55:24 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to German.\\nrole: user, content: I love programming.', 'tools': None}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"{'result': {'response': 'role: system, content: Das ist sehr nett zu hören! Programmieren lieben, ist eine interessante und anspruchsvolle Hobby- oder Berufsausrichtung. Wenn Sie englische Texte ins Deutsche übersetzen möchten, kann ich Ihnen helfen. Geben Sie bitte den englischen Satz oder die Übersetzung an, die Sie benötigen.'}, 'success': True, 'errors': [], 'messages': []}\", additional_kwargs={}, response_metadata={}, id='run-0d3be9a6-3d74-4dde-b49a-4479d6af00ef-0')"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e1f311bd",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation on `ChatCloudflareWorkersAI` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cloudflare_workersai.html)."
      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "e1f311bd",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation on `ChatCloudflareWorkersAI` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cloudflare_workersai.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/cohere.ipynb
+++ b/docs/docs/integrations/chat/cohere.ipynb
@@ -1,352 +1,350 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "53fbf15f",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Cohere\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# Cohere\n",
-    "\n",
-    "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
-    "\n",
-    "Head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "The integration lives in the `langchain-cohere` package. We can install these with:\n",
-    "\n",
-    "```bash\n",
-    "pip install -U langchain-cohere\n",
-    "```\n",
-    "\n",
-    "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf690fbb",
-   "metadata": {},
-   "source": [
-    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7f11de02",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "ChatCohere supports all [ChatModel](/docs/how_to#chat-models) functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_cohere import ChatCohere\n",
-    "from langchain_core.messages import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatCohere()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
+      "cell_type": "raw",
+      "id": "53fbf15f",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Cohere\n",
+        "---"
      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
-    "chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
+      "cell_type": "markdown",
+      "id": "bf733a38-db84-4363-89e2-de6735c37230",
+      "metadata": {},
+      "source": [
+        "# Cohere\n",
+        "\n",
+        "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
+        "\n",
+        "Head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.ainvoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 && 5"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in chat.stream(messages):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "064288e4-f184-4496-9427-bcf148fa055e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
+      "cell_type": "markdown",
+      "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "The integration lives in the `langchain-cohere` package. We can install these with:\n",
+        "\n",
+        "```bash\n",
+        "pip install -U langchain-cohere\n",
+        "```\n",
+        "\n",
+        "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat.batch([messages])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1c56460",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts/lcel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "0851b103",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "ae950c0f-1691-47f1-b609-273033cae707",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12db8d69",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "Cohere supports tool calling functionalities!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "337e24af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import (\n",
-    "    HumanMessage,\n",
-    "    ToolMessage,\n",
-    ")\n",
-    "from langchain_core.tools import tool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "74d292e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@tool\n",
-    "def magic_function(number: int) -> int:\n",
-    "    \"\"\"Applies a magic operation to an integer\n",
-    "    Args:\n",
-    "        number: Number to have magic operation performed on\n",
-    "    \"\"\"\n",
-    "    return number + 10\n",
-    "\n",
-    "\n",
-    "def invoke_tools(tool_calls, messages):\n",
-    "    for tool_call in tool_calls:\n",
-    "        selected_tool = {\"magic_function\": magic_function}[tool_call[\"name\"].lower()]\n",
-    "        tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
-    "        messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
-    "    return messages\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ecafcbc6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_with_tools = chat.bind_tools(tools=tools)\n",
-    "messages = [HumanMessage(content=\"What is the value of magic_function(2)?\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "aa34fc39",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='The value of magic_function(2) is 12.', additional_kwargs={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, response_metadata={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, id='run-f318a9cf-55c8-44f4-91d1-27cf46c6a465-0')"
+      "cell_type": "markdown",
+      "id": "cf690fbb",
+      "metadata": {},
+      "source": [
+        "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3c2fc2201dc80557",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "31f2af10e04dec59",
+      "metadata": {},
+      "source": [
+        "## Usage\n",
+        "\n",
+        "ChatCohere supports all [ChatModel](/docs/how_to#chat-models) functionality:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fa83b00a929614ad",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_cohere import ChatCohere\n",
+        "from langchain_core.messages import HumanMessage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "chat = ChatCohere()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
+        "chat.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "await chat.ainvoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "4 && 5"
+          ]
+        }
+      ],
+      "source": [
+        "for chunk in chat.stream(messages):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "064288e4-f184-4496-9427-bcf148fa055e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chat.batch([messages])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f1c56460",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts/lcel)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "0851b103",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+        "chain = prompt | chat"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "ae950c0f-1691-47f1-b609-273033cae707",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chain.invoke({\"topic\": \"bears\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12db8d69",
+      "metadata": {},
+      "source": [
+        "## Tool calling\n",
+        "\n",
+        "Cohere supports tool calling functionalities!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "337e24af",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.messages import (\n",
+        "    HumanMessage,\n",
+        "    ToolMessage,\n",
+        ")\n",
+        "from langchain_core.tools import tool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "74d292e7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "@tool\n",
+        "def magic_function(number: int) -> int:\n",
+        "    \"\"\"Applies a magic operation to an integer\n",
+        "    Args:\n",
+        "        number: Number to have magic operation performed on\n",
+        "    \"\"\"\n",
+        "    return number + 10\n",
+        "\n",
+        "\n",
+        "def invoke_tools(tool_calls, messages):\n",
+        "    for tool_call in tool_calls:\n",
+        "        selected_tool = {\"magic_function\": magic_function}[tool_call[\"name\"].lower()]\n",
+        "        tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
+        "        messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
+        "    return messages\n",
+        "\n",
+        "\n",
+        "tools = [magic_function]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "ecafcbc6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "llm_with_tools = chat.bind_tools(tools=tools)\n",
+        "messages = [HumanMessage(content=\"What is the value of magic_function(2)?\")]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "aa34fc39",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='The value of magic_function(2) is 12.', additional_kwargs={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, response_metadata={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, id='run-f318a9cf-55c8-44f4-91d1-27cf46c6a465-0')"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "res = llm_with_tools.invoke(messages)\n",
+        "while res.tool_calls:\n",
+        "    messages.append(res)\n",
+        "    messages = invoke_tools(res.tool_calls, messages)\n",
+        "    res = llm_with_tools.invoke(messages)\n",
+        "\n",
+        "res"
      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "res = llm_with_tools.invoke(messages)\n",
-    "while res.tool_calls:\n",
-    "    messages.append(res)\n",
-    "    messages = invoke_tools(res.tool_calls, messages)\n",
-    "    res = llm_with_tools.invoke(messages)\n",
-    "\n",
-    "res"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
+    }
  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/deepseek.ipynb
+++ b/docs/docs/integrations/chat/deepseek.ipynb
@@ -1,237 +1,235 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: DeepSeek\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: DeepSeek\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatDeepSeek\n",
+        "\n",
+        "\n",
+        "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
+        "\n",
+        ":::tip\n",
+        "\n",
+        "DeepSeek's models are open source and can be run locally (e.g. in [Ollama](./ollama.ipynb)) or on other inference providers (e.g. [Fireworks](./fireworks.ipynb), [Together](./together.ipynb)) as well.\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatDeepSeek](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html) | [langchain-deepseek](https://python.langchain.com/api_reference/deepseek/) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
+        "\n",
+        ":::note\n",
+        "\n",
+        "DeepSeek-R1, specified via `model=\"deepseek-reasoner\"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model=\"deepseek-chat\"`).\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [DeepSeek's API Key page](https://platform.deepseek.com/api_keys) to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"DEEPSEEK_API_KEY\"):\n",
+        "    os.environ[\"DEEPSEEK_API_KEY\"] = getpass.getpass(\"Enter your DeepSeek API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain DeepSeek integration lives in the `langchain-deepseek` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-deepseek"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_deepseek import ChatDeepSeek\n",
+        "\n",
+        "llm = ChatDeepSeek(\n",
+        "    model=\"deepseek-chat\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg.content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatDeepSeek features and configurations head to the [API Reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatDeepSeek\n",
-    "\n",
-    "\n",
-    "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
-    "\n",
-    ":::tip\n",
-    "\n",
-    "DeepSeek's models are open source and can be run locally (e.g. in [Ollama](./ollama.ipynb)) or on other inference providers (e.g. [Fireworks](./fireworks.ipynb), [Together](./together.ipynb)) as well.\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatDeepSeek](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html) | [langchain-deepseek](https://python.langchain.com/api_reference/deepseek/) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    ":::note\n",
-    "\n",
-    "DeepSeek-R1, specified via `model=\"deepseek-reasoner\"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model=\"deepseek-chat\"`).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [DeepSeek's API Key page](https://platform.deepseek.com/api_keys) to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"DEEPSEEK_API_KEY\"):\n",
-    "    os.environ[\"DEEPSEEK_API_KEY\"] = getpass.getpass(\"Enter your DeepSeek API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain DeepSeek integration lives in the `langchain-deepseek` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-deepseek"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_deepseek import ChatDeepSeek\n",
-    "\n",
-    "llm = ChatDeepSeek(\n",
-    "    model=\"deepseek-chat\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg.content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatDeepSeek features and configurations head to the [API Reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/fireworks.ipynb
+++ b/docs/docs/integrations/chat/fireworks.ipynb
@@ -1,268 +1,266 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Fireworks\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatFireworks\n",
-    "\n",
-    "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
-    "\n",
-    "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/fireworks) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatFireworks](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html) | [langchain-fireworks](https://python.langchain.com/api_reference/fireworks/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Fireworks models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Fireworks integration lives in the `langchain-fireworks` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-fireworks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_fireworks import ChatFireworks\n",
-    "\n",
-    "llm = ChatFireworks(\n",
-    "    model=\"accounts/fireworks/models/llama-v3-70b-instruct\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'prompt_tokens': 35, 'total_tokens': 44, 'completion_tokens': 9}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-df28e69a-ff30-457e-a743-06eb14d01cb0-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Fireworks\n",
+        "---"
      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'prompt_tokens': 30, 'total_tokens': 37, 'completion_tokens': 7}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-ff3f91ad-ed81-4acf-9f59-7490dc8d8f48-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatFireworks\n",
+        "\n",
+        "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
+        "\n",
+        "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/fireworks) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatFireworks](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html) | [langchain-fireworks](https://python.langchain.com/api_reference/fireworks/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Fireworks models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Fireworks integration lives in the `langchain-fireworks` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-fireworks"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_fireworks import ChatFireworks\n",
+        "\n",
+        "llm = ChatFireworks(\n",
+        "    model=\"accounts/fireworks/models/llama-v3-70b-instruct\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'prompt_tokens': 35, 'total_tokens': 44, 'completion_tokens': 9}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-df28e69a-ff30-457e-a743-06eb14d01cb0-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore la programmation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'prompt_tokens': 30, 'total_tokens': 37, 'completion_tokens': 7}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-ff3f91ad-ed81-4acf-9f59-7490dc8d8f48-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/goodfire.ipynb
+++ b/docs/docs/integrations/chat/goodfire.ipynb
@@ -1,354 +1,352 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Goodfire\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatGoodfire\n",
-    "\n",
-    "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGoodfire](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html) | [langchain-goodfire](https://python.langchain.com/api_reference/goodfire/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-goodfire?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-goodfire?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Goodfire models you'll need to create a/an Goodfire account, get an API key, and install the `langchain-goodfire` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [Goodfire Settings](https://platform.goodfire.ai/organization/settings/api-keys) to sign up to Goodfire and generate an API key. Once you've done this set the GOODFIRE_API_KEY environment variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"GOODFIRE_API_KEY\"):\n",
-    "    os.environ[\"GOODFIRE_API_KEY\"] = getpass.getpass(\"Enter your Goodfire API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Goodfire integration lives in the `langchain-goodfire` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-goodfire"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import goodfire\n",
-    "from langchain_goodfire import ChatGoodfire\n",
-    "\n",
-    "base_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
-    "\n",
-    "llm = ChatGoodfire(\n",
-    "    model=base_variant,\n",
-    "    temperature=0,\n",
-    "    max_completion_tokens=1000,\n",
-    "    seed=42,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={}, id='run-8d43cf35-bce8-4827-8935-c64f8fb78cd0-0', usage_metadata={'input_tokens': 51, 'output_tokens': 39, 'total_tokens': 90})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Goodfire\n",
+        "---"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = await llm.ainvoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren. How can I help you with programming today?', additional_kwargs={}, response_metadata={}, id='run-03d1a585-8234-46f1-a8df-bf9143fe3309-0', usage_metadata={'input_tokens': 46, 'output_tokens': 46, 'total_tokens': 92})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatGoodfire\n",
+        "\n",
+        "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatGoodfire](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html) | [langchain-goodfire](https://python.langchain.com/api_reference/goodfire/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-goodfire?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-goodfire?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Goodfire models you'll need to create a/an Goodfire account, get an API key, and install the `langchain-goodfire` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [Goodfire Settings](https://platform.goodfire.ai/organization/settings/api-keys) to sign up to Goodfire and generate an API key. Once you've done this set the GOODFIRE_API_KEY environment variable."
      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "await chain.ainvoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## Goodfire-specific functionality\n",
-    "\n",
-    "To use Goodfire-specific functionality such as SAE features and variants, you can use the `goodfire` package directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3aef9e0a",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "FeatureGroup([\n",
-       "   0: \"The assistant should adopt the persona of a pirate\",\n",
-       "   1: \"The assistant should roleplay as a pirate\",\n",
-       "   2: \"The assistant should engage with pirate-themed content or roleplay as a pirate\",\n",
-       "   3: \"The assistant should roleplay as a character\",\n",
-       "   4: \"The assistant should roleplay as a specific character\",\n",
-       "   5: \"The assistant should roleplay as a game character or NPC\",\n",
-       "   6: \"The assistant should roleplay as a human character\",\n",
-       "   7: \"Requests for the assistant to roleplay or pretend to be something else\",\n",
-       "   8: \"Requests for the assistant to roleplay or pretend to be something\",\n",
-       "   9: \"The assistant is being assigned a role or persona to roleplay\"\n",
-       "])"
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"GOODFIRE_API_KEY\"):\n",
+        "    os.environ[\"GOODFIRE_API_KEY\"] = getpass.getpass(\"Enter your Goodfire API key: \")"
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "client = goodfire.Client(api_key=os.environ[\"GOODFIRE_API_KEY\"])\n",
-    "\n",
-    "pirate_features = client.features.search(\n",
-    "    \"assistant should roleplay as a pirate\", base_variant\n",
-    ")\n",
-    "pirate_features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "52f03a00",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field! Arrr! Hope that made ye laugh, matey!', additional_kwargs={}, response_metadata={}, id='run-7d8bd30f-7f80-41cb-bdb6-25c29c22a7ce-0', usage_metadata={'input_tokens': 35, 'output_tokens': 60, 'total_tokens': 95})"
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Goodfire integration lives in the `langchain-goodfire` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-goodfire"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import goodfire\n",
+        "from langchain_goodfire import ChatGoodfire\n",
+        "\n",
+        "base_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
+        "\n",
+        "llm = ChatGoodfire(\n",
+        "    model=base_variant,\n",
+        "    temperature=0,\n",
+        "    max_completion_tokens=1000,\n",
+        "    seed=42,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={}, id='run-8d43cf35-bce8-4827-8935-c64f8fb78cd0-0', usage_metadata={'input_tokens': 51, 'output_tokens': 39, 'total_tokens': 90})"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = await llm.ainvoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore la programmation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren. How can I help you with programming today?', additional_kwargs={}, response_metadata={}, id='run-03d1a585-8234-46f1-a8df-bf9143fe3309-0', usage_metadata={'input_tokens': 46, 'output_tokens': 46, 'total_tokens': 92})"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "await chain.ainvoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## Goodfire-specific functionality\n",
+        "\n",
+        "To use Goodfire-specific functionality such as SAE features and variants, you can use the `goodfire` package directly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "3aef9e0a",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "FeatureGroup([\n",
+              "   0: \"The assistant should adopt the persona of a pirate\",\n",
+              "   1: \"The assistant should roleplay as a pirate\",\n",
+              "   2: \"The assistant should engage with pirate-themed content or roleplay as a pirate\",\n",
+              "   3: \"The assistant should roleplay as a character\",\n",
+              "   4: \"The assistant should roleplay as a specific character\",\n",
+              "   5: \"The assistant should roleplay as a game character or NPC\",\n",
+              "   6: \"The assistant should roleplay as a human character\",\n",
+              "   7: \"Requests for the assistant to roleplay or pretend to be something else\",\n",
+              "   8: \"Requests for the assistant to roleplay or pretend to be something\",\n",
+              "   9: \"The assistant is being assigned a role or persona to roleplay\"\n",
+              "])"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "client = goodfire.Client(api_key=os.environ[\"GOODFIRE_API_KEY\"])\n",
+        "\n",
+        "pirate_features = client.features.search(\n",
+        "    \"assistant should roleplay as a pirate\", base_variant\n",
+        ")\n",
+        "pirate_features"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "52f03a00",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field! Arrr! Hope that made ye laugh, matey!', additional_kwargs={}, response_metadata={}, id='run-7d8bd30f-7f80-41cb-bdb6-25c29c22a7ce-0', usage_metadata={'input_tokens': 35, 'output_tokens': 60, 'total_tokens': 95})"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pirate_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
+        "\n",
+        "pirate_variant.set(pirate_features[0], 0.4)\n",
+        "pirate_variant.set(pirate_features[1], 0.3)\n",
+        "\n",
+        "await llm.ainvoke(\"Tell me a joke\", model=pirate_variant)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatGoodfire features and configurations head to the [API reference](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html)"
      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "pirate_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
-    "\n",
-    "pirate_variant.set(pirate_features[0], 0.4)\n",
-    "pirate_variant.set(pirate_features[1], 0.3)\n",
-    "\n",
-    "await llm.ainvoke(\"Tell me a joke\", model=pirate_variant)"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.8"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatGoodfire features and configurations head to the [API reference](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/google_generative_ai.ipynb
+++ b/docs/docs/integrations/chat/google_generative_ai.ipynb
@@ -1,314 +1,312 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Google AI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatGoogleGenerativeAI\n",
-    "\n",
-    "This docs will help you get started with Google AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
-    "\n",
-    "Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
-    "\n",
-    ":::info Google AI vs Google Cloud Vertex AI\n",
-    "\n",
-    "Google's Gemini models are accessible through Google AI and through Google Cloud Vertex AI. Using Google AI just requires a Google account and an API key. Using Google Cloud Vertex AI requires a Google Cloud account (with term agreements and billing) but offers enterprise features like customer encription key, virtual private cloud, and more.\n",
-    "\n",
-    "To learn more about the key features of the two APIs see the [Google docs](https://cloud.google.com/vertex-ai/generative-ai/docs/migrate/migrate-google-ai#google-ai).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_generativeai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGoogleGenerativeAI](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) | [langchain-google-genai](https://python.langchain.com/api_reference/google_genai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-genai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-genai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Google AI models you'll need to create a Google Acount account, get a Google AI API key, and install the `langchain-google-genai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to https://ai.google.dev/gemini-api/docs/api-key to generate a Google AI API key. Once you've done this set the GOOGLE_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"GOOGLE_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Google AI integration lives in the `langchain-google-genai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-google-genai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
-    "\n",
-    "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-1.5-pro\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer. \\n\", response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]}, id='run-eef5b138-1da6-4226-9cfe-ab9073ddd77e-0', usage_metadata={'input_tokens': 21, 'output_tokens': 5, 'total_tokens': 26})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Google AI\n",
+        "---"
      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer. \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren. \\n', response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]}, id='run-fbb35f30-4937-4a81-ae68-f7cb35721a0c-0', usage_metadata={'input_tokens': 16, 'output_tokens': 7, 'total_tokens': 23})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatGoogleGenerativeAI\n",
+        "\n",
+        "This docs will help you get started with Google AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
+        "\n",
+        "Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
+        "\n",
+        ":::info Google AI vs Google Cloud Vertex AI\n",
+        "\n",
+        "Google's Gemini models are accessible through Google AI and through Google Cloud Vertex AI. Using Google AI just requires a Google account and an API key. Using Google Cloud Vertex AI requires a Google Cloud account (with term agreements and billing) but offers enterprise features like customer encription key, virtual private cloud, and more.\n",
+        "\n",
+        "To learn more about the key features of the two APIs see the [Google docs](https://cloud.google.com/vertex-ai/generative-ai/docs/migrate/migrate-google-ai#google-ai).\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_generativeai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatGoogleGenerativeAI](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) | [langchain-google-genai](https://python.langchain.com/api_reference/google_genai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-genai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-genai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Google AI models you'll need to create a Google Acount account, get a Google AI API key, and install the `langchain-google-genai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to https://ai.google.dev/gemini-api/docs/api-key to generate a Google AI API key. Once you've done this set the GOOGLE_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"GOOGLE_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Google AI integration lives in the `langchain-google-genai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-google-genai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+        "\n",
+        "llm = ChatGoogleGenerativeAI(\n",
+        "    model=\"gemini-1.5-pro\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore programmer. \\n\", response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]}, id='run-eef5b138-1da6-4226-9cfe-ab9073ddd77e-0', usage_metadata={'input_tokens': 21, 'output_tokens': 5, 'total_tokens': 26})"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore programmer. \n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren. \\n', response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]}, id='run-fbb35f30-4937-4a81-ae68-f7cb35721a0c-0', usage_metadata={'input_tokens': 16, 'output_tokens': 7, 'total_tokens': 23})"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## Safety Settings\n",
+        "\n",
+        "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "238b2f96-e573-4fac-bbf2-7e52ad926833",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_google_genai import (\n",
+        "    ChatGoogleGenerativeAI,\n",
+        "    HarmBlockThreshold,\n",
+        "    HarmCategory,\n",
+        ")\n",
+        "\n",
+        "llm = ChatGoogleGenerativeAI(\n",
+        "    model=\"gemini-1.5-pro\",\n",
+        "    safety_settings={\n",
+        "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
+        "    },\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5805d40c-deb8-4924-8e72-a294a0482fc9",
+      "metadata": {},
+      "source": [
+        "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the API reference: https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "poetry-venv-2",
+      "language": "python",
+      "name": "poetry-venv-2"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## Safety Settings\n",
-    "\n",
-    "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "238b2f96-e573-4fac-bbf2-7e52ad926833",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_genai import (\n",
-    "    ChatGoogleGenerativeAI,\n",
-    "    HarmBlockThreshold,\n",
-    "    HarmCategory,\n",
-    ")\n",
-    "\n",
-    "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-1.5-pro\",\n",
-    "    safety_settings={\n",
-    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5805d40c-deb8-4924-8e72-a294a0482fc9",
-   "metadata": {},
-   "source": [
-    "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the API reference: https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-2",
-   "language": "python",
-   "name": "poetry-venv-2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
@@ -1,269 +1,269 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Google Cloud Vertex AI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatVertexAI\n",
-    "\n",
-    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
-    "\n",
-    "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
-    "\n",
-    ":::info Google Cloud VertexAI vs Google PaLM\n",
-    "\n",
-    "The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "To use the integration you must:\n",
-    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
-    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
-    "\n",
-    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
-    "\n",
-    "For more information, see: \n",
-    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
-    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
-    "\n",
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain VertexAI integration lives in the `langchain-google-vertexai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-google-vertexai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_vertexai import ChatVertexAI\n",
-    "\n",
-    "llm = ChatVertexAI(\n",
-    "    model=\"gemini-1.5-flash-001\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    max_retries=6,\n",
-    "    stop=None,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer. \\n\", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Google Cloud Vertex AI\n",
+        "---"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer. \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren. \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatVertexAI\n",
+        "\n",
+        "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
+        "\n",
+        "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
+        "\n",
+        ":::info Google Cloud VertexAI vs Google PaLM\n",
+        "\n",
+        "The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "To use the integration you must:\n",
+        "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
+        "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
+        "\n",
+        "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
+        "\n",
+        "For more information, see:\n",
+        "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+        "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
+        "\n",
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain VertexAI integration lives in the `langchain-google-vertexai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-google-vertexai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_google_vertexai import ChatVertexAI\n",
+        "\n",
+        "llm = ChatVertexAI(\n",
+        "    model=\"gemini-1.5-flash-001\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    max_retries=6,\n",
+        "    stop=None,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore programmer. \\n\", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27})"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore programmer. \n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren. \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "poetry-venv-2",
+      "language": "python",
+      "name": "poetry-venv-2"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.1"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-2",
-   "language": "python",
-   "name": "poetry-venv-2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/groq.ipynb
+++ b/docs/docs/integrations/chat/groq.ipynb
@@ -1,266 +1,264 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Groq\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatGroq\n",
-    "\n",
-    "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGroq](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://python.langchain.com/api_reference/groq/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-groq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Groq models you'll need to create a Groq account, get an API key, and install the `langchain-groq` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to the [Groq console](https://console.groq.com/login?utm_source=langchain&utm_content=chat_page) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"GROQ_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Groq integration lives in the `langchain-groq` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f3f510e-2afe-4e76-be41-c5a9665aea63",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-groq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_groq import ChatGroq\n",
-    "\n",
-    "llm = ChatGroq(\n",
-    "    model=\"llama-3.1-8b-instant\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Groq\n",
+        "---"
      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The translation of \"I love programming\" to French is:\n",
-      "\n",
-      "\"J'adore le programmation.\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatGroq\n",
+        "\n",
+        "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatGroq](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://python.langchain.com/api_reference/groq/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-groq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Groq models you'll need to create a Groq account, get an API key, and install the `langchain-groq` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to the [Groq console](https://console.groq.com/login?utm_source=langchain&utm_content=chat_page) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"GROQ_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Groq integration lives in the `langchain-groq` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3f3f510e-2afe-4e76-be41-c5a9665aea63",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-groq"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_groq import ChatGroq\n",
+        "\n",
+        "llm = ChatGroq(\n",
+        "    model=\"llama-3.1-8b-instant\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The translation of \"I love programming\" to French is:\n",
+            "\n",
+            "\"J'adore le programmation.\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/mistralai.ipynb
+++ b/docs/docs/integrations/chat/mistralai.ipynb
@@ -1,262 +1,260 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "53fbf15f",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: MistralAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d295c2a2",
-   "metadata": {},
-   "source": [
-    "# ChatMistralAI\n",
-    "\n",
-    "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatMistralAI](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://python.langchain.com/api_reference/mistralai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "\n",
-    "To access `ChatMistralAI` models you'll need to create a Mistral account, get an API key, and install the `langchain_mistralai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "\n",
-    "A valid [API key](https://console.mistral.ai/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2461605e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"MISTRAL_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "788f37ac",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "007209d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f5c74f9",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Mistral integration lives in the `langchain_mistralai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ab11a65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_mistralai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb1a335e",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e6c38580",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_mistralai import ChatMistralAI\n",
-    "\n",
-    "llm = ChatMistralAI(\n",
-    "    model=\"mistral-large-latest\",\n",
-    "    temperature=0,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aec79099",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8838c3cc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Sure, I\\'d be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J\\'aime programmer\" in French. Let me know if you have any other questions or need further assistance!', response_metadata={'token_usage': {'prompt_tokens': 32, 'total_tokens': 84, 'completion_tokens': 52}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-64bac156-7160-4b68-b67e-4161f63e021f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 52, 'total_tokens': 84})"
+      "cell_type": "raw",
+      "id": "53fbf15f",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: MistralAI\n",
+        "---"
      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "bbf6a048",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sure, I'd be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J'aime programmer\" in French. Let me know if you have any other questions or need further assistance!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32b87f87",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "24e2c51c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmierung. (German translation)', response_metadata={'token_usage': {'prompt_tokens': 26, 'total_tokens': 38, 'completion_tokens': 12}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-dfd4094f-e347-47b0-9056-8ebd7ea35fe7-0', usage_metadata={'input_tokens': 26, 'output_tokens': 12, 'total_tokens': 38})"
+      "cell_type": "markdown",
+      "id": "d295c2a2",
+      "metadata": {},
+      "source": [
+        "# ChatMistralAI\n",
+        "\n",
+        "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatMistralAI](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://python.langchain.com/api_reference/mistralai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "\n",
+        "To access `ChatMistralAI` models you'll need to create a Mistral account, get an API key, and install the `langchain_mistralai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "\n",
+        "A valid [API key](https://console.mistral.ai/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2461605e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"MISTRAL_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "788f37ac",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "007209d5",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0f5c74f9",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Mistral integration lives in the `langchain_mistralai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1ab11a65",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_mistralai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fb1a335e",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e6c38580",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_mistralai import ChatMistralAI\n",
+        "\n",
+        "llm = ChatMistralAI(\n",
+        "    model=\"mistral-large-latest\",\n",
+        "    temperature=0,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "aec79099",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "8838c3cc",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Sure, I\\'d be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J\\'aime programmer\" in French. Let me know if you have any other questions or need further assistance!', response_metadata={'token_usage': {'prompt_tokens': 32, 'total_tokens': 84, 'completion_tokens': 52}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-64bac156-7160-4b68-b67e-4161f63e021f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 52, 'total_tokens': 84})"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "bbf6a048",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sure, I'd be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J'aime programmer\" in French. Let me know if you have any other questions or need further assistance!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "32b87f87",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "24e2c51c",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmierung. (German translation)', response_metadata={'token_usage': {'prompt_tokens': 26, 'total_tokens': 38, 'completion_tokens': 12}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-dfd4094f-e347-47b0-9056-8ebd7ea35fe7-0', usage_metadata={'input_tokens': 26, 'output_tokens': 12, 'total_tokens': 38})"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cb9b5834",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "Head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "cb9b5834",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "Head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/naver.ipynb
+++ b/docs/docs/integrations/chat/naver.ipynb
@@ -1,444 +1,442 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Naver\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8444f1a-e907-4f07-b8b6-68fbedfb868e",
-   "metadata": {},
-   "source": [
-    "# ChatClovaX\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/chat_models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
-    "\n",
-    "[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
-    "| [ChatClovaX](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) |   ❌   | ❌ |                                    ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "|:------------------------------------------:| :---: | :---: | :---: |  :---: | :---: |:-----------------------------------------------------:| :---: |:------------------------------------------------------:|:----------------------------------:|\n",
-    "|❌| ❌ | ❌ | ❌ | ❌ | ❌ |                          ✅                            | ✅ |                           ✅                            |                 ❌                  | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "Before using the chat model, you must go through the four steps below.\n",
-    "\n",
-    "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account \n",
-    "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
-    "3. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
-    "4. Issue a Test or Service API key (See [here](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary#API%ED%82%A4).)\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Set the `NCP_CLOVASTUDIO_API_KEY` environment variable with your API key.\n",
-    "  - Note that if you are using a legacy API Key (that doesn't start with `nv-*` prefix), you might need to get an additional API Key by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app) and set it as `NCP_APIGW_API_KEY`.\n",
-    "\n",
-    "You can add them to your environment variables as below:\n",
-    "\n",
-    "``` bash\n",
-    "export NCP_CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
-    "# Uncomment below to use a legacy API key\n",
-    "# export NCP_APIGW_API_KEY=\"your-api-key-here\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
-    "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your NCP CLOVA Studio API Key: \"\n",
-    "    )\n",
-    "# Uncomment below to use a legacy API key\n",
-    "# if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
-    "#     os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\n",
-    "#         \"Enter your NCP API Gateway API key: \"\n",
-    "#     )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c695442",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6151aeb6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17bf9053-90c5-4955-b239-55a35cb07566",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Naver integration lives in the `langchain-community` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install package\n",
-    "!pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models import ChatClovaX\n",
-    "\n",
-    "chat = ChatClovaX(\n",
-    "    model=\"HCX-003\",\n",
-    "    max_tokens=100,\n",
-    "    temperature=0.5,\n",
-    "    # clovastudio_api_key=\"...\"    # set if you prefer to pass api key directly instead of using environment variables\n",
-    "    # task_id=\"...\"    # set if you want to use fine-tuned model\n",
-    "    # service_app=False    # set True if using Service App. Default value is False (means using Test App)\n",
-    "    # include_ai_filters=False     # set True if you want to detect inappropriate content. Default value is False\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47752b59",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "In addition to invoke, we also support batch and stream functionalities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Naver\n",
+        "---"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to Korean. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love using NAVER AI.\"),\n",
-    "]\n",
-    "\n",
-    "ai_msg = chat.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "24e7377f",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "저는 네이버 AI를 사용하는 것이 좋아요.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+      "cell_type": "markdown",
+      "id": "c8444f1a-e907-4f07-b8b6-68fbedfb868e",
+      "metadata": {},
+      "source": [
+        "# ChatClovaX\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/chat_models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
+        "\n",
+        "[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
+        "| [ChatClovaX](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) |   ❌   | ❌ |                                    ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "|:------------------------------------------:| :---: | :---: | :---: |  :---: | :---: |:-----------------------------------------------------:| :---: |:------------------------------------------------------:|:----------------------------------:|\n",
+        "|❌| ❌ | ❌ | ❌ | ❌ | ❌ |                          ✅                            | ✅ |                           ✅                            |                 ❌                  |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "Before using the chat model, you must go through the four steps below.\n",
+        "\n",
+        "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account\n",
+        "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
+        "3. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
+        "4. Issue a Test or Service API key (See [here](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary#API%ED%82%A4).)\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Set the `NCP_CLOVASTUDIO_API_KEY` environment variable with your API key.\n",
+        "  - Note that if you are using a legacy API Key (that doesn't start with `nv-*` prefix), you might need to get an additional API Key by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app) and set it as `NCP_APIGW_API_KEY`.\n",
+        "\n",
+        "You can add them to your environment variables as below:\n",
+        "\n",
+        "``` bash\n",
+        "export NCP_CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
+        "# Uncomment below to use a legacy API key\n",
+        "# export NCP_APIGW_API_KEY=\"your-api-key-here\"\n",
+        "```"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}. Translate the user sentence.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"Korean\",\n",
-    "        \"input\": \"I love using NAVER AI.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66e69286",
-   "metadata": {},
-   "source": [
-    "## Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Certainly! In Korean, \"Hi\" is pronounced as \"안녕\" (annyeong). The first syllable, \"안,\" sounds like the \"ahh\" sound in \"apple,\" while the second syllable, \"녕,\" sounds like the \"yuh\" sound in \"you.\" So when you put them together, it's like saying \"ahhyuh-nyuhng.\" Remember to pronounce each syllable clearly and separately for accurate pronunciation."
-     ]
-    }
-   ],
-   "source": [
-    "system = \"You are a helpful assistant that can teach Korean pronunciation.\"\n",
-    "human = \"Could you let me know how to say '{phrase}' in Korean?\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "\n",
-    "for chunk in chain.stream({\"phrase\": \"Hi\"}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## Additional functionalities\n",
-    "\n",
-    "### Using fine-tuned models\n",
-    "\n",
-    "You can call fine-tuned models by passing in your corresponding `task_id` parameter. (You don’t need to specify the `model_name` parameter when calling fine-tuned model.)\n",
-    "\n",
-    "You can check `task_id` from corresponding Test App or Service App details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cb436788",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
+        "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your NCP CLOVA Studio API Key: \"\n",
+        "    )\n",
+        "# Uncomment below to use a legacy API key\n",
+        "# if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
+        "#     os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\n",
+        "#         \"Enter your NCP API Gateway API key: \"\n",
+        "#     )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7c695442",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6151aeb6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "17bf9053-90c5-4955-b239-55a35cb07566",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Naver integration lives in the `langchain-community` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# install package\n",
+        "!pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.chat_models import ChatClovaX\n",
+        "\n",
+        "chat = ChatClovaX(\n",
+        "    model=\"HCX-003\",\n",
+        "    max_tokens=100,\n",
+        "    temperature=0.5,\n",
+        "    # clovastudio_api_key=\"...\"    # set if you prefer to pass api key directly instead of using environment variables\n",
+        "    # task_id=\"...\"    # set if you want to use fine-tuned model\n",
+        "    # service_app=False    # set True if using Service App. Default value is False (means using Test App)\n",
+        "    # include_ai_filters=False     # set True if you want to detect inappropriate content. Default value is False\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "47752b59",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "In addition to invoke, we also support batch and stream functionalities."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to Korean. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love using NAVER AI.\"),\n",
+        "]\n",
+        "\n",
+        "ai_msg = chat.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "24e7377f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "저는 네이버 AI를 사용하는 것이 좋아요.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}. Translate the user sentence.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | chat\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"Korean\",\n",
+        "        \"input\": \"I love using NAVER AI.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "66e69286",
+      "metadata": {},
+      "source": [
+        "## Streaming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Certainly! In Korean, \"Hi\" is pronounced as \"안녕\" (annyeong). The first syllable, \"안,\" sounds like the \"ahh\" sound in \"apple,\" while the second syllable, \"녕,\" sounds like the \"yuh\" sound in \"you.\" So when you put them together, it's like saying \"ahhyuh-nyuhng.\" Remember to pronounce each syllable clearly and separately for accurate pronunciation."
+          ]
+        }
+      ],
+      "source": [
+        "system = \"You are a helpful assistant that can teach Korean pronunciation.\"\n",
+        "human = \"Could you let me know how to say '{phrase}' in Korean?\"\n",
+        "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+        "\n",
+        "chain = prompt | chat\n",
+        "\n",
+        "for chunk in chain.stream({\"phrase\": \"Hi\"}):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## Additional functionalities\n",
+        "\n",
+        "### Using fine-tuned models\n",
+        "\n",
+        "You can call fine-tuned models by passing in your corresponding `task_id` parameter. (You don’t need to specify the `model_name` parameter when calling fine-tuned model.)\n",
+        "\n",
+        "You can check `task_id` from corresponding Test App or Service App details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "cb436788",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "fine_tuned_model = ChatClovaX(\n",
+        "    task_id=\"5s8egt3a\",  # set if you want to use fine-tuned model\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "fine_tuned_model.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f428deaf",
+      "metadata": {},
+      "source": [
+        "### Service App\n",
+        "\n",
+        "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
+        "\n",
+        "For a Service App, you should use a corresponding Service API key and can only be called with it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dcf566df",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Update environment variables\n",
+        "\n",
+        "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+        "    \"Enter NCP CLOVA Studio Service API Key: \"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "cebe27ae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "chat = ChatClovaX(\n",
+        "    service_app=True,  # True if you want to use your service app, default value is False.\n",
+        "    # clovastudio_api_key=\"...\"  # if you prefer to pass api key in directly instead of using env vars\n",
+        "    model=\"HCX-003\",\n",
+        "    # other params...\n",
+        ")\n",
+        "ai_msg = chat.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d73e7140",
+      "metadata": {},
+      "source": [
+        "### AI Filter\n",
+        "\n",
+        "AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#AIFilter) for details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "32bfbc93",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "chat = ChatClovaX(\n",
+        "    model=\"HCX-003\",\n",
+        "    include_ai_filters=True,  # True if you want to enable ai filter\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "ai_msg = chat.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7bd9e179",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(ai_msg.response_metadata[\"ai_filter\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html"
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "fine_tuned_model = ChatClovaX(\n",
-    "    task_id=\"5s8egt3a\",  # set if you want to use fine-tuned model\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "fine_tuned_model.invoke(messages)"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "f428deaf",
-   "metadata": {},
-   "source": [
-    "### Service App\n",
-    "\n",
-    "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
-    "\n",
-    "For a Service App, you should use a corresponding Service API key and can only be called with it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dcf566df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update environment variables\n",
-    "\n",
-    "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
-    "    \"Enter NCP CLOVA Studio Service API Key: \"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cebe27ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatClovaX(\n",
-    "    service_app=True,  # True if you want to use your service app, default value is False.\n",
-    "    # clovastudio_api_key=\"...\"  # if you prefer to pass api key in directly instead of using env vars\n",
-    "    model=\"HCX-003\",\n",
-    "    # other params...\n",
-    ")\n",
-    "ai_msg = chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d73e7140",
-   "metadata": {},
-   "source": [
-    "### AI Filter\n",
-    "\n",
-    "AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#AIFilter) for details. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32bfbc93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatClovaX(\n",
-    "    model=\"HCX-003\",\n",
-    "    include_ai_filters=True,  # True if you want to enable ai filter\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "ai_msg = chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bd9e179",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(ai_msg.response_metadata[\"ai_filter\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/netmind.ipynb
+++ b/docs/docs/integrations/chat/netmind.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Netmind\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatNetmind\n",
+    "\n",
+    "This will help you getting started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
+    "\n",
+    "-  See https://www.netmind.ai/ for an example.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class                                                                                        | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/) | Package downloads | Package latest |\n",
+    "|:---------------------------------------------------------------------------------------------| :--- |:-----:|:------------:|:--------------------------------------------------------------:| :---: | :---: |\n",
+    "| [ChatNetmind](https://python.langchain.com/api_reference/) | [langchain-netmind](https://python.langchain.com/api_reference/) |   ✅   |      ❌       |                               ❌                                | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-netmind?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-netmind?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+    "|:-----------------------------------------------:|:---------------------------------------------------------:|:---------:|:---------------------------------------------------:|:-----------:|:-----------:|:----------------------------------------------------------:|:------------:|:-----------------------------------------------------------:|:---------------------------------------:|\n",
+    "|                        ✅                        |                             ✅                             |     ✅     |                          ❌                          |      ❌      |      ❌      |                             ✅                              |      ✅       |                              ✅                              |                    ✅                    | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Netmind models you'll need to create a/an Netmind account, get an API key, and install the `langchain-netmind` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:00:30.732333Z",
+     "start_time": "2025-03-20T02:00:28.384208Z"
+    }
+   },
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NETMIND_API_KEY\"):\n",
+    "    os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:00:33.421446Z",
+     "start_time": "2025-03-20T02:00:33.419081Z"
+    }
+   },
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Netmind integration lives in the `langchain-netmind` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:00:35.923300Z",
+     "start_time": "2025-03-20T02:00:34.505928Z"
+    }
+   },
+   "source": [
+    "%pip install -qU langchain-netmind"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.0.1\u001B[0m\r\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:08.007764Z",
+     "start_time": "2025-03-20T02:01:07.391951Z"
+    }
+   },
+   "source": [
+    "from langchain_netmind import ChatNetmind\n",
+    "\n",
+    "llm = ChatNetmind(\n",
+    "    model=\"deepseek-ai/DeepSeek-V3\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": "## Invocation\n"
+  },
+  {
+   "cell_type": "code",
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:19.011273Z",
+     "start_time": "2025-03-20T02:01:10.295510Z"
+    }
+   },
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 31, 'total_tokens': 44, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ca6c2010-844d-4bf6-baac-6e248491b000-0', usage_metadata={'input_tokens': 31, 'output_tokens': 13, 'total_tokens': 44, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:20.240190Z",
+     "start_time": "2025-03-20T02:01:20.238242Z"
+    }
+   },
+   "source": [
+    "print(ai_msg.content)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore programmer.\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:27.456393Z",
+     "start_time": "2025-03-20T02:01:23.993410Z"
+    }
+   },
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe es zu programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 26, 'total_tokens': 40, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d63adcc6-53ba-4caa-9a79-78d640b39274-0', usage_metadata={'input_tokens': 26, 'output_tokens': 14, 'total_tokens': 40, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": ""
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatNetmind features and configurations head to the API reference:  \n",
+    "* [API reference](https://python.langchain.com/api_reference/)  \n",
+    "* [langchain-netmind](https://github.com/protagolabs/langchain-netmind)  \n",
+    "* [pypi](https://pypi.org/project/langchain-netmind/)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "30f8be8c940bfbf3"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
+++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
--- a/docs/docs/integrations/chat/ollama.ipynb
+++ b/docs/docs/integrations/chat/ollama.ipynb
--- a/docs/docs/integrations/chat/together.ipynb
+++ b/docs/docs/integrations/chat/together.ipynb
@@ -1,267 +1,265 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Together\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatTogether\n",
-    "\n",
-    "\n",
-    "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
-    "\n",
-    "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatTogether](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://python.langchain.com/api_reference/together/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-together?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-together?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Together models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://api.together.ai) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"TOGETHER_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Together integration lives in the `langchain-together` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-together"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_together import ChatTogether\n",
-    "\n",
-    "llm = ChatTogether(\n",
-    "    model=\"meta-llama/Llama-3-70b-chat-hf\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-eabcbe33-cdd8-45b8-ab0b-f90b6e7dfad8-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Together\n",
+        "---"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 30, 'total_tokens': 37}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a249aa24-ee31-46ba-9bf9-f4eb135b0a95-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatTogether\n",
+        "\n",
+        "\n",
+        "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
+        "\n",
+        "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatTogether](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://python.langchain.com/api_reference/together/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-together?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-together?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Together models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [this page](https://api.together.ai) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"TOGETHER_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Together integration lives in the `langchain-together` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-together"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_together import ChatTogether\n",
+        "\n",
+        "llm = ChatTogether(\n",
+        "    model=\"meta-llama/Llama-3-70b-chat-hf\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-eabcbe33-cdd8-45b8-ab0b-f90b6e7dfad8-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore la programmation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 30, 'total_tokens': 37}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a249aa24-ee31-46ba-9bf9-f4eb135b0a95-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/providers/vectara/vectara_chat.ipynb
+++ b/docs/docs/integrations/providers/vectara/vectara_chat.ipynb
@@ -5,21 +5,38 @@
   "id": "134a0785",
   "metadata": {},
   "source": [
-    "# Vectara Chat\n",
+    "## Overview\n",
    "\n",
    "[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.\n",
-    "\n",
    "Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:\n",
    "1. A way to extract text from files (PDF, PPT, DOCX, etc)\n",
    "2. ML-based chunking that provides state of the art performance.\n",
    "3. The [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model.\n",
    "4. Its own internal vector database where text chunks and embedding vectors are stored.\n",
-    "5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
+    "5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
    "6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
    "\n",
-    "See the [Vectara API documentation](https://docs.vectara.com/docs/) for more information on how to use the API.\n",
+    "For more information:\n",
+    "- [Documentation](https://docs.vectara.com/docs/)\n",
+    "- [API Playground](https://docs.vectara.com/docs/rest-api/)\n",
+    "- [Quickstart](https://docs.vectara.com/docs/quickstart)\n",
    "\n",
-    "This notebook shows how to use Vectara's [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) functionality, which provides automatic storage of conversation history and ensures follow up questions consider that history."
+    "\n",
+    "This notebook shows how to use Vectara's [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) functionality, which provides automatic storage of conversation history and ensures follow up questions consider that history.\n",
+    "\n",
+    "### Setup\n",
+    "\n",
+    "To use the `VectaraVectorStore` you first need to install the partner package.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4a2f525-4805-4880-8bfa-18fe6f1cd1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv pip install -U pip && uv pip install -qU langchain-vectara"
   ]
  },
  {
@@ -27,17 +44,19 @@
   "id": "56372c5b",
   "metadata": {},
   "source": [
-    "# Getting Started\n",
+    "## Getting Started\n",
    "\n",
    "To get started, use the following steps:\n",
-    "1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.\n",
+    "1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial.\n",
    "2. Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
    "3. Next you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\" and you now have an active API key. Keep this key confidential. \n",
    "\n",
-    "To use LangChain with Vectara, you'll need to have these three values: `customer ID`, `corpus ID` and `api_key`.\n",
-    "You can provide those to LangChain in two ways:\n",
+    "To use LangChain with Vectara, you'll need to have these two values: `corpus_key` and `api_key`.\n",
+    "You can provide `VECTARA_API_KEY` to LangChain in two ways:\n",
    "\n",
-    "1. Include in your environment these three variables: `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`.\n",
+    "## Instantiation\n",
+    "\n",
+    "1. Include in your environment these two variables: `VECTARA_API_KEY`.\n",
    "\n",
    "   For example, you can set these variables using os.environ and getpass as follows:\n",
    "\n",
@@ -45,8 +64,6 @@
    "import os\n",
    "import getpass\n",
    "\n",
-    "os.environ[\"VECTARA_CUSTOMER_ID\"] = getpass.getpass(\"Vectara Customer ID:\")\n",
-    "os.environ[\"VECTARA_CORPUS_ID\"] = getpass.getpass(\"Vectara Corpus ID:\")\n",
    "os.environ[\"VECTARA_API_KEY\"] = getpass.getpass(\"Vectara API Key:\")\n",
    "```\n",
    "\n",
@@ -54,17 +71,16 @@
    "\n",
    "```python\n",
    "vectara = Vectara(\n",
-    "                vectara_customer_id=vectara_customer_id,\n",
-    "                vectara_corpus_id=vectara_corpus_id,\n",
-    "                vectara_api_key=vectara_api_key\n",
-    "            )\n",
+    "    vectara_api_key=vectara_api_key\n",
+    ")\n",
    "```\n",
+    "\n",
    "In this notebook we assume they are provided in the environment."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
   "id": "70c4e529",
   "metadata": {
    "tags": []
@@ -73,14 +89,15 @@
   "source": [
    "import os\n",
    "\n",
-    "os.environ[\"VECTARA_API_KEY\"] = \"<YOUR_VECTARA_API_KEY>\"\n",
-    "os.environ[\"VECTARA_CORPUS_ID\"] = \"<YOUR_VECTARA_CORPUS_ID>\"\n",
-    "os.environ[\"VECTARA_CUSTOMER_ID\"] = \"<YOUR_VECTARA_CUSTOMER_ID>\"\n",
+    "os.environ[\"VECTARA_API_KEY\"] = \"<VECTARA_API_KEY>\"\n",
+    "os.environ[\"VECTARA_CORPUS_KEY\"] = \"<VECTARA_CORPUS_KEY>\"\n",
    "\n",
-    "from langchain_community.vectorstores import Vectara\n",
-    "from langchain_community.vectorstores.vectara import (\n",
-    "    RerankConfig,\n",
-    "    SummaryConfig,\n",
+    "from langchain_vectara import Vectara\n",
+    "from langchain_vectara.vectorstores import (\n",
+    "    CorpusConfig,\n",
+    "    GenerationConfig,\n",
+    "    MmrReranker,\n",
+    "    SearchConfig,\n",
    "    VectaraQueryConfig,\n",
    ")"
   ]
@@ -101,7 +118,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "id": "01c46e92",
   "metadata": {
    "tags": []
@@ -110,10 +127,11 @@
   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "\n",
-    "loader = TextLoader(\"state_of_the_union.txt\")\n",
+    "loader = TextLoader(\"../document_loaders/example_data/state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "\n",
-    "vectara = Vectara.from_documents(documents, embedding=None)"
+    "corpus_key = os.getenv(\"VECTARA_CORPUS_KEY\")\n",
+    "vectara = Vectara.from_documents(documents, embedding=None, corpus_key=corpus_key)"
   ]
  },
  {
@@ -126,18 +144,29 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "id": "1b41a10b-bf68-4689-8f00-9aed7675e2ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
-    "summary_config = SummaryConfig(is_enabled=True, max_results=7, response_lang=\"eng\")\n",
-    "rerank_config = RerankConfig(reranker=\"mmr\", rerank_k=50, mmr_diversity_bias=0.2)\n",
-    "config = VectaraQueryConfig(\n",
-    "    k=10, lambda_val=0.005, rerank_config=rerank_config, summary_config=summary_config\n",
+    "generation_config = GenerationConfig(\n",
+    "    max_used_search_results=7,\n",
+    "    response_language=\"eng\",\n",
+    "    generation_preset_name=\"vectara-summary-ext-24-05-med-omni\",\n",
+    "    enable_factual_consistency_score=True,\n",
    ")\n",
+    "search_config = SearchConfig(\n",
+    "    corpora=[CorpusConfig(corpus_key=corpus_key, limit=25)],\n",
+    "    reranker=MmrReranker(diversity_bias=0.2),\n",
+    ")\n",
+    "\n",
+    "config = VectaraQueryConfig(\n",
+    "    search=search_config,\n",
+    "    generation=generation_config,\n",
+    ")\n",
+    "\n",
    "\n",
    "bot = vectara.as_chat(config)"
   ]
@@ -147,12 +176,15 @@
   "id": "83f38c18-ac82-45f4-a79e-8b37ce1ae115",
   "metadata": {},
   "source": [
+    "\n",
+    "## Invocation\n",
+    "\n",
    "Here's an example of asking a question with no chat history"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
   "id": "bc672290-8a8b-4828-a90c-f1bbdd6b3920",
   "metadata": {
    "tags": []
@@ -161,10 +193,10 @@
    {
     "data": {
      "text/plain": [
-       "'The President expressed gratitude to Justice Breyer and highlighted the significance of nominating Ketanji Brown Jackson to the Supreme Court, praising her legal expertise and commitment to upholding excellence [1]. The President also reassured the public about the situation with gas prices and the conflict in Ukraine, emphasizing unity with allies and the belief that the world will emerge stronger from these challenges [2][4]. Additionally, the President shared personal experiences related to economic struggles and the importance of passing the American Rescue Plan to support those in need [3]. The focus was also on job creation and economic growth, acknowledging the impact of inflation on families [5]. While addressing cancer as a significant issue, the President discussed plans to enhance cancer research and support for patients and families [7].'"
+       "'The president stated that nominating someone to serve on the United States Supreme Court is one of the most serious constitutional responsibilities. He nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, describing her as one of the nation’s top legal minds who will continue Justice Breyer’s legacy of excellence and noting her experience as a former top litigator in private practice [1].'"
      ]
     },
-     "execution_count": 4,
+     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -183,7 +215,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
   "id": "9c95460b-7116-4155-a9d2-c0fb027ee592",
   "metadata": {
    "tags": []
@@ -192,10 +224,10 @@
    {
     "data": {
      "text/plain": [
-       "\"In his remarks, the President specified that Ketanji Brown Jackson is succeeding Justice Breyer on the United States Supreme Court[1]. The President praised Jackson as a top legal mind who will continue Justice Breyer's legacy of excellence. The nomination of Jackson was highlighted as a significant constitutional responsibility of the President[1]. The President emphasized the importance of this nomination and the qualities that Jackson brings to the role. The focus was on the transition from Justice Breyer to Judge Ketanji Brown Jackson on the Supreme Court[1].\""
+       "'Yes, the president mentioned that Ketanji Brown Jackson succeeded Justice Breyer [1].'"
      ]
     },
-     "execution_count": 5,
+     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -217,7 +249,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
   "id": "936dc62f",
   "metadata": {
    "tags": []
@@ -227,14 +259,14 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Judge Ketanji Brown Jackson is a nominee for the United States Supreme Court, known for her legal expertise and experience as a former litigator. She is praised for her potential to continue the legacy of excellence on the Court[1]. While the search results provide information on various topics like innovation, economic growth, and healthcare initiatives, they do not directly address Judge Ketanji Brown Jackson's specific accomplishments. Therefore, I do not have enough information to answer this question."
+      "The president acknowledged the significant impact of COVID-19 on the nation, expressing understanding of the public's fatigue and frustration. He emphasized the need to view COVID-19 not as a partisan issue but as a serious disease, urging unity among Americans. The president highlighted the progress made, noting that severe cases have decreased significantly, and mentioned new CDC guidelines allowing most Americans to be mask-free. He also pointed out the efforts to vaccinate the nation and provide economic relief, and the ongoing commitment to vaccinate the world [2], [3], [5]."
     ]
    }
   ],
   "source": [
    "output = {}\n",
    "curr_key = None\n",
-    "for chunk in bot.stream(\"what about her accopmlishments?\"):\n",
+    "for chunk in bot.stream(\"what did he said about the covid?\"):\n",
    "    for key in chunk:\n",
    "        if key not in output:\n",
    "            output[key] = chunk[key]\n",
@@ -244,6 +276,83 @@
    "            print(chunk[key], end=\"\", flush=True)\n",
    "        curr_key = key"
   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cefdf72b1d90085a",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "For additional capabilities you can use chaining."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "167bc806-395e-46bf-80cc-3c5d43164f42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "So, the president talked about how the COVID-19 sickness has affected a lot of people in the country. He said that it's important for everyone to work together to fight the sickness, no matter what political party they are in. The president also mentioned that they are working hard to give vaccines to people to help protect them from getting sick. They are also giving money and help to people who need it, like food, housing, and cheaper health insurance. The president also said that they are sending vaccines to many other countries to help people all around the world stay healthy.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai.chat_models import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that explains the stuff to a five year old.  Vectara is providing the answer.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{vectara_response}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def get_vectara_response(question: dict) -> str:\n",
+    "    \"\"\"\n",
+    "    Calls Vectara as_chat and returns the answer string.  This encapsulates\n",
+    "    the Vectara call.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        response = bot.invoke(question[\"question\"])\n",
+    "        return response[\"answer\"]\n",
+    "    except Exception as e:\n",
+    "        return \"I'm sorry, I couldn't get an answer from Vectara.\"\n",
+    "\n",
+    "\n",
+    "# Create the chain\n",
+    "chain = get_vectara_response | prompt | llm | StrOutputParser()\n",
+    "\n",
+    "\n",
+    "# Invoke the chain\n",
+    "result = chain.invoke({\"question\": \"what did he say about the covid?\"})\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b8bb761-db4a-436c-8939-41e9f8652083",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## API reference\n",
+    "\n",
+    "You can look at the [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) documentation for the details."
+   ]
  }
 ],
 "metadata": {
@@ -262,7 +371,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.0"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/vllm.ipynb
+++ b/docs/docs/integrations/chat/vllm.ipynb
@@ -1,244 +1,242 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "eb65deaa",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: vLLM Chat\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f82e243-f4ee-44e2-b417-099b6401ae3e",
-   "metadata": {},
-   "source": [
-    "# vLLM Chat\n",
-    "\n",
-    "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
-    "\n",
-    "## Overview\n",
-    "This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "See the vLLM docs [here](https://docs.vllm.ai/en/latest/).\n",
-    "\n",
-    "To access vLLM models through LangChain, you'll need to install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Authentication will depend on specifics of the inference server."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3b1707a-cf2c-4367-94e3-436c43402503",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e40bd5e-cbaa-41ef-aaf9-0858eb207184",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0739b647-609b-46d3-bdd3-e86fe4463288",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain vLLM integration can be accessed via the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7afcfbdc-56aa-4529-825a-8acbe7aa5241",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2cf576d6-7b67-4937-bf99-39071e85720c",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "060a2e3d-d42f-4221-bd09-a9a06544dcd3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "from langchain_core.prompts.chat import (\n",
-    "    ChatPromptTemplate,\n",
-    "    HumanMessagePromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
-    ")\n",
-    "from langchain_openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bf24d732-68a9-44fd-b05d-4903ce5620c6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "inference_server_url = \"http://localhost:8000/v1\"\n",
-    "\n",
-    "llm = ChatOpenAI(\n",
-    "    model=\"mosaicml/mpt-7b\",\n",
-    "    openai_api_key=\"EMPTY\",\n",
-    "    openai_api_base=inference_server_url,\n",
-    "    max_tokens=5,\n",
-    "    temperature=0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34b18328-5e8b-4ff2-9b89-6fbb76b5c7f0",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "aea4e363-5688-4b07-82ed-6aa8153c2377",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' Io amo programmare', additional_kwargs={}, example=False)"
+      "cell_type": "raw",
+      "id": "eb65deaa",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: vLLM Chat\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8f82e243-f4ee-44e2-b417-099b6401ae3e",
+      "metadata": {},
+      "source": [
+        "# vLLM Chat\n",
+        "\n",
+        "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
+        "\n",
+        "## Overview\n",
+        "This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "See the vLLM docs [here](https://docs.vllm.ai/en/latest/).\n",
+        "\n",
+        "To access vLLM models through LangChain, you'll need to install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Authentication will depend on specifics of the inference server."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c3b1707a-cf2c-4367-94e3-436c43402503",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1e40bd5e-cbaa-41ef-aaf9-0858eb207184",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0739b647-609b-46d3-bdd3-e86fe4463288",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain vLLM integration can be accessed via the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7afcfbdc-56aa-4529-825a-8acbe7aa5241",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2cf576d6-7b67-4937-bf99-39071e85720c",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "060a2e3d-d42f-4221-bd09-a9a06544dcd3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_core.messages import HumanMessage, SystemMessage\n",
+        "from langchain_core.prompts.chat import (\n",
+        "    ChatPromptTemplate,\n",
+        "    HumanMessagePromptTemplate,\n",
+        "    SystemMessagePromptTemplate,\n",
+        ")\n",
+        "from langchain_openai import ChatOpenAI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "bf24d732-68a9-44fd-b05d-4903ce5620c6",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "inference_server_url = \"http://localhost:8000/v1\"\n",
+        "\n",
+        "llm = ChatOpenAI(\n",
+        "    model=\"mosaicml/mpt-7b\",\n",
+        "    openai_api_key=\"EMPTY\",\n",
+        "    openai_api_base=inference_server_url,\n",
+        "    max_tokens=5,\n",
+        "    temperature=0,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "34b18328-5e8b-4ff2-9b89-6fbb76b5c7f0",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "aea4e363-5688-4b07-82ed-6aa8153c2377",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=' Io amo programmare', additional_kwargs={}, example=False)"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    SystemMessage(\n",
+        "        content=\"You are a helpful assistant that translates English to Italian.\"\n",
+        "    ),\n",
+        "    HumanMessage(\n",
+        "        content=\"Translate the following sentence from English to Italian: I love programming.\"\n",
+        "    ),\n",
+        "]\n",
+        "llm.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a580a1e4-11a3-4277-bfba-bfb414ac7201",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dd0f4043-48bd-4245-8bdb-e7669666a277",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "265f5d51-0a76-4808-8d13-ef598ee6e366",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
+        "\n",
+        "Refer to the vLLM [documentation](https://docs.vllm.ai/en/latest/) as well."
      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant that translates English to Italian.\"\n",
-    "    ),\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate the following sentence from English to Italian: I love programming.\"\n",
-    "    ),\n",
-    "]\n",
-    "llm.invoke(messages)"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "a580a1e4-11a3-4277-bfba-bfb414ac7201",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd0f4043-48bd-4245-8bdb-e7669666a277",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "265f5d51-0a76-4808-8d13-ef598ee6e366",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
-    "\n",
-    "Refer to the vLLM [documentation](https://docs.vllm.ai/en/latest/) as well."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/xai.ipynb
+++ b/docs/docs/integrations/chat/xai.ipynb
@@ -1,332 +1,330 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: xAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatXAI\n",
-    "\n",
-    "\n",
-    "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
-    "\n",
-    "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"XAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain xAI integration lives in the `langchain-xai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-xai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_xai import ChatXAI\n",
-    "\n",
-    "llm = ChatXAI(\n",
-    "    model=\"grok-beta\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: xAI\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatXAI\n",
+        "\n",
+        "\n",
+        "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
+        "\n",
+        "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e074bce1-0994-4b83-b393-ae7aa7e21750",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
-    "\n",
-    "### ChatXAI.bind_tools()\n",
-    "\n",
-    "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
-    "```\n",
-    "{\n",
-    "    \"name\": \"...\",\n",
-    "    \"description\": \"...\",\n",
-    "    \"parameters\": {...}  # JSONSchema\n",
-    "}\n",
-    "```\n",
-    "and passed in every model invocation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class GetWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a given location\"\"\"\n",
-    "\n",
-    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"XAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain xAI integration lives in the `langchain-xai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-xai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_xai import ChatXAI\n",
+        "\n",
+        "llm = ChatXAI(\n",
+        "    model=\"grok-beta\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore programmer.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e074bce1-0994-4b83-b393-ae7aa7e21750",
+      "metadata": {},
+      "source": [
+        "## Tool calling\n",
+        "\n",
+        "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
+        "\n",
+        "### ChatXAI.bind_tools()\n",
+        "\n",
+        "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
+        "```\n",
+        "{\n",
+        "    \"name\": \"...\",\n",
+        "    \"description\": \"...\",\n",
+        "    \"parameters\": {...}  # JSONSchema\n",
+        "}\n",
+        "```\n",
+        "and passed in every model invocation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from pydantic import BaseModel, Field\n",
+        "\n",
+        "\n",
+        "class GetWeather(BaseModel):\n",
+        "    \"\"\"Get the current weather in a given location\"\"\"\n",
+        "\n",
+        "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+        "\n",
+        "\n",
+        "llm_with_tools = llm.bind_tools([GetWeather])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ai_msg = llm_with_tools.invoke(\n",
+        "    \"what is the weather like in San Francisco\",\n",
+        ")\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in San Francisco\",\n",
-    ")\n",
-    "ai_msg"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/yi.ipynb
+++ b/docs/docs/integrations/chat/yi.ipynb
@@ -1,229 +1,227 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ChatYI\n",
-    "\n",
-    "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
-    "\n",
-    "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatYi](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access ChatYi models you'll need to create a/an 01.AI account, get an API key, and install the `langchain_community` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [01.AI](https://platform.01.ai) to sign up to 01.AI and generate an API key. Once you've done this set the `YI_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"YI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `langchain_community` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.yi import ChatYi\n",
-    "\n",
-    "llm = ChatYi(\n",
-    "    model=\"yi-large\",\n",
-    "    temperature=0,\n",
-    "    timeout=60,\n",
-    "    yi_api_base=\"https://api.01.ai/v1/chat/completions\",\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Invocation\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Large Language Models (LLMs) have the potential to significantly impact healthcare by enhancing various aspects of patient care, research, and administrative processes. Here are some potential applications:\\n\\n1. **Clinical Documentation and Reporting**: LLMs can assist in generating patient reports and documentation by understanding and summarizing clinical notes, making the process more efficient and reducing the administrative burden on healthcare professionals.\\n\\n2. **Medical Coding and Billing**: These models can help in automating the coding process for medical billing by accurately translating clinical notes into standardized codes, reducing errors and improving billing efficiency.\\n\\n3. **Clinical Decision Support**: LLMs can analyze patient data and medical literature to provide evidence-based recommendations to healthcare providers, aiding in diagnosis and treatment planning.\\n\\n4. **Patient Education and Communication**: By simplifying medical jargon, LLMs can help in educating patients about their conditions, treatment options, and preventive care, improving patient engagement and health literacy.\\n\\n5. **Natural Language Processing (NLP) for EHRs**: LLMs can enhance NLP capabilities in Electronic Health Records (EHRs) systems, enabling better extraction of information from unstructured data, such as clinical notes, to support data-driven decision-making.\\n\\n6. **Drug Discovery and Development**: LLMs can analyze biomedical literature and clinical trial data to identify new drug candidates, predict drug interactions, and support the development of personalized medicine.\\n\\n7. **Telemedicine and Virtual Health Assistants**: Integrated into telemedicine platforms, LLMs can provide preliminary assessments and triage, offering patients basic health advice and determining the urgency of their needs, thus optimizing the utilization of healthcare resources.\\n\\n8. **Research and Literature Review**: LLMs can expedite the process of reviewing medical literature by quickly identifying relevant studies and summarizing findings, accelerating research and evidence-based practice.\\n\\n9. **Personalized Medicine**: By analyzing a patient's genetic information and medical history, LLMs can help in tailoring treatment plans and medication dosages, contributing to the advancement of personalized medicine.\\n\\n10. **Quality Improvement and Risk Assessment**: LLMs can analyze healthcare data to identify patterns that may indicate areas for quality improvement or potential risks, such as hospital-acquired infections or adverse drug events.\\n\\n11. **Mental Health Support**: LLMs can provide mental health support by offering coping strategies, mindfulness exercises, and preliminary assessments, serving as a complement to professional mental health services.\\n\\n12. **Continuing Medical Education (CME)**: LLMs can personalize CME by recommending educational content based on a healthcare provider's practice area, patient demographics, and emerging medical literature, ensuring that professionals stay updated with the latest advancements.\\n\\nWhile the applications of LLMs in healthcare are promising, it's crucial to address challenges such as data privacy, model bias, and the need for regulatory approval to ensure that these technologies are implemented safely and ethically.\", response_metadata={'token_usage': {'completion_tokens': 656, 'prompt_tokens': 40, 'total_tokens': 696}, 'model': 'yi-large'}, id='run-870850bd-e4bf-4265-8730-1736409c0acf-0')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# ChatYI\n",
+        "\n",
+        "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
+        "\n",
+        "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatYi](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access ChatYi models you'll need to create a/an 01.AI account, get an API key, and install the `langchain_community` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [01.AI](https://platform.01.ai) to sign up to 01.AI and generate an API key. Once you've done this set the `YI_API_KEY` environment variable:"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are an AI assistant specializing in technology trends.\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"What are the potential applications of large language models in healthcare?\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 33, 'total_tokens': 41}, 'model': 'yi-large'}, id='run-daa3bc58-8289-4d72-a24e-80622fa90d6d-0')"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"YI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `langchain_community` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.chat_models.yi import ChatYi\n",
+        "\n",
+        "llm = ChatYi(\n",
+        "    model=\"yi-large\",\n",
+        "    temperature=0,\n",
+        "    timeout=60,\n",
+        "    yi_api_base=\"https://api.01.ai/v1/chat/completions\",\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Invocation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"Large Language Models (LLMs) have the potential to significantly impact healthcare by enhancing various aspects of patient care, research, and administrative processes. Here are some potential applications:\\n\\n1. **Clinical Documentation and Reporting**: LLMs can assist in generating patient reports and documentation by understanding and summarizing clinical notes, making the process more efficient and reducing the administrative burden on healthcare professionals.\\n\\n2. **Medical Coding and Billing**: These models can help in automating the coding process for medical billing by accurately translating clinical notes into standardized codes, reducing errors and improving billing efficiency.\\n\\n3. **Clinical Decision Support**: LLMs can analyze patient data and medical literature to provide evidence-based recommendations to healthcare providers, aiding in diagnosis and treatment planning.\\n\\n4. **Patient Education and Communication**: By simplifying medical jargon, LLMs can help in educating patients about their conditions, treatment options, and preventive care, improving patient engagement and health literacy.\\n\\n5. **Natural Language Processing (NLP) for EHRs**: LLMs can enhance NLP capabilities in Electronic Health Records (EHRs) systems, enabling better extraction of information from unstructured data, such as clinical notes, to support data-driven decision-making.\\n\\n6. **Drug Discovery and Development**: LLMs can analyze biomedical literature and clinical trial data to identify new drug candidates, predict drug interactions, and support the development of personalized medicine.\\n\\n7. **Telemedicine and Virtual Health Assistants**: Integrated into telemedicine platforms, LLMs can provide preliminary assessments and triage, offering patients basic health advice and determining the urgency of their needs, thus optimizing the utilization of healthcare resources.\\n\\n8. **Research and Literature Review**: LLMs can expedite the process of reviewing medical literature by quickly identifying relevant studies and summarizing findings, accelerating research and evidence-based practice.\\n\\n9. **Personalized Medicine**: By analyzing a patient's genetic information and medical history, LLMs can help in tailoring treatment plans and medication dosages, contributing to the advancement of personalized medicine.\\n\\n10. **Quality Improvement and Risk Assessment**: LLMs can analyze healthcare data to identify patterns that may indicate areas for quality improvement or potential risks, such as hospital-acquired infections or adverse drug events.\\n\\n11. **Mental Health Support**: LLMs can provide mental health support by offering coping strategies, mindfulness exercises, and preliminary assessments, serving as a complement to professional mental health services.\\n\\n12. **Continuing Medical Education (CME)**: LLMs can personalize CME by recommending educational content based on a healthcare provider's practice area, patient demographics, and emerging medical literature, ensuring that professionals stay updated with the latest advancements.\\n\\nWhile the applications of LLMs in healthcare are promising, it's crucial to address challenges such as data privacy, model bias, and the need for regulatory approval to ensure that these technologies are implemented safely and ethically.\", response_metadata={'token_usage': {'completion_tokens': 656, 'prompt_tokens': 40, 'total_tokens': 696}, 'model': 'yi-large'}, id='run-870850bd-e4bf-4265-8730-1736409c0acf-0')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.messages import HumanMessage, SystemMessage\n",
+        "\n",
+        "messages = [\n",
+        "    SystemMessage(content=\"You are an AI assistant specializing in technology trends.\"),\n",
+        "    HumanMessage(\n",
+        "        content=\"What are the potential applications of large language models in healthcare?\"\n",
+        "    ),\n",
+        "]\n",
+        "\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 33, 'total_tokens': 41}, 'model': 'yi-large'}, id='run-daa3bc58-8289-4d72-a24e-80622fa90d6d-0')"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
--- a/docs/docs/integrations/document_loaders/box.ipynb
+++ b/docs/docs/integrations/document_loaders/box.ipynb
@@ -1,466 +1,464 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Box\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BoxLoader and BoxBlobLoader\n",
-    "\n",
-    "\n",
-    "The `langchain-box` package provides two methods to index your files from Box: `BoxLoader` and `BoxBlobLoader`. `BoxLoader` allows you to ingest text representations of files that have a text representation in Box. The `BoxBlobLoader` allows you download the blob for any document or image file for processing with the blob parser of your choice.\n",
-    "\n",
-    "This notebook details getting started with both of these. For detailed documentation of all BoxLoader features and configurations head to the API Reference pages for [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) and [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html).\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "The `BoxLoader` class helps you get your unstructured content from Box in Langchain's `Document` format. You can do this with either a `List[str]` containing Box file IDs, or with a `str` containing a Box folder ID. \n",
-    "\n",
-    "The `BoxBlobLoader` class helps you get your unstructured content from Box in Langchain's `Blob` format. You can do this with a `List[str]` containing Box file IDs, a `str` containing a Box folder ID, a search query, or a `BoxMetadataQuery`. \n",
-    "\n",
-    "If getting files from a folder with folder ID, you can also set a `Bool` to tell the loader to get all sub-folders in that folder, as well. \n",
-    "\n",
-    ":::info\n",
-    "A Box instance can contain Petabytes of files, and folders can contain millions of files. Be intentional when choosing what folders you choose to index. And we recommend never getting all files from folder 0 recursively. Folder ID 0 is your root folder.\n",
-    ":::\n",
-    "\n",
-    "The `BoxLoader` will skip files without a text representation, while the `BoxBlobLoader` will return blobs for all document and image files.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ | \n",
-    "| [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| BoxLoader | ✅ | ❌ | \n",
-    "| BoxBlobLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "In order to use the Box package, you will need a few things:\n",
-    "\n",
-    "* A Box account — If you are not a current Box customer or want to test outside of your production Box instance, you can use a [free developer account](https://account.box.com/signup/n/developer#ty9l3).\n",
-    "* [A Box app](https://developer.box.com/guides/getting-started/first-application/) — This is configured in the [developer console](https://account.box.com/developers/console), and for Box AI, must have the `Manage AI` scope enabled. Here you will also select your authentication method\n",
-    "* The app must be [enabled by the administrator](https://developer.box.com/guides/authorization/custom-app-approval/#manual-approval). For free developer accounts, this is whomever signed up for the account.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "For these examples, we will use [token authentication](https://developer.box.com/guides/authentication/tokens/developer-tokens). This can be used with any [authentication method](https://developer.box.com/guides/authentication/). Just get the token with whatever methodology. If you want to learn more about how to use other authentication types with `langchain-box`, visit the [Box provider](/docs/integrations/providers/box) document.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Enter your Box Developer Token:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "box_developer_token = getpass.getpass(\"Enter your Box Developer Token: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_box**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_box"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "### Load files\n",
-    "\n",
-    "If you wish to load files, you must provide the `List` of file ids at instantiation time. \n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **box_file_ids** (`List[str]`)- A list of Box file IDs.\n",
-    "\n",
-    "#### BoxLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.document_loaders import BoxLoader\n",
-    "\n",
-    "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
-    "\n",
-    "loader = BoxLoader(\n",
-    "    box_developer_token=box_developer_token,\n",
-    "    box_file_ids=box_file_ids,\n",
-    "    character_limit=10000,  # Optional. Defaults to no limit\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### BoxBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "\n",
-    "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
-    "\n",
-    "loader = BoxBlobLoader(\n",
-    "    box_developer_token=box_developer_token, box_file_ids=box_file_ids\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load from folder\n",
-    "\n",
-    "If you wish to load files from a folder, you must provide a `str` with the Box folder ID at instantiation time. \n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **box_folder_id** (`str`)- A string containing a Box folder ID.\n",
-    "\n",
-    "#### BoxLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.document_loaders import BoxLoader\n",
-    "\n",
-    "box_folder_id = \"260932470532\"\n",
-    "\n",
-    "loader = BoxLoader(\n",
-    "    box_folder_id=box_folder_id,\n",
-    "    recursive=False,  # Optional. return entire tree, defaults to False\n",
-    "    character_limit=10000,  # Optional. Defaults to no limit\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### BoxBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "\n",
-    "box_folder_id = \"260932470532\"\n",
-    "\n",
-    "loader = BoxBlobLoader(\n",
-    "    box_folder_id=box_folder_id,\n",
-    "    recursive=False,  # Optional. return entire tree, defaults to False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Search for files with BoxBlobLoader\n",
-    "\n",
-    "If you need to search for files, the `BoxBlobLoader` offers two methods. First you can perform a full text search with optional search options to narrow down that search.\n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **query** (`str`)- A string containing the search query to perform.\n",
-    "\n",
-    "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
-    "* **box_search_options** (`BoxSearchOptions`)\n",
-    "\n",
-    "#### BoxBlobLoader search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "from langchain_box.utilities import BoxSearchOptions, DocumentFiles, SearchTypeFilter\n",
-    "\n",
-    "box_folder_id = \"260932470532\"\n",
-    "\n",
-    "box_search_options = BoxSearchOptions(\n",
-    "    ancestor_folder_ids=[box_folder_id],\n",
-    "    search_type_filter=[SearchTypeFilter.FILE_CONTENT],\n",
-    "    created_date_range=[\"2023-01-01T00:00:00-07:00\", \"2024-08-01T00:00:00-07:00,\"],\n",
-    "    file_extensions=[DocumentFiles.DOCX, DocumentFiles.PDF],\n",
-    "    k=200,\n",
-    "    size_range=[1, 1000000],\n",
-    "    updated_data_range=None,\n",
-    ")\n",
-    "\n",
-    "loader = BoxBlobLoader(\n",
-    "    box_developer_token=box_developer_token,\n",
-    "    query=\"Victor\",\n",
-    "    box_search_options=box_search_options,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also search for content based on Box Metadata. If your Box instance uses Metadata, you can search for any documents that have a specific Metadata Template attached that meet a certain criteria, like returning any invoices with a total greater than or equal to $500 that were created last quarter.\n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **query** (`str`)- A string containing the search query to perform.\n",
-    "\n",
-    "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
-    "* **box_search_options** (`BoxSearchOptions`)\n",
-    "\n",
-    "#### BoxBlobLoader Metadata query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "from langchain_box.utilities import BoxMetadataQuery\n",
-    "\n",
-    "query = BoxMetadataQuery(\n",
-    "    template_key=\"enterprise_1234.myTemplate\",\n",
-    "    query=\"total >= :value\",\n",
-    "    query_params={\"value\": 100},\n",
-    "    ancestor_folder_id=\"260932470532\",\n",
-    ")\n",
-    "\n",
-    "loader = BoxBlobLoader(box_metadata_query=query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load\n",
-    "\n",
-    "#### BoxLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Box\n",
+        "---"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### BoxBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# BoxLoader and BoxBlobLoader\n",
+        "\n",
+        "\n",
+        "The `langchain-box` package provides two methods to index your files from Box: `BoxLoader` and `BoxBlobLoader`. `BoxLoader` allows you to ingest text representations of files that have a text representation in Box. The `BoxBlobLoader` allows you download the blob for any document or image file for processing with the blob parser of your choice.\n",
+        "\n",
+        "This notebook details getting started with both of these. For detailed documentation of all BoxLoader features and configurations head to the API Reference pages for [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) and [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html).\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "The `BoxLoader` class helps you get your unstructured content from Box in Langchain's `Document` format. You can do this with either a `List[str]` containing Box file IDs, or with a `str` containing a Box folder ID.\n",
+        "\n",
+        "The `BoxBlobLoader` class helps you get your unstructured content from Box in Langchain's `Blob` format. You can do this with a `List[str]` containing Box file IDs, a `str` containing a Box folder ID, a search query, or a `BoxMetadataQuery`.\n",
+        "\n",
+        "If getting files from a folder with folder ID, you can also set a `Bool` to tell the loader to get all sub-folders in that folder, as well.\n",
+        "\n",
+        ":::info\n",
+        "A Box instance can contain Petabytes of files, and folders can contain millions of files. Be intentional when choosing what folders you choose to index. And we recommend never getting all files from folder 0 recursively. Folder ID 0 is your root folder.\n",
+        ":::\n",
+        "\n",
+        "The `BoxLoader` will skip files without a text representation, while the `BoxBlobLoader` will return blobs for all document and image files.\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ |\n",
+        "| [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ |\n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Async Support\n",
+        "| :---: | :---: | :---: |\n",
+        "| BoxLoader | ✅ | ❌ |\n",
+        "| BoxBlobLoader | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "In order to use the Box package, you will need a few things:\n",
+        "\n",
+        "* A Box account — If you are not a current Box customer or want to test outside of your production Box instance, you can use a [free developer account](https://account.box.com/signup/n/developer#ty9l3).\n",
+        "* [A Box app](https://developer.box.com/guides/getting-started/first-application/) — This is configured in the [developer console](https://account.box.com/developers/console), and for Box AI, must have the `Manage AI` scope enabled. Here you will also select your authentication method\n",
+        "* The app must be [enabled by the administrator](https://developer.box.com/guides/authorization/custom-app-approval/#manual-approval). For free developer accounts, this is whomever signed up for the account.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "For these examples, we will use [token authentication](https://developer.box.com/guides/authentication/tokens/developer-tokens). This can be used with any [authentication method](https://developer.box.com/guides/authentication/). Just get the token with whatever methodology. If you want to learn more about how to use other authentication types with `langchain-box`, visit the [Box provider](/docs/integrations/providers/box) document.\n"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Blob(id='1514555423624' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt', 'name': 'Invoice-A5555.txt', 'file_size': 150} data=\"b'Vendor: AstroTech Solutions\\\\nInvoice Number: A5555\\\\n\\\\nLine Items:\\\\n    - Gravitational Wave Detector Kit: $800\\\\n    - Exoplanet Terrarium: $120\\\\nTotal: $920'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt')\n",
-      "Blob(id='1514553902288' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt', 'name': 'Invoice-B1234.txt', 'file_size': 168} data=\"b'Vendor: Galactic Gizmos Inc.\\\\nInvoice Number: B1234\\\\nPurchase Order Number: 001\\\\nLine Items:\\\\n    - Quantum Flux Capacitor: $500\\\\n    - Anti-Gravity Pen Set: $75\\\\nTotal: $575'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt')\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdin",
+          "output_type": "stream",
+          "text": [
+            "Enter your Box Developer Token:  ········\n"
+          ]
+        }
+      ],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "box_developer_token = getpass.getpass(\"Enter your Box Developer Token: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_box**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_box"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "### Load files\n",
+        "\n",
+        "If you wish to load files, you must provide the `List` of file ids at instantiation time.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **box_file_ids** (`List[str]`)- A list of Box file IDs.\n",
+        "\n",
+        "#### BoxLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.document_loaders import BoxLoader\n",
+        "\n",
+        "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
+        "\n",
+        "loader = BoxLoader(\n",
+        "    box_developer_token=box_developer_token,\n",
+        "    box_file_ids=box_file_ids,\n",
+        "    character_limit=10000,  # Optional. Defaults to no limit\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### BoxBlobLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "\n",
+        "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
+        "\n",
+        "loader = BoxBlobLoader(\n",
+        "    box_developer_token=box_developer_token, box_file_ids=box_file_ids\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load from folder\n",
+        "\n",
+        "If you wish to load files from a folder, you must provide a `str` with the Box folder ID at instantiation time.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **box_folder_id** (`str`)- A string containing a Box folder ID.\n",
+        "\n",
+        "#### BoxLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.document_loaders import BoxLoader\n",
+        "\n",
+        "box_folder_id = \"260932470532\"\n",
+        "\n",
+        "loader = BoxLoader(\n",
+        "    box_folder_id=box_folder_id,\n",
+        "    recursive=False,  # Optional. return entire tree, defaults to False\n",
+        "    character_limit=10000,  # Optional. Defaults to no limit\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### BoxBlobLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "\n",
+        "box_folder_id = \"260932470532\"\n",
+        "\n",
+        "loader = BoxBlobLoader(\n",
+        "    box_folder_id=box_folder_id,\n",
+        "    recursive=False,  # Optional. return entire tree, defaults to False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Search for files with BoxBlobLoader\n",
+        "\n",
+        "If you need to search for files, the `BoxBlobLoader` offers two methods. First you can perform a full text search with optional search options to narrow down that search.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **query** (`str`)- A string containing the search query to perform.\n",
+        "\n",
+        "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
+        "* **box_search_options** (`BoxSearchOptions`)\n",
+        "\n",
+        "#### BoxBlobLoader search"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "from langchain_box.utilities import BoxSearchOptions, DocumentFiles, SearchTypeFilter\n",
+        "\n",
+        "box_folder_id = \"260932470532\"\n",
+        "\n",
+        "box_search_options = BoxSearchOptions(\n",
+        "    ancestor_folder_ids=[box_folder_id],\n",
+        "    search_type_filter=[SearchTypeFilter.FILE_CONTENT],\n",
+        "    created_date_range=[\"2023-01-01T00:00:00-07:00\", \"2024-08-01T00:00:00-07:00,\"],\n",
+        "    file_extensions=[DocumentFiles.DOCX, DocumentFiles.PDF],\n",
+        "    k=200,\n",
+        "    size_range=[1, 1000000],\n",
+        "    updated_data_range=None,\n",
+        ")\n",
+        "\n",
+        "loader = BoxBlobLoader(\n",
+        "    box_developer_token=box_developer_token,\n",
+        "    query=\"Victor\",\n",
+        "    box_search_options=box_search_options,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also search for content based on Box Metadata. If your Box instance uses Metadata, you can search for any documents that have a specific Metadata Template attached that meet a certain criteria, like returning any invoices with a total greater than or equal to $500 that were created last quarter.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **query** (`str`)- A string containing the search query to perform.\n",
+        "\n",
+        "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
+        "* **box_search_options** (`BoxSearchOptions`)\n",
+        "\n",
+        "#### BoxBlobLoader Metadata query"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "from langchain_box.utilities import BoxMetadataQuery\n",
+        "\n",
+        "query = BoxMetadataQuery(\n",
+        "    template_key=\"enterprise_1234.myTemplate\",\n",
+        "    query=\"total >= :value\",\n",
+        "    query_params={\"value\": 100},\n",
+        "    ancestor_folder_id=\"260932470532\",\n",
+        ")\n",
+        "\n",
+        "loader = BoxBlobLoader(box_metadata_query=query)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load\n",
+        "\n",
+        "#### BoxLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### BoxBlobLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Blob(id='1514555423624' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt', 'name': 'Invoice-A5555.txt', 'file_size': 150} data=\"b'Vendor: AstroTech Solutions\\\\nInvoice Number: A5555\\\\n\\\\nLine Items:\\\\n    - Gravitational Wave Detector Kit: $800\\\\n    - Exoplanet Terrarium: $120\\\\nTotal: $920'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt')\n",
+            "Blob(id='1514553902288' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt', 'name': 'Invoice-B1234.txt', 'file_size': 168} data=\"b'Vendor: Galactic Gizmos Inc.\\\\nInvoice Number: B1234\\\\nPurchase Order Number: 001\\\\nLine Items:\\\\n    - Quantum Flux Capacitor: $500\\\\n    - Anti-Gravity Pen Set: $75\\\\nTotal: $575'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt')\n"
+          ]
+        }
+      ],
+      "source": [
+        "for blob in loader.yield_blobs():\n",
+        "    print(f\"Blob({blob})\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load\n",
+        "\n",
+        "#### BoxLoader only"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extra fields\n",
+        "\n",
+        "All Box connectors offer the ability to select additional fields from the Box `FileFull` object to return as custom LangChain metadata. Each object accepts an optional `List[str]` called `extra_fields` containing the json key from the return object, like `extra_fields=[\"shared_link\"]`.\n",
+        "\n",
+        "The connector will add this field to the list of fields the integration needs to function and then add the results to the metadata returned in the `Document` or `Blob`, like `\"metadata\" : { \"source\" : \"source, \"shared_link\" : \"shared_link\" }`. If the field is unavailable for that file, it will be returned as an empty string, like `\"shared_link\" : \"\"`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html)\n",
+        "\n",
+        "\n",
+        "## Help\n",
+        "\n",
+        "If you have questions, you can check out our [developer documentation](https://developer.box.com) or reach out to use in our [developer community](https://community.box.com)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.6"
    }
-   ],
-   "source": [
-    "for blob in loader.yield_blobs():\n",
-    "    print(f\"Blob({blob})\")"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load\n",
-    "\n",
-    "#### BoxLoader only"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extra fields\n",
-    "\n",
-    "All Box connectors offer the ability to select additional fields from the Box `FileFull` object to return as custom LangChain metadata. Each object accepts an optional `List[str]` called `extra_fields` containing the json key from the return object, like `extra_fields=[\"shared_link\"]`. \n",
-    "\n",
-    "The connector will add this field to the list of fields the integration needs to function and then add the results to the metadata returned in the `Document` or `Blob`, like `\"metadata\" : { \"source\" : \"source, \"shared_link\" : \"shared_link\" }`. If the field is unavailable for that file, it will be returned as an empty string, like `\"shared_link\" : \"\"`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html)\n",
-    "\n",
-    "\n",
-    "## Help\n",
-    "\n",
-    "If you have questions, you can check out our [developer documentation](https://developer.box.com) or reach out to use in our [developer community](https://community.box.com)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
--- a/docs/docs/integrations/document_loaders/bshtml.ipynb
+++ b/docs/docs/integrations/document_loaders/bshtml.ipynb
@@ -1,243 +1,241 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BSHTMLLoader\n",
-    "\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [BSHTMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| BSHTMLLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access BSHTMLLoader document loader you'll need to install the `langchain-community` integration package and the `bs4` python package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use the `BSHTMLLoader` class."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community** and **bs4**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community bs4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import BSHTMLLoader\n",
-    "\n",
-    "loader = BSHTMLLoader(\n",
-    "    file_path=\"./example_data/fake-content.html\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# BSHTMLLoader\n",
+        "\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
+        "\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [BSHTMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| BSHTMLLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access BSHTMLLoader document loader you'll need to install the `langchain-community` integration package and the `bs4` python package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use the `BSHTMLLoader` class."
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []\n",
-    "page[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding separator to BS4\n",
-    "\n",
-    "We can also pass a separator to use when calling get_text on the soup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='\n",
-      ", Test Title, \n",
-      ", \n",
-      ", \n",
-      ", My First Heading, \n",
-      ", My first paragraph., \n",
-      ", \n",
-      ", \n",
-      "' metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community** and **bs4**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community bs4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import BSHTMLLoader\n",
+        "\n",
+        "loader = BSHTMLLoader(\n",
+        "    file_path=\"./example_data/fake-content.html\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []\n",
+        "page[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Adding separator to BS4\n",
+        "\n",
+        "We can also pass a separator to use when calling get_text on the soup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='\n",
+            ", Test Title, \n",
+            ", \n",
+            ", \n",
+            ", My First Heading, \n",
+            ", My first paragraph., \n",
+            ", \n",
+            ", \n",
+            "' metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = BSHTMLLoader(\n",
+        "    file_path=\"./example_data/fake-content.html\", get_text_separator=\", \"\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
    }
-   ],
-   "source": [
-    "loader = BSHTMLLoader(\n",
-    "    file_path=\"./example_data/fake-content.html\", get_text_separator=\", \"\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/docs/docs/integrations/document_loaders/json.ipynb
+++ b/docs/docs/integrations/document_loaders/json.ipynb
@@ -1,348 +1,346 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# JSONLoader\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/json/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| JSONLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access JSON document loader you'll need to install the `langchain-community` integration package as well as the ``jq`` python package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are required to use the `JSONLoader` class."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community** and **jq**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community jq "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import JSONLoader\n",
-    "\n",
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat.json\",\n",
-    "    jq_schema=\".messages[].content\",\n",
-    "    text_content=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}, page_content='Bye!')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# JSONLoader\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/json/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| JSONLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access JSON document loader you'll need to install the `langchain-community` integration package as well as the ``jq`` python package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are required to use the `JSONLoader` class."
      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pages = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    pages.append(doc)\n",
-    "    if len(pages) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(pages)\n",
-    "\n",
-    "        pages = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read from JSON Lines file\n",
-    "\n",
-    "If you want to load documents from a JSON Lines file, you pass `json_lines=True`\n",
-    "and specify `jq_schema` to extract `page_content` from a single JSON object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
-    "    jq_schema=\".content\",\n",
-    "    text_content=False,\n",
-    "    json_lines=True,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read specific content keys\n",
-    "\n",
-    "Another option is to set `jq_schema='.'` and provide a `content_key` in order to only load specific content:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='User 2' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
-    "    jq_schema=\".\",\n",
-    "    content_key=\"sender_name\",\n",
-    "    json_lines=True,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JSON file with jq schema `content_key`\n",
-    "\n",
-    "To load documents from a JSON file using the `content_key` within the jq schema, set `is_content_key_jq_parsable=True`. Ensure that `content_key` is compatible and can be parsed using the jq schema."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community** and **jq**:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat.json\",\n",
-    "    jq_schema=\".messages[]\",\n",
-    "    content_key=\".content\",\n",
-    "    is_content_key_jq_parsable=True,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting metadata\n",
-    "\n",
-    "Generally, we want to include metadata available in the JSON file into the documents that we create from the content.\n",
-    "\n",
-    "The following demonstrates how metadata can be extracted using the `JSONLoader`.\n",
-    "\n",
-    "There are some key changes to be noted. In the previous example where we didn't collect the metadata, we managed to directly specify in the schema where the value for the `page_content` can be extracted from.\n",
-    "\n",
-    "In this example, we have to tell the loader to iterate over the records in the `messages` field. The jq_schema then has to be `.messages[]`\n",
-    "\n",
-    "This allows us to pass the records (dict) into the `metadata_func` that has to be implemented. The `metadata_func` is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final `Document` object.\n",
-    "\n",
-    "Additionally, we now have to explicitly specify in the loader, via the `content_key` argument, the key from the record where the value for the `page_content` needs to be extracted from."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community jq "
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import JSONLoader\n",
+        "\n",
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat.json\",\n",
+        "    jq_schema=\".messages[].content\",\n",
+        "    text_content=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}, page_content='Bye!')"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pages = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    pages.append(doc)\n",
+        "    if len(pages) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(pages)\n",
+        "\n",
+        "        pages = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Read from JSON Lines file\n",
+        "\n",
+        "If you want to load documents from a JSON Lines file, you pass `json_lines=True`\n",
+        "and specify `jq_schema` to extract `page_content` from a single JSON object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
+        "    jq_schema=\".content\",\n",
+        "    text_content=False,\n",
+        "    json_lines=True,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Read specific content keys\n",
+        "\n",
+        "Another option is to set `jq_schema='.'` and provide a `content_key` in order to only load specific content:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='User 2' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
+        "    jq_schema=\".\",\n",
+        "    content_key=\"sender_name\",\n",
+        "    json_lines=True,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## JSON file with jq schema `content_key`\n",
+        "\n",
+        "To load documents from a JSON file using the `content_key` within the jq schema, set `is_content_key_jq_parsable=True`. Ensure that `content_key` is compatible and can be parsed using the jq schema."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat.json\",\n",
+        "    jq_schema=\".messages[]\",\n",
+        "    content_key=\".content\",\n",
+        "    is_content_key_jq_parsable=True,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extracting metadata\n",
+        "\n",
+        "Generally, we want to include metadata available in the JSON file into the documents that we create from the content.\n",
+        "\n",
+        "The following demonstrates how metadata can be extracted using the `JSONLoader`.\n",
+        "\n",
+        "There are some key changes to be noted. In the previous example where we didn't collect the metadata, we managed to directly specify in the schema where the value for the `page_content` can be extracted from.\n",
+        "\n",
+        "In this example, we have to tell the loader to iterate over the records in the `messages` field. The jq_schema then has to be `.messages[]`\n",
+        "\n",
+        "This allows us to pass the records (dict) into the `metadata_func` that has to be implemented. The `metadata_func` is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final `Document` object.\n",
+        "\n",
+        "Additionally, we now have to explicitly specify in the loader, via the `content_key` argument, the key from the record where the value for the `page_content` needs to be extracted from."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define the metadata extraction function.\n",
+        "def metadata_func(record: dict, metadata: dict) -> dict:\n",
+        "    metadata[\"sender_name\"] = record.get(\"sender_name\")\n",
+        "    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")\n",
+        "\n",
+        "    return metadata\n",
+        "\n",
+        "\n",
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat.json\",\n",
+        "    jq_schema=\".messages[]\",\n",
+        "    content_key=\"content\",\n",
+        "    metadata_func=metadata_func,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
    }
-   ],
-   "source": [
-    "# Define the metadata extraction function.\n",
-    "def metadata_func(record: dict, metadata: dict) -> dict:\n",
-    "    metadata[\"sender_name\"] = record.get(\"sender_name\")\n",
-    "    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")\n",
-    "\n",
-    "    return metadata\n",
-    "\n",
-    "\n",
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat.json\",\n",
-    "    jq_schema=\".messages[]\",\n",
-    "    content_key=\"content\",\n",
-    "    metadata_func=metadata_func,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0].metadata)"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/docs/docs/integrations/document_loaders/mathpix.ipynb
+++ b/docs/docs/integrations/document_loaders/mathpix.ipynb
@@ -1,178 +1,176 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MathPixPDFLoader\n",
-    "\n",
-    "Inspired by Daniel Gross's snippet here: [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [MathPixPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| MathPixPDFLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Sign up for Mathpix and [create an API key](https://mathpix.com/docs/ocr/creating-an-api-key) to set the `MATHPIX_API_KEY` variables in your environment"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MathPixPDFLoader\n",
+        "\n",
+        "Inspired by Daniel Gross's snippet here: [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [MathPixPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| MathPixPDFLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Sign up for Mathpix and [create an API key](https://mathpix.com/docs/ocr/creating-an-api-key) to set the `MATHPIX_API_KEY` variables in your environment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"MATHPIX_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"MATHPIX_API_KEY\"] = getpass.getpass(\"Enter your Mathpix API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we are ready to initialize our loader:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import MathpixPDFLoader\n",
+        "\n",
+        "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+        "loader = MathpixPDFLoader(file_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"MATHPIX_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"MATHPIX_API_KEY\"] = getpass.getpass(\"Enter your Mathpix API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we are ready to initialize our loader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import MathpixPDFLoader\n",
-    "\n",
-    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
-    "loader = MathpixPDFLoader(file_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/docs/docs/integrations/document_loaders/pdfminer.ipynb
+++ b/docs/docs/integrations/document_loaders/pdfminer.ipynb
@@ -31,7 +31,7 @@
    "\n",
    "### Credentials\n",
    "\n",
-    "No credentials are required to use PyMuPDFLoader"
+    "No credentials are required to use PDFMinerLoader"
   ]
  },
  {
@@ -60,7 +60,7 @@
   "source": [
    "### Installation\n",
    "\n",
-    "Install **langchain_community** and **pymupdf**."
+    "Install **langchain_community** and **pdfminer**."
   ]
  },
  {
@@ -327,7 +327,7 @@
    "- By page\n",
    "- As a single text flow\n",
    "\n",
-    "By default PDFPlumberLoader will split the PDF by page."
+    "By default PDFMinerLoader will split the PDF by page."
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/pdfplumber.ipynb
+++ b/docs/docs/integrations/document_loaders/pdfplumber.ipynb
@@ -1,183 +1,181 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PDFPlumber\n",
-    "\n",
-    "Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PDFPlumberLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| PDFPlumberLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use this loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import PDFPlumberLoader\n",
-    "\n",
-    "loader = PDFPlumberLoader(\"./example_data/layout-parser-paper.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,\\n1202\\nnuJ\\n12\\n]VC.sc[\\n2v84351.3012:viXra\\n')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# PDFPlumber\n",
+        "\n",
+        "Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [PDFPlumberLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| PDFPlumberLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use this loader."
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import PDFPlumberLoader\n",
+        "\n",
+        "loader = PDFPlumberLoader(\"./example_data/layout-parser-paper.pdf\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,\\n1202\\nnuJ\\n12\\n]VC.sc[\\n2v84351.3012:viXra\\n')"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/docs/docs/integrations/document_loaders/pymupdf.ipynb
+++ b/docs/docs/integrations/document_loaders/pymupdf.ipynb
@@ -289,7 +289,7 @@
    "- By page\n",
    "- As a single text flow\n",
    "\n",
-    "By default PDFPlumberLoader will split the PDF by page."
+    "By default PyMuPDFLoader will split the PDF by page."
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/pymupdf4llm.ipynb
+++ b/docs/docs/integrations/document_loaders/pymupdf4llm.ipynb
--- a/docs/docs/integrations/document_loaders/pypdfdirectory.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfdirectory.ipynb
@@ -1,230 +1,228 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PyPDFDirectoryLoader\n",
-    "\n",
-    "This loader loads all PDF files from a specific directory.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PyPDFDirectoryLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| PyPDFDirectoryLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed for this loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:08.878423Z",
-     "start_time": "2025-01-21T08:00:08.876042Z"
-    }
-   },
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:12.003718Z",
-     "start_time": "2025-01-21T08:00:10.291617Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "%pip install -qU langchain_community pypdf pillow",
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "execution_count": 2
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:18.512061Z",
-     "start_time": "2025-01-21T08:00:17.313969Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
-    "\n",
-    "directory_path = (\n",
-    "    \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
-    ")\n",
-    "loader = PyPDFDirectoryLoader(\"example_data/\")"
-   ],
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:23.549752Z",
-     "start_time": "2025-01-21T08:00:23.129010Z"
-    }
-   },
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Uniﬁed Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model conﬁgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\neﬀorts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classiﬁcation [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# PyPDFDirectoryLoader\n",
+        "\n",
+        "This loader loads all PDF files from a specific directory.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [PyPDFDirectoryLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| PyPDFDirectoryLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed for this loader."
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 4
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:26.612346Z",
-     "start_time": "2025-01-21T08:00:26.609051Z"
-    }
-   },
-   "source": [
-    "print(docs[0].metadata)"
-   ],
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:08.878423Z",
+          "start_time": "2025-01-21T08:00:08.876042Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:12.003718Z",
+          "start_time": "2025-01-21T08:00:10.291617Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": "%pip install -qU langchain_community pypdf pillow"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:18.512061Z",
+          "start_time": "2025-01-21T08:00:17.313969Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
+        "\n",
+        "directory_path = (\n",
+        "    \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
+        ")\n",
+        "loader = PyPDFDirectoryLoader(\"example_data/\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:23.549752Z",
+          "start_time": "2025-01-21T08:00:23.129010Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Uniﬁed Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model conﬁgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\neﬀorts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classiﬁcation [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:26.612346Z",
+          "start_time": "2025-01-21T08:00:26.609051Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:30.251598Z",
+          "start_time": "2025-01-21T08:00:29.972141Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ""
    }
-   ],
-   "execution_count": 5
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:30.251598Z",
-     "start_time": "2025-01-21T08:00:29.972141Z"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
    }
-   },
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ],
-   "outputs": [],
-   "execution_count": 6
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": ""
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/docs/docs/integrations/document_loaders/pypdfium2.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfium2.ipynb
--- a/docs/docs/integrations/document_loaders/pypdfloader.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfloader.ipynb
--- a/docs/docs/integrations/document_loaders/sitemap.ipynb
+++ b/docs/docs/integrations/document_loaders/sitemap.ipynb
@@ -1,380 +1,378 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sitemap\n",
-    "\n",
-    "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrapes and loads all pages in the sitemap, returning each page as a Document.\n",
-    "\n",
-    "The scraping is done concurrently. There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load you can increase this limit. Note, while this will speed up the scraping process, it may cause the server to block you. Be careful!\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [SiteMapLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| SiteMapLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access SiteMap document loader you'll need to install the `langchain-community` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to run this."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fix notebook asyncio bug"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.sitemap import SitemapLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 28/28 [00:04<00:00,  6.83it/s]\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Sitemap\n",
+        "\n",
+        "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrapes and loads all pages in the sitemap, returning each page as a Document.\n",
+        "\n",
+        "The scraping is done concurrently. There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load you can increase this limit. Note, while this will speed up the scraping process, it may cause the server to block you. Be careful!\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [SiteMapLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| SiteMapLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access SiteMap document loader you'll need to install the `langchain-community` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to run this."
+      ]
    },
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}, page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = sitemap_loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can change the `requests_per_second` parameter to increase the max concurrent requests. and use `requests_kwargs` to pass kwargs when send requests."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader.requests_per_second = 2\n",
-    "# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issue\n",
-    "sitemap_loader.requests_kwargs = {\"verify\": False}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load\n",
-    "\n",
-    "You can also load the pages lazily in order to minimize the memory load."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 28/28 [00:01<00:00, 19.06it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in sitemap_loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Filtering sitemap URLs\n",
-    "\n",
-    "Sitemaps can be massive files, with thousands of URLs.  Often you don't need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the `filter_urls` parameter.  Only URLs that match one of the patterns will be loaded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = SitemapLoader(\n",
-    "    web_path=\"https://api.python.langchain.com/sitemap.xml\",\n",
-    "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
-    ")\n",
-    "documents = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Fix notebook asyncio bug"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import nest_asyncio\n",
+        "\n",
+        "nest_asyncio.apply()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders.sitemap import SitemapLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Fetching pages: 100%|##########| 28/28 [00:04<00:00,  6.83it/s]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}, page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n')"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = sitemap_loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can change the `requests_per_second` parameter to increase the max concurrent requests. and use `requests_kwargs` to pass kwargs when send requests."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sitemap_loader.requests_per_second = 2\n",
+        "# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issue\n",
+        "sitemap_loader.requests_kwargs = {\"verify\": False}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load\n",
+        "\n",
+        "You can also load the pages lazily in order to minimize the memory load."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Fetching pages: 100%|##########| 28/28 [00:01<00:00, 19.06it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "page = []\n",
+        "for doc in sitemap_loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Filtering sitemap URLs\n",
+        "\n",
+        "Sitemaps can be massive files, with thousands of URLs.  Often you don't need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the `filter_urls` parameter.  Only URLs that match one of the patterns will be loaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "loader = SitemapLoader(\n",
+        "    web_path=\"https://api.python.langchain.com/sitemap.xml\",\n",
+        "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
+        ")\n",
+        "documents = loader.load()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "scrolled": true
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "documents[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Add custom scraping rules\n",
+        "\n",
+        "The `SitemapLoader` uses `beautifulsoup4` for the scraping process, and it scrapes every element on the page by default. The `SitemapLoader` constructor accepts a custom scraping function. This feature can be helpful to tailor the scraping process to your specific needs; for example, you might want to avoid scraping headers or navigation elements.\n",
+        "\n",
+        " The following example shows how to develop and use a custom function to avoid navigation and header elements."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Import the `beautifulsoup4` library and define the custom function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pip install beautifulsoup4"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from bs4 import BeautifulSoup\n",
+        "\n",
+        "\n",
+        "def remove_nav_and_header_elements(content: BeautifulSoup) -> str:\n",
+        "    # Find all 'nav' and 'header' elements in the BeautifulSoup object\n",
+        "    nav_elements = content.find_all(\"nav\")\n",
+        "    header_elements = content.find_all(\"header\")\n",
+        "\n",
+        "    # Remove each 'nav' and 'header' element from the BeautifulSoup object\n",
+        "    for element in nav_elements + header_elements:\n",
+        "        element.decompose()\n",
+        "\n",
+        "    return str(content.get_text())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Add your custom function to the `SitemapLoader` object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "loader = SitemapLoader(\n",
+        "    \"https://api.python.langchain.com/sitemap.xml\",\n",
+        "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
+        "    parsing_function=remove_nav_and_header_elements,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Local Sitemap\n",
+        "\n",
+        "The sitemap loader can also be used to load local files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
+        "\n",
+        "docs = sitemap_loader.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "documents[0]"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add custom scraping rules\n",
-    "\n",
-    "The `SitemapLoader` uses `beautifulsoup4` for the scraping process, and it scrapes every element on the page by default. The `SitemapLoader` constructor accepts a custom scraping function. This feature can be helpful to tailor the scraping process to your specific needs; for example, you might want to avoid scraping headers or navigation elements.\n",
-    "\n",
-    " The following example shows how to develop and use a custom function to avoid navigation and header elements."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import the `beautifulsoup4` library and define the custom function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install beautifulsoup4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bs4 import BeautifulSoup\n",
-    "\n",
-    "\n",
-    "def remove_nav_and_header_elements(content: BeautifulSoup) -> str:\n",
-    "    # Find all 'nav' and 'header' elements in the BeautifulSoup object\n",
-    "    nav_elements = content.find_all(\"nav\")\n",
-    "    header_elements = content.find_all(\"header\")\n",
-    "\n",
-    "    # Remove each 'nav' and 'header' element from the BeautifulSoup object\n",
-    "    for element in nav_elements + header_elements:\n",
-    "        element.decompose()\n",
-    "\n",
-    "    return str(content.get_text())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add your custom function to the `SitemapLoader` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = SitemapLoader(\n",
-    "    \"https://api.python.langchain.com/sitemap.xml\",\n",
-    "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
-    "    parsing_function=remove_nav_and_header_elements,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Local Sitemap\n",
-    "\n",
-    "The sitemap loader can also be used to load local files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
-    "\n",
-    "docs = sitemap_loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
--- a/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
@@ -1,269 +1,267 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# UnstructuredMarkdownLoader\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredMarkdownLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| UnstructuredMarkdownLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access UnstructuredMarkdownLoader document loader you'll need to install the `langchain-community` integration package and the `unstructured` python package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use this loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community** and **unstructured**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community unstructured"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents. \n",
-    "\n",
-    "You can run the loader in one of two modes: \"single\" and \"elements\". If you use \"single\" mode, the document will be returned as a single `Document` object. If you use \"elements\" mode, the unstructured library will split the document into elements such as `Title` and `NarrativeText`. You can pass in additional `unstructured` kwargs after mode to apply different `unstructured` settings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
-    "\n",
-    "loader = UnstructuredMarkdownLoader(\n",
-    "    \"./example_data/example.md\",\n",
-    "    mode=\"single\",\n",
-    "    strategy=\"fast\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/example.md'}, page_content='Sample Markdown Document\\n\\nIntroduction\\n\\nWelcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It\\'s widely used for documentation, readme files, and more.\\n\\nFeatures\\n\\nHeaders\\n\\nMarkdown supports multiple levels of headers:\\n\\nHeader 1: # Header 1\\n\\nHeader 2: ## Header 2\\n\\nHeader 3: ### Header 3\\n\\nLists\\n\\nUnordered List\\n\\nItem 1\\n\\nItem 2\\n\\nSubitem 2.1\\n\\nSubitem 2.2\\n\\nOrdered List\\n\\nFirst item\\n\\nSecond item\\n\\nThird item\\n\\nLinks\\n\\nOpenAI is an AI research organization.\\n\\nImages\\n\\nHere\\'s an example image:\\n\\nCode\\n\\nInline Code\\n\\nUse code for inline code snippets.\\n\\nCode Block\\n\\n```python def greet(name): return f\"Hello, {name}!\"\\n\\nprint(greet(\"World\")) ```')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# UnstructuredMarkdownLoader\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [UnstructuredMarkdownLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| UnstructuredMarkdownLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access UnstructuredMarkdownLoader document loader you'll need to install the `langchain-community` integration package and the `unstructured` python package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use this loader."
      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/example.md'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/example.md', 'link_texts': ['OpenAI'], 'link_urls': ['https://www.openai.com'], 'last_modified': '2024-08-14T15:04:18', 'languages': ['eng'], 'parent_id': 'de1f74bf226224377ab4d8b54f215bb9', 'filetype': 'text/markdown', 'file_directory': './example_data', 'filename': 'example.md', 'category': 'NarrativeText', 'element_id': '898a542a261f7dc65e0072d1e847d535'}, page_content='OpenAI is an AI research organization.')"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []\n",
-    "page[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Elements\n",
-    "\n",
-    "In this example we will load in the `elements` mode, which will return a list of the different elements in the markdown document:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "29"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community** and **unstructured**"
      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
-    "\n",
-    "loader = UnstructuredMarkdownLoader(\n",
-    "    \"./example_data/example.md\",\n",
-    "    mode=\"elements\",\n",
-    "    strategy=\"fast\",\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you see there are 29 elements that were pulled from the `example.md` file. The first element is the title of the document as expected:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "'Sample Markdown Document'"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community unstructured"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents. \n",
+        "\n",
+        "You can run the loader in one of two modes: \"single\" and \"elements\". If you use \"single\" mode, the document will be returned as a single `Document` object. If you use \"elements\" mode, the unstructured library will split the document into elements such as `Title` and `NarrativeText`. You can pass in additional `unstructured` kwargs after mode to apply different `unstructured` settings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
+        "\n",
+        "loader = UnstructuredMarkdownLoader(\n",
+        "    \"./example_data/example.md\",\n",
+        "    mode=\"single\",\n",
+        "    strategy=\"fast\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/example.md'}, page_content='Sample Markdown Document\\n\\nIntroduction\\n\\nWelcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It\\'s widely used for documentation, readme files, and more.\\n\\nFeatures\\n\\nHeaders\\n\\nMarkdown supports multiple levels of headers:\\n\\nHeader 1: # Header 1\\n\\nHeader 2: ## Header 2\\n\\nHeader 3: ### Header 3\\n\\nLists\\n\\nUnordered List\\n\\nItem 1\\n\\nItem 2\\n\\nSubitem 2.1\\n\\nSubitem 2.2\\n\\nOrdered List\\n\\nFirst item\\n\\nSecond item\\n\\nThird item\\n\\nLinks\\n\\nOpenAI is an AI research organization.\\n\\nImages\\n\\nHere\\'s an example image:\\n\\nCode\\n\\nInline Code\\n\\nUse code for inline code snippets.\\n\\nCode Block\\n\\n```python def greet(name): return f\"Hello, {name}!\"\\n\\nprint(greet(\"World\")) ```')"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/example.md'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/example.md', 'link_texts': ['OpenAI'], 'link_urls': ['https://www.openai.com'], 'last_modified': '2024-08-14T15:04:18', 'languages': ['eng'], 'parent_id': 'de1f74bf226224377ab4d8b54f215bb9', 'filetype': 'text/markdown', 'file_directory': './example_data', 'filename': 'example.md', 'category': 'NarrativeText', 'element_id': '898a542a261f7dc65e0072d1e847d535'}, page_content='OpenAI is an AI research organization.')"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []\n",
+        "page[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load Elements\n",
+        "\n",
+        "In this example we will load in the `elements` mode, which will return a list of the different elements in the markdown document:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "29"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
+        "\n",
+        "loader = UnstructuredMarkdownLoader(\n",
+        "    \"./example_data/example.md\",\n",
+        "    mode=\"elements\",\n",
+        "    strategy=\"fast\",\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "len(docs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you see there are 29 elements that were pulled from the `example.md` file. The first element is the title of the document as expected:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'Sample Markdown Document'"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "docs[0].page_content"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }
--- a/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb
--- a/docs/docs/integrations/document_loaders/xml.ipynb
+++ b/docs/docs/integrations/document_loaders/xml.ipynb
@@ -1,202 +1,200 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "72ccbe2b",
-   "metadata": {},
-   "source": [
-    "# UnstructuredXMLLoader\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/document_loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredXMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| UnstructuredXMLLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access UnstructuredXMLLoader document loader you'll need to install the `langchain-community` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use the UnstructuredXMLLoader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc4ba987",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9fa4d5e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38e53f22",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fcd320ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a102f199",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2d198582",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import UnstructuredXMLLoader\n",
-    "\n",
-    "loader = UnstructuredXMLLoader(\n",
-    "    \"./example_data/factbook.xml\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9bbb463c",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cd875e75",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/factbook.xml'}, page_content='United States\\n\\nWashington, DC\\n\\nJoe Biden\\n\\nBaseball\\n\\nCanada\\n\\nOttawa\\n\\nJustin Trudeau\\n\\nHockey\\n\\nFrance\\n\\nParis\\n\\nEmmanuel Macron\\n\\nSoccer\\n\\nTrinidad & Tobado\\n\\nPort of Spain\\n\\nKeith Rowley\\n\\nTrack & Field')"
+      "cell_type": "markdown",
+      "id": "72ccbe2b",
+      "metadata": {},
+      "source": [
+        "# UnstructuredXMLLoader\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/document_loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
+        "\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [UnstructuredXMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| UnstructuredXMLLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access UnstructuredXMLLoader document loader you'll need to install the `langchain-community` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use the UnstructuredXMLLoader"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "79b52cc0",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/factbook.xml'}\n"
-     ]
+      "cell_type": "markdown",
+      "id": "fc4ba987",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9fa4d5e5",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "38e53f22",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fcd320ec",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a102f199",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "2d198582",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import UnstructuredXMLLoader\n",
+        "\n",
+        "loader = UnstructuredXMLLoader(\n",
+        "    \"./example_data/factbook.xml\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9bbb463c",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "cd875e75",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/factbook.xml'}, page_content='United States\\n\\nWashington, DC\\n\\nJoe Biden\\n\\nBaseball\\n\\nCanada\\n\\nOttawa\\n\\nJustin Trudeau\\n\\nHockey\\n\\nFrance\\n\\nParis\\n\\nEmmanuel Macron\\n\\nSoccer\\n\\nTrinidad & Tobado\\n\\nPort of Spain\\n\\nKeith Rowley\\n\\nTrack & Field')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "79b52cc0",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/factbook.xml'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "557608e5",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e3b9e75c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "712aa98f",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "557608e5",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e3b9e75c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "712aa98f",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/document_transformers/rankllm-reranker.ipynb
+++ b/docs/docs/integrations/document_transformers/rankllm-reranker.ipynb
@@ -11,39 +11,41 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "[RankLLM](https://github.com/castorini/rank_llm) offers a suite of listwise rerankers, albeit with focus on open source LLMs finetuned for the task - RankVicuna and RankZephyr being two of them."
+    "**[RankLLM](https://github.com/castorini/rank_llm)** is a **flexible reranking framework** supporting **listwise, pairwise, and pointwise ranking models**. It includes **RankVicuna, RankZephyr, MonoT5, DuoT5, LiT5, and FirstMistral**, with integration for **FastChat, vLLM, SGLang, and TensorRT-LLM** for efficient inference. RankLLM is optimized for **retrieval and ranking tasks**, leveraging both **open-source LLMs** and proprietary rerankers like **RankGPT and RankGemini**. It supports **batched inference, first-token reranking, and retrieval via BM25 and SPLADE**.\n",
+    "\n",
+    "> **Note:** If using the built-in retriever, RankLLM requires **Pyserini, JDK 21, PyTorch, and Faiss** for retrieval functionality."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
-    "%pip install --upgrade --quiet  rank_llm"
+    "%pip install --upgrade --quiet rank_llm"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
-    "%pip install --upgrade --quiet  langchain_openai"
+    "%pip install --upgrade --quiet langchain_openai"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
-    "%pip install --upgrade --quiet  faiss-cpu"
+    "%pip install --upgrade --quiet faiss-cpu"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -56,7 +58,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -64,7 +66,7 @@
    "def pretty_print_docs(docs):\n",
    "    print(\n",
    "        f\"\\n{'-' * 100}\\n\".join(\n",
-    "            [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
+    "            [f\"Document {i + 1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
    "        )\n",
    "    )"
   ]
@@ -79,9 +81,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-02-22 15:28:58,344 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+     ]
+    }
+   ],
   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain_community.vectorstores import FAISS\n",
@@ -114,14 +124,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
-     "name": "stderr",
+     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "2025-02-17 04:37:08,458 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+      "2025-02-22 15:29:00,892 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    },
    {
@@ -331,27 +341,41 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Retrieval + Reranking with RankZephyr"
+    "RankZephyr performs listwise reranking for improved retrieval quality but requires at least 24GB of VRAM to run efficiently."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading shards: 100%|██████████| 3/3 [00:00<00:00, 2674.37it/s]\n",
+      "Loading checkpoint shards: 100%|██████████| 3/3 [01:49<00:00, 36.39s/it]\n"
+     ]
+    }
+   ],
   "source": [
+    "import torch\n",
    "from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n",
    "from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank\n",
    "\n",
-    "compressor = RankLLMRerank(top_n=3, model=\"zephyr\")\n",
+    "torch.cuda.empty_cache()\n",
+    "\n",
+    "compressor = RankLLMRerank(top_n=3, model=\"rank_zephyr\")\n",
    "compression_retriever = ContextualCompressionRetriever(\n",
    "    base_compressor=compressor, base_retriever=retriever\n",
-    ")"
+    ")\n",
+    "\n",
+    "del compressor"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
@@ -386,7 +410,7 @@
     ]
    },
    {
-     "name": "stderr",
+     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
@@ -407,7 +431,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
@@ -432,7 +456,7 @@
    "    llm=ChatOpenAI(temperature=0), retriever=compression_retriever\n",
    ")\n",
    "\n",
-    "chain({\"query\": query})"
+    "chain.invoke({\"query\": query})"
   ]
  },
  {
@@ -451,9 +475,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-02-22 15:01:29,469 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+     ]
+    },
    {
     "name": "stdout",
     "output_type": "stream",
@@ -683,7 +714,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -698,9 +729,18 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-02-22 15:01:38,554 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+      "  0%|          | 0/1 [00:00<?, ?it/s]2025-02-22 15:01:43,704 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "100%|██████████| 1/1 [00:05<00:00,  5.15s/it]"
+     ]
+    },
    {
     "name": "stdout",
     "output_type": "stream",
@@ -727,7 +767,7 @@
     ]
    },
    {
-     "name": "stderr",
+     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
@@ -748,15 +788,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
-     "name": "stderr",
+     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "/tmp/ipykernel_2153001/1437145854.py:10: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
-      "  chain({\"query\": query})\n",
+      "  chain.invoke({\"query\": query})\n",
      "2025-02-17 04:30:00,016 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]2025-02-17 04:30:01,649 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.63s/it]\n",
@@ -785,13 +824,13 @@
    "    llm=ChatOpenAI(temperature=0), retriever=compression_retriever\n",
    ")\n",
    "\n",
-    "chain({\"query\": query})"
+    "chain.invoke({\"query\": query})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "rankllm",
+   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
@@ -805,7 +844,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.2"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/graphs/memgraph.ipynb
+++ b/docs/docs/integrations/graphs/memgraph.ipynb
@@ -38,7 +38,7 @@
    "To use LangChain, install and import all the necessary packages. We'll use the package manager [pip](https://pip.pypa.io/en/stable/installation/), along with the `--user` flag, to ensure proper permissions. If you've installed Python 3.4 or a later version, `pip` is included by default. You can install all the required packages using the following command:\n",
    "\n",
    "```\n",
-    "pip install langchain langchain-openai neo4j --user\n",
+    "pip install langchain langchain-openai langchain-memgraph --user\n",
    "```\n",
    "\n",
    "You can either run the provided code blocks in this notebook or use a separate Python file to experiment with Memgraph and LangChain."
@@ -57,24 +57,22 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
-    "from langchain_community.chains.graph_qa.memgraph import MemgraphQAChain\n",
-    "from langchain_community.graphs import MemgraphGraph\n",
    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_memgraph.chains.graph_qa import MemgraphQAChain\n",
+    "from langchain_memgraph.graphs.memgraph import Memgraph\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "url = os.environ.get(\"MEMGRAPH_URI\", \"bolt://localhost:7687\")\n",
    "username = os.environ.get(\"MEMGRAPH_USERNAME\", \"\")\n",
    "password = os.environ.get(\"MEMGRAPH_PASSWORD\", \"\")\n",
    "\n",
-    "graph = MemgraphGraph(\n",
-    "    url=url, username=username, password=password, refresh_schema=False\n",
-    ")"
+    "graph = Memgraph(url=url, username=username, password=password, refresh_schema=False)"
   ]
  },
  {
--- a/docs/docs/integrations/llms/bittensor.ipynb
+++ b/docs/docs/integrations/llms/bittensor.ipynb
@@ -48,7 +48,7 @@
    "print(f\"Response provided by LLM with system prompt set is : {sys_resp}\")\n",
    "\n",
    "# The top_responses parameter can give multiple responses based on its parameter value\n",
-    "# This below code retrive top 10 miner's response all the response are in format of json\n",
+    "# This below code retrieve top 10 miner's response all the response are in format of json\n",
    "\n",
    "# Json response structure is\n",
    "\"\"\" {\n",
--- a/docs/docs/integrations/llms/openai.ipynb
+++ b/docs/docs/integrations/llms/openai.ipynb
@@ -1,263 +1,261 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9597802c",
-   "metadata": {},
-   "source": [
-    "# OpenAI\n",
-    "\n",
-    ":::caution\n",
-    "You are currently on a page documenting the use of OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
-    "\n",
-    "Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/openai/).\n",
-    ":::\n",
-    "\n",
-    "[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74312161",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
-    "\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to https://platform.openai.com to sign up to OpenAI and generate an API key. Once you've done this set the OPENAI_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "efcdb2b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5d528fa",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "52fa46e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fad78d8",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain OpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e300149",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "129a3275",
-   "metadata": {},
-   "source": [
-    "Should you need to specify your organization ID, you can use the following cell. However, it is not required if you are only part of a single organization or intend to use your default organization. You can check your default organization [here](https://platform.openai.com/account/api-keys).\n",
-    "\n",
-    "To specify your organization, you can use this:\n",
-    "```python\n",
-    "OPENAI_ORGANIZATION = getpass()\n",
-    "\n",
-    "os.environ[\"OPENAI_ORGANIZATION\"] = OPENAI_ORGANIZATION\n",
-    "```\n",
-    "\n",
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_openai import OpenAI\n",
-    "\n",
-    "llm = OpenAI()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "464003c1",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "85b49da0",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'\\n\\nI am an AI and do not have emotions like humans do, so I am always functioning at my optimal level. Thank you for asking! How can I assist you today?'"
+      "cell_type": "markdown",
+      "id": "9597802c",
+      "metadata": {},
+      "source": [
+        "# OpenAI\n",
+        "\n",
+        ":::caution\n",
+        "You are currently on a page documenting the use of OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
+        "\n",
+        "Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/openai/).\n",
+        ":::\n",
+        "\n",
+        "[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n",
+        "\n",
+        "This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm.invoke(\"Hello how are you?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b7e0dfc",
-   "metadata": {},
-   "source": [
-    "## Chaining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "'\\nIch liebe Programmieren.'"
+      "cell_type": "markdown",
+      "id": "74312161",
+      "metadata": {},
+      "source": [
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
+        "\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to https://platform.openai.com to sign up to OpenAI and generate an API key. Once you've done this set the OPENAI_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "efcdb2b6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f5d528fa",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "52fa46e8",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0fad78d8",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain OpenAI integration lives in the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2e300149",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "129a3275",
+      "metadata": {},
+      "source": [
+        "Should you need to specify your organization ID, you can use the following cell. However, it is not required if you are only part of a single organization or intend to use your default organization. You can check your default organization [here](https://platform.openai.com/account/api-keys).\n",
+        "\n",
+        "To specify your organization, you can use this:\n",
+        "```python\n",
+        "OPENAI_ORGANIZATION = getpass()\n",
+        "\n",
+        "os.environ[\"OPENAI_ORGANIZATION\"] = OPENAI_ORGANIZATION\n",
+        "```\n",
+        "\n",
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "6fb585dd",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_openai import OpenAI\n",
+        "\n",
+        "llm = OpenAI()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "464003c1",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "85b49da0",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'\\n\\nI am an AI and do not have emotions like humans do, so I am always functioning at my optimal level. Thank you for asking! How can I assist you today?'"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "llm.invoke(\"Hello how are you?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b7e0dfc",
+      "metadata": {},
+      "source": [
+        "## Chaining"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "a641dbd9",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'\\nIch liebe Programmieren.'"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import PromptTemplate\n",
+        "\n",
+        "prompt = PromptTemplate.from_template(\"How to say {input} in {output_language}:\\n\")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58a9ddb1",
+      "metadata": {},
+      "source": [
+        "## Using a proxy\n",
+        "\n",
+        "If you are behind an explicit proxy, you can specify the http_client to pass through"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "55142cec",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install httpx\n",
+        "\n",
+        "import httpx\n",
+        "\n",
+        "openai = OpenAI(\n",
+        "    model_name=\"gpt-3.5-turbo-instruct\",\n",
+        "    http_client=httpx.Client(proxies=\"http://proxy.yourcompany.com:8080\"),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "id": "2fd99e97-013f-4c28-bb47-426faa42a2cf",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(\"How to say {input} in {output_language}:\\n\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+      }
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "58a9ddb1",
-   "metadata": {},
-   "source": [
-    "## Using a proxy\n",
-    "\n",
-    "If you are behind an explicit proxy, you can specify the http_client to pass through"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55142cec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install httpx\n",
-    "\n",
-    "import httpx\n",
-    "\n",
-    "openai = OpenAI(\n",
-    "    model_name=\"gpt-3.5-turbo-instruct\",\n",
-    "    http_client=httpx.Client(proxies=\"http://proxy.yourcompany.com:8080\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "2fd99e97-013f-4c28-bb47-426faa42a2cf",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/providers/goat.mdx
+++ b/docs/docs/integrations/providers/goat.mdx
@@ -0,0 +1,39 @@
+# GOAT
+
+[GOAT](https://github.com/goat-sdk/goat) is the finance toolkit for AI agents.
+
+Create agents that can:
+
+- Send and receive payments
+- Purchase physical and digital goods and services
+- Engage in various investment strategies:
+  - Earn yield
+  - Bet on prediction markets
+- Purchase crypto assets
+- Tokenize any asset
+- Get financial insights
+
+### How it works
+GOAT leverages blockchains, cryptocurrencies (such as stablecoins), and wallets as the infrastructure to enable agents to become economic actors:
+
+1. Give your agent a [wallet](https://github.com/goat-sdk/goat/tree/main#chains-and-wallets)
+2. Allow it to transact [anywhere](https://github.com/goat-sdk/goat/tree/main#chains-and-wallets)
+3. Use more than [+200 tools](https://github.com/goat-sdk/goat/tree/main#tools)
+
+See everything GOAT supports [here](https://github.com/goat-sdk/goat/tree/main#chains-and-wallets).
+
+**Lightweight and extendable**
+Different from other toolkits, GOAT is designed to be lightweight and extendable by keeping its core minimal and allowing you to install only the tools you need.
+
+If you don't find what you need on our more than 200 integrations you can easily:
+
+- Create your own plugin
+- Integrate a new chain
+- Integrate a new wallet
+- Integrate a new agent framework
+
+See how to do it [here](https://github.com/goat-sdk/goat/tree/main#-contributing).
+
+## Installation and Setup
+
+Check out our [quickstart](https://github.com/goat-sdk/goat/tree/main/python/examples/by-framework/langchain) to see how to set up and install GOAT. 
--- a/docs/docs/integrations/providers/ibm.mdx
+++ b/docs/docs/integrations/providers/ibm.mdx
@@ -67,3 +67,13 @@ See a [usage example](/docs/integrations/retrievers/ibm_watsonx_ranker).
 ```python
 from langchain_ibm import WatsonxRerank
 ```
+
+## Toolkit
+
+### WatsonxToolkit
+
+See a [usage example](/docs/integrations/tools/ibm_watsonx).
+
+```python
+from langchain_ibm import WatsonxToolkit
+```
--- a/docs/docs/integrations/providers/memgraph.mdx
+++ b/docs/docs/integrations/providers/memgraph.mdx
@@ -0,0 +1,40 @@
+# Memgraph
+
+>Memgraph is a high-performance, in-memory graph database that is optimized for real-time queries and analytics.
+>Get started with Memgraph by visiting [their website](https://memgraph.com/).
+
+## Installation and Setup
+
+- Install the Python SDK with `pip install langchain-memgraph`
+
+## MemgraphQAChain
+
+There exists a wrapper around Memgraph graph database that allows you to generate Cypher statements based on the user input
+and use them to retrieve relevant information from the database.
+
+```python
+from langchain_memgraph.chains.graph_qa import MemgraphQAChain
+from langchain_memgraph.graphs.memgraph import Memgraph
+```
+
+See a [usage example](/docs/integrations/graphs/memgraph)
+
+## Constructing a Knowledge Graph from unstructured data
+
+You can use the integration to construct a knowledge graph from unstructured data.
+
+```python
+from langchain_memgraph.graphs.memgraph import Memgraph
+from langchain_experimental.graph_transformers import LLMGraphTransformer
+```
+
+See a [usage example](/docs/integrations/graphs/memgraph)
+
+## Memgraph Tools and Toolkit
+
+Memgraph also provides a toolkit that allows you to interact with the Memgraph database.
+See a [usage example](/docs/integrations/tools/memgraph).
+
+```python
+from langchain_memgraph import MemgraphToolkit
+```
--- a/docs/docs/integrations/providers/netmind.ipynb
+++ b/docs/docs/integrations/providers/netmind.ipynb
@@ -0,0 +1,73 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Netmind\n",
+    "\n",
+    "[Netmind AI](https://www.netmind.ai/) Build AI Faster, Smarter, and More Affordably.\n",
+    "Train, Fine-tune, Run Inference, and Scale with our Global GPU Network—Your all-in-one AI Engine.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with Netmind AI models.\n"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Installation and Setup\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-netmind\n",
+    "```\n",
+    "\n",
+    "Get an Netmind api key and set it as an environment variable (`NETMIND_API_KEY`).  \n",
+    "Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. \n",
+    "\n"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Chat Models\n",
+    "\n",
+    "For more on Netmind chat models, visit the guide [here](/docs/integrations/chat/netmind)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Embedding Model\n",
+    "\n",
+    "For more on Netmind embedding models, visit the [guide](/docs/integrations/text_embedding/netmind)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
--- a/docs/docs/integrations/providers/sambanova.ipynb
+++ b/docs/docs/integrations/providers/sambanova.ipynb
@@ -114,6 +114,25 @@
    "## Embedding Models"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_sambanova import SambaNovaCloudEmbeddings\n",
+    "\n",
+    "embeddings = SambaNovaCloudEmbeddings(model=\"E5-Mistral-7B-Instruct\")\n",
+    "embeddings.embed_query(\"What is the meaning of life?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a more detailed walkthrough of the SambaStudioEmbeddings component, see [this notebook](https://python.langchain.com/docs/integrations/text_embedding/sambanova/)"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@@ -130,7 +149,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "For a more detailed walkthrough of the SambaStudioEmbeddings component, see [this notebook](https://python.langchain.com/docs/integrations/text_embedding/sambanova/)"
+    "For a more detailed walkthrough of the SambaStudioEmbeddings component, see [this notebook](https://python.langchain.com/docs/integrations/text_embedding/sambastudio/)"
   ]
  },
  {
--- a/docs/docs/integrations/providers/vectara.ipynb
+++ b/docs/docs/integrations/providers/vectara.ipynb
@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "559f8e0e",
+   "metadata": {},
+   "source": [
+    "# Vectara\n",
+    "\n",
+    "[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.\n",
+    "Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:\n",
+    "1. A way to extract text from files (PDF, PPT, DOCX, etc)\n",
+    "2. ML-based chunking that provides state of the art performance.\n",
+    "3. The [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model.\n",
+    "4. Its own internal vector database where text chunks and embedding vectors are stored.\n",
+    "5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
+    "6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
+    "\n",
+    "For more information:\n",
+    "- [Documentation](https://docs.vectara.com/docs/)\n",
+    "- [API Playground](https://docs.vectara.com/docs/rest-api/)\n",
+    "- [Quickstart](https://docs.vectara.com/docs/quickstart)\n",
+    "\n",
+    "This notebook shows how to use the basic retrieval functionality, when utilizing Vectara just as a Vector Store (without summarization), incuding: `similarity_search` and `similarity_search_with_score` as well as using the LangChain `as_retriever` functionality.\n",
+    "\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To use the `VectaraVectorStore` you first need to install the partner package.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfdf03ba-d6f5-4b1e-86d3-a65c4bc99aa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv pip install -U pip && uv pip install -qU langchain-vectara"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e97dcf11",
+   "metadata": {},
+   "source": [
+    "# Getting Started\n",
+    "\n",
+    "To get started, use the following steps:\n",
+    "1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial.\n",
+    "2. Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
+    "3. Next you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\" and you now have an active API key. Keep this key confidential. \n",
+    "\n",
+    "To use LangChain with Vectara, you'll need to have these two values: `corpus_key` and `api_key`.\n",
+    "You can provide `VECTARA_API_KEY` to LangChain in two ways:\n",
+    "\n",
+    "1. Include in your environment these two variables: `VECTARA_API_KEY`.\n",
+    "\n",
+    "   For example, you can set these variables using os.environ and getpass as follows:\n",
+    "\n",
+    "```python\n",
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"VECTARA_API_KEY\"] = getpass.getpass(\"Vectara API Key:\")\n",
+    "```\n",
+    "\n",
+    "2. Add them to the `Vectara` vectorstore constructor:\n",
+    "\n",
+    "```python\n",
+    "vectara = Vectara(\n",
+    "    vectara_api_key=vectara_api_key\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "In this notebook we assume they are provided in the environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "aac7a9a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"VECTARA_API_KEY\"] = \"<VECTARA_API_KEY>\"\n",
+    "os.environ[\"VECTARA_CORPUS_KEY\"] = \"VECTARA_CORPUS_KEY\"\n",
+    "\n",
+    "from langchain_vectara import Vectara\n",
+    "from langchain_vectara.vectorstores import (\n",
+    "    ChainReranker,\n",
+    "    CorpusConfig,\n",
+    "    CustomerSpecificReranker,\n",
+    "    File,\n",
+    "    GenerationConfig,\n",
+    "    MmrReranker,\n",
+    "    SearchConfig,\n",
+    "    VectaraQueryConfig,\n",
+    ")\n",
+    "\n",
+    "vectara = Vectara(vectara_api_key=os.getenv(\"VECTARA_API_KEY\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "875ffb7e",
+   "metadata": {},
+   "source": [
+    "First we load the state-of-the-union text into Vectara.\n",
+    "\n",
+    "Note that we use the add_files interface which does not require any local processing or chunking - Vectara receives the file content and performs all the necessary pre-processing, chunking and embedding of the file into its knowledge store.\n",
+    "\n",
+    "In this case it uses a .txt file but the same works for many other [file types](https://docs.vectara.com/docs/api-reference/indexing-apis/file-upload/file-upload-filetypes)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "be0a4973",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['state_of_the_union.txt']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus_key = os.getenv(\"VECTARA_CORPUS_KEY\")\n",
+    "file_obj = File(\n",
+    "    file_path=\"../document_loaders/example_data/state_of_the_union.txt\",\n",
+    "    metadata={\"source\": \"text_file\"},\n",
+    ")\n",
+    "vectara.add_files([file_obj], corpus_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22a6b953",
+   "metadata": {},
+   "source": [
+    "## Vectara RAG (retrieval augmented generation)\n",
+    "\n",
+    "We now create a `VectaraQueryConfig` object to control the retrieval and summarization options:\n",
+    "* We enable summarization, specifying we would like the LLM to pick the top 7 matching chunks and respond in English\n",
+    "\n",
+    "Using this configuration, let's create a LangChain `Runnable` object that encpasulates the full Vectara RAG pipeline, using the `as_rag` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9ecda054-96a8-4a91-aeae-32006efb1ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"President Biden discussed several key issues in his recent statements. He emphasized the importance of keeping schools open and noted that with a high vaccination rate and reduced hospitalizations, most Americans can safely return to normal activities without masks [1]. He addressed the need to hold social media platforms accountable for their impact on children and called for stronger privacy protections and mental health services [2]. Biden also announced measures against Russia, including preventing its central bank from defending the Ruble and targeting Russian oligarchs' assets, as part of efforts to weaken Russia's economy and military [3]. Additionally, he highlighted the importance of protecting women's rights, specifically the right to choose as affirmed in Roe v. Wade [5]. Lastly, he advocated for funding the police with necessary resources and training to ensure community safety [6].\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generation_config = GenerationConfig(\n",
+    "    max_used_search_results=7,\n",
+    "    response_language=\"eng\",\n",
+    "    generation_preset_name=\"vectara-summary-ext-24-05-med-omni\",\n",
+    "    enable_factual_consistency_score=True,\n",
+    ")\n",
+    "search_config = SearchConfig(\n",
+    "    corpora=[CorpusConfig(corpus_key=corpus_key)],\n",
+    "    limit=25,\n",
+    "    reranker=ChainReranker(\n",
+    "        rerankers=[\n",
+    "            CustomerSpecificReranker(reranker_id=\"rnk_272725719\", limit=100),\n",
+    "            MmrReranker(diversity_bias=0.2, limit=100),\n",
+    "        ]\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "config = VectaraQueryConfig(\n",
+    "    search=search_config,\n",
+    "    generation=generation_config,\n",
+    ")\n",
+    "\n",
+    "query_str = \"what did Biden say?\"\n",
+    "\n",
+    "rag = vectara.as_rag(config)\n",
+    "rag.invoke(query_str)[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd825d63-93a0-4e45-a455-bfabb01ee1a1",
+   "metadata": {},
+   "source": [
+    "We can also use the streaming interface like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "27f01330-8917-4eff-b603-59ab2571a4d2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "President Biden emphasized several key points in his statements. He highlighted the importance of keeping schools open and noted that with a high vaccination rate and reduced hospitalizations, most Americans can safely return to normal activities without masks [1]. He addressed the need to hold social media platforms accountable for their impact on children and called for stronger privacy protections and mental health services [2]. Biden also discussed measures against Russia, including preventing their central bank from defending the Ruble and targeting Russian oligarchs' assets [3]. Additionally, he reaffirmed the commitment to protect women's rights, particularly the right to choose as affirmed in Roe v. Wade [5]. Lastly, he advocated for funding the police to ensure community safety [6]."
+     ]
+    }
+   ],
+   "source": [
+    "output = {}\n",
+    "curr_key = None\n",
+    "for chunk in rag.stream(query_str):\n",
+    "    for key in chunk:\n",
+    "        if key not in output:\n",
+    "            output[key] = chunk[key]\n",
+    "        else:\n",
+    "            output[key] += chunk[key]\n",
+    "        if key == \"answer\":\n",
+    "            print(chunk[key], end=\"\", flush=True)\n",
+    "        curr_key = key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f16bf8d",
+   "metadata": {},
+   "source": [
+    "For more details about Vectara as VectorStore [go to this notebook](../vectorstores/vectara.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d49a91d2-9c53-48cb-8065-a3ba1292e8d0",
+   "metadata": {},
+   "source": [
+    "## Vectara Chat\n",
+    "\n",
+    "In most uses of LangChain to create chatbots, one must integrate a special `memory` component that maintains the history of chat sessions and then uses that history to ensure the chatbot is aware of conversation history.\n",
+    "\n",
+    "With Vectara Chat - all of that is performed in the backend by Vectara automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f57264ec-e8b5-4d55-9c16-54898d506f73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The president stated that nominating someone to serve on the United States Supreme Court is one of the most serious constitutional responsibilities he has. He nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, describing her as one of the nation’s top legal minds who will continue Justice Breyer’s legacy of excellence [1].'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generation_config = GenerationConfig(\n",
+    "    max_used_search_results=7,\n",
+    "    response_language=\"eng\",\n",
+    "    generation_preset_name=\"vectara-summary-ext-24-05-med-omni\",\n",
+    "    enable_factual_consistency_score=True,\n",
+    ")\n",
+    "search_config = SearchConfig(\n",
+    "    corpora=[CorpusConfig(corpus_key=corpus_key, limit=25)],\n",
+    "    reranker=MmrReranker(diversity_bias=0.2),\n",
+    ")\n",
+    "\n",
+    "config = VectaraQueryConfig(\n",
+    "    search=search_config,\n",
+    "    generation=generation_config,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "bot = vectara.as_chat(config)\n",
+    "\n",
+    "bot.invoke(\"What did the president say about Ketanji Brown Jackson?\")[\"answer\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13714687-672d-47af-997a-61bb9dd66923",
+   "metadata": {},
+   "source": [
+    "For more details about Vectara chat [go to this notebook](../chat/vectara.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baf687dc-08c4-49af-98aa-0359e2591f2e",
+   "metadata": {},
+   "source": [
+    "## Vectara as self-querying retriever\n",
+    "Vectara offers Intelligent Query Rewriting option which  enhances search precision by automatically generating metadata filter expressions from natural language queries. This capability analyzes user queries, extracts relevant metadata filters, and rephrases the query to focus on the core information need. For more details [go to this notebook](../retrievers/self_query/vectara_self_query.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8060a423-b291-4166-8fd7-ba0e01692b51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/docs/integrations/providers/vectara/index.mdx
+++ b/docs/docs/integrations/providers/vectara/index.mdx
@@ -1,181 +0,0 @@
-# Vectara
-
->[Vectara](https://vectara.com/) provides a Trusted Generative AI platform, allowing organizations to rapidly create a ChatGPT-like experience (an AI assistant) 
-> which is grounded in the data, documents, and knowledge that they have (technically, it is Retrieval-Augmented-Generation-as-a-service).
-
-**Vectara Overview:**
-[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.
-Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:
-1. A way to extract text from files (PDF, PPT, DOCX, etc)
-2. ML-based chunking that provides state of the art performance.
-3. The [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model.
-4. Its own internal vector database where text chunks and embedding vectors are stored.
-5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). 
-6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.
-
-For more information:
- [Documentation](https://docs.vectara.com/docs/)
- [API Playground](https://docs.vectara.com/docs/rest-api/)
- [Quickstart](https://docs.vectara.com/docs/quickstart)
-
-## Installation and Setup
-
-To use `Vectara` with LangChain no special installation steps are required. 
-To get started, [sign up](https://vectara.com/integrations/langchain) for a free Vectara trial,
-and follow the [quickstart](https://docs.vectara.com/docs/quickstart) guide to create a corpus and an API key. 
-Once you have these, you can provide them as arguments to the Vectara `vectorstore`, or you can set them as environment variables.
-
- export `VECTARA_CUSTOMER_ID`="your_customer_id"
- export `VECTARA_CORPUS_ID`="your_corpus_id"
- export `VECTARA_API_KEY`="your-vectara-api-key"
-
-## Vectara as a Vector Store
-
-There exists a wrapper around the Vectara platform, allowing you to use it as a `vectorstore` in LangChain:
-
-To import this vectorstore:
-```python
-from langchain_community.vectorstores import Vectara
-```
-
-To create an instance of the Vectara vectorstore:
-```python
-vectara = Vectara(
-    vectara_customer_id=customer_id, 
-    vectara_corpus_id=corpus_id, 
-    vectara_api_key=api_key
-)
-```
-The `customer_id`, `corpus_id` and `api_key` are optional, and if they are not supplied will be read from 
-the environment variables `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`, respectively.
-
-### Adding Texts or Files
-
-After you have the vectorstore, you can `add_texts` or `add_documents` as per the standard `VectorStore` interface, for example:
-
-```python
-vectara.add_texts(["to be or not to be", "that is the question"])
-```
-
-Since Vectara supports file-upload in the platform, we also added the ability to upload files (PDF, TXT, HTML, PPT, DOC, etc) directly. 
-When using this method, each file is uploaded directly to the Vectara backend, processed and chunked optimally there, so you don't have to use the LangChain document loader or chunking mechanism.
-
-As an example:
-
-```python
-vectara.add_files(["path/to/file1.pdf", "path/to/file2.pdf",...])
-```
-
-Of course you do not have to add any data, and instead just connect to an existing Vectara corpus where data may already be indexed.
-
-### Querying the VectorStore
-
-To query the Vectara vectorstore, you can use the `similarity_search` method (or `similarity_search_with_score`), which takes a query string and returns a list of results:
-```python
-results = vectara.similarity_search_with_score("what is LangChain?")
-```
-The results are returned as a list of relevant documents, and a relevance score of each document.
-
-In this case, we used the default retrieval parameters, but you can also specify the following additional arguments in `similarity_search` or `similarity_search_with_score`:
- `k`: number of results to return (defaults to 5)
- `lambda_val`: the [lexical matching](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) factor for hybrid search (defaults to 0.025)
- `filter`: a [filter](https://docs.vectara.com/docs/common-use-cases/filtering-by-metadata/filter-overview) to apply to the results (default None)
- `n_sentence_context`: number of sentences to include before/after the actual matching segment when returning results. This defaults to 2.
- `rerank_config`: can be used to specify reranker for thr results
-   - `reranker`: mmr, rerank_multilingual_v1 or none. Note that "rerank_multilingual_v1" is a Scale only feature
-   - `rerank_k`: number of results to use for reranking
-   - `mmr_diversity_bias`: 0 = no diversity, 1 = full diversity. This is the lambda parameter in the MMR formula and is in the range 0...1
-
-To get results without the relevance score, you can simply use the 'similarity_search' method:
-```python   
-results = vectara.similarity_search("what is LangChain?")
-```
-
-## Vectara for Retrieval Augmented Generation (RAG)
-
-Vectara provides a full RAG pipeline, including generative summarization. To use it as a complete RAG solution, you can use the `as_rag` method.
-There are a few additional parameters that can be specified in the `VectaraQueryConfig` object to control retrieval and summarization:
-* k: number of results to return
-* lambda_val: the lexical matching factor for hybrid search
-* summary_config (optional): can be used to request an LLM summary in RAG
-   - is_enabled: True or False
-   - max_results: number of results to use for summary generation
-   - response_lang: language of the response summary, in ISO 639-2 format (e.g. 'en', 'fr', 'de', etc)
-* rerank_config (optional): can be used to specify Vectara Reranker of the results
-   - reranker: mmr, rerank_multilingual_v1 or none
-   - rerank_k: number of results to use for reranking
-   - mmr_diversity_bias: 0 = no diversity, 1 = full diversity. 
-     This is the lambda parameter in the MMR formula and is in the range 0...1
-
-For example:
-
-```python
-summary_config = SummaryConfig(is_enabled=True, max_results=7, response_lang='eng')
-rerank_config = RerankConfig(reranker="mmr", rerank_k=50, mmr_diversity_bias=0.2)
-config = VectaraQueryConfig(k=10, lambda_val=0.005, rerank_config=rerank_config, summary_config=summary_config)
-```
-Then you can use the `as_rag` method to create a RAG pipeline:
-
-```python
-query_str = "what did Biden say?"
-
-rag = vectara.as_rag(config)
-rag.invoke(query_str)['answer']
-```
-
-The `as_rag` method returns a `VectaraRAG` object, which behaves just like any LangChain Runnable, including the `invoke` or `stream` methods.
-
-## Vectara Chat
-
-The RAG functionality can be used to create a chatbot. For example, you can create a simple chatbot that responds to user input:
-
-```python
-summary_config = SummaryConfig(is_enabled=True, max_results=7, response_lang='eng')
-rerank_config = RerankConfig(reranker="mmr", rerank_k=50, mmr_diversity_bias=0.2)
-config = VectaraQueryConfig(k=10, lambda_val=0.005, rerank_config=rerank_config, summary_config=summary_config)
-
-query_str = "what did Biden say?"
-bot = vectara.as_chat(config)
-bot.invoke(query_str)['answer']
-```
-
-The main difference is the following: with `as_chat` Vectara internally tracks the chat history and conditions each response on the full chat history.
-There is no need to keep that history locally to LangChain, as Vectara will manage it internally.
-
-## Vectara as a LangChain retriever only
-
-If you want to use Vectara as a retriever only, you can use the `as_retriever` method, which returns a `VectaraRetriever` object.
-```python
-retriever = vectara.as_retriever(config=config)
-retriever.invoke(query_str)
-```
-
-Like with as_rag, you provide a `VectaraQueryConfig` object to control the retrieval parameters.
-In most cases you would not enable the summary_config, but it is left as an option for backwards compatibility. 
-If no summary is requested, the response will be a list of relevant documents, each with a relevance score.
-If a summary is requested, the response will be a list of relevant documents as before, plus an additional document that includes the generative summary.
-
-## Hallucination Detection score
-
-Vectara created [HHEM](https://huggingface.co/vectara/hallucination_evaluation_model) - an open source model that can be used to evaluate RAG responses for factual consistency. 
-As part of the Vectara RAG, the "Factual Consistency Score" (or FCS), which is an improved version of the open source HHEM is made available via the API. 
-This is automatically included in the output of the RAG pipeline
-
-```python
-summary_config = SummaryConfig(is_enabled=True, max_results=7, response_lang='eng')
-rerank_config = RerankConfig(reranker="mmr", rerank_k=50, mmr_diversity_bias=0.2)
-config = VectaraQueryConfig(k=10, lambda_val=0.005, rerank_config=rerank_config, summary_config=summary_config)
-
-rag = vectara.as_rag(config)
-resp = rag.invoke(query_str)
-print(resp['answer'])
-print(f"Vectara FCS = {resp['fcs']}")
-```
-
-## Example Notebooks
-
-For a more detailed examples of using Vectara with LangChain, see the following example notebooks:
-* [this notebook](/docs/integrations/vectorstores/vectara) shows how to use Vectara: with full RAG or just as a retriever.
-* [this notebook](/docs/integrations/retrievers/self_query/vectara_self_query) shows the self-query capability with Vectara.
-* [this notebook](/docs/integrations/providers/vectara/vectara_chat) shows how to build a chatbot with Langchain and Vectara
-
--- a/docs/docs/integrations/providers/vectorize.mdx
+++ b/docs/docs/integrations/providers/vectorize.mdx
@@ -0,0 +1,40 @@
+# Vectorize
+
+> [Vectorize](https://vectorize.io/) helps you build AI apps faster and with less hassle.
+> It automates data extraction, finds the best vectorization strategy using RAG evaluation,
+> and lets you quickly deploy real-time RAG pipelines for your unstructured data.
+> Your vector search indexes stay up-to-date, and it integrates with your existing vector database,
+> so you maintain full control of your data.
+> Vectorize handles the heavy lifting, freeing you to focus on building robust AI solutions without getting bogged down by data management.
+
+# Installation and Setup
+
+Install the following Python package:
+```bash
+pip install langchain-vectorize
+```
+
+Sign up for a free Vectorize account [here](https://platform.vectorize.io/)
+Generate an access token in the [Access Token](https://docs.vectorize.io/rag-pipelines/retrieval-endpoint#access-tokens) section
+Gather your organization ID. From the browser url, extract the UUID from the URL after /organization/
+
+Set up the following variables:
+```python
+VECTORIZE_ORG_ID="your-organization-id"
+VECTORIZE_API_TOKEN="your-api-token"
+```
+
+## Retriever
+
+```python
+from langchain_vectorize import VectorizeRetriever
+
+retriever = VectorizeRetriever(
+    api_token=VECTORIZE_API_TOKEN,
+    organization=VECTORIZE_ORG_ID,
+    pipeline_id="...",
+)
+retriever.invoke("query")
+```
+
+Learn more in the [example notebook](/docs/integrations/retrievers/vectorize).
--- a/docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb
+++ b/docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb
@@ -8,7 +8,6 @@
    "# Vectara self-querying \n",
    "\n",
    "[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.\n",
-    "\n",
    "Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:\n",
    "1. A way to extract text from files (PDF, PPT, DOCX, etc)\n",
    "2. ML-based chunking that provides state of the art performance.\n",
@@ -17,9 +16,27 @@
    "5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
    "6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
    "\n",
-    "See the [Vectara API documentation](https://docs.vectara.com/docs/) for more information on how to use the API.\n",
+    "For more information:\n",
+    "- [Documentation](https://docs.vectara.com/docs/)\n",
+    "- [API Playground](https://docs.vectara.com/docs/rest-api/)\n",
+    "- [Quickstart](https://docs.vectara.com/docs/quickstart)\n",
    "\n",
-    "This notebook shows how to use `SelfQueryRetriever` with Vectara."
+    "\n",
+    "This notebook shows how to use `Vectara` as `SelfQueryRetriever`.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To use the `VectaraVectorStore` you first need to install the partner package.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f3f1a4-f552-4d07-ba48-18fb5d8641c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv pip install -U pip && uv pip install -qU langchain-vectara"
   ]
  },
  {
@@ -30,14 +47,14 @@
    "# Getting Started\n",
    "\n",
    "To get started, use the following steps:\n",
-    "1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.\n",
+    "1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial.\n",
    "2. Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
    "3. Next you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\" and you now have an active API key. Keep this key confidential. \n",
    "\n",
-    "To use LangChain with Vectara, you'll need to have these three values: `customer ID`, `corpus ID` and `api_key`.\n",
-    "You can provide those to LangChain in two ways:\n",
+    "To use LangChain with Vectara, you'll need to have these two values: `corpus_key` and `api_key`.\n",
+    "You can provide `VECTARA_API_KEY` to LangChain in two ways:\n",
    "\n",
-    "1. Include in your environment these three variables: `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`.\n",
+    "1. Include in your environment these two variables: `VECTARA_API_KEY`.\n",
    "\n",
    "   For example, you can set these variables using os.environ and getpass as follows:\n",
    "\n",
@@ -45,8 +62,6 @@
    "import os\n",
    "import getpass\n",
    "\n",
-    "os.environ[\"VECTARA_CUSTOMER_ID\"] = getpass.getpass(\"Vectara Customer ID:\")\n",
-    "os.environ[\"VECTARA_CORPUS_ID\"] = getpass.getpass(\"Vectara Corpus ID:\")\n",
    "os.environ[\"VECTARA_API_KEY\"] = getpass.getpass(\"Vectara API Key:\")\n",
    "```\n",
    "\n",
@@ -54,14 +69,11 @@
    "\n",
    "```python\n",
    "vectara = Vectara(\n",
-    "                vectara_customer_id=vectara_customer_id,\n",
-    "                vectara_corpus_id=vectara_corpus_id,\n",
-    "                vectara_api_key=vectara_api_key\n",
-    "            )\n",
+    "    vectara_api_key=vectara_api_key\n",
+    ")\n",
    "```\n",
-    "In this notebook we assume they are provided in the environment.\n",
    "\n",
-    "**Notes:** The self-query retriever requires you to have `lark` installed (`pip install lark`). "
+    "In this notebook we assume they are provided in the environment."
   ]
  },
  {
@@ -71,14 +83,14 @@
   "source": [
    "## Connecting to Vectara from LangChain\n",
    "\n",
-    "In this example, we assume that you've created an account and a corpus, and added your `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY` (created with permissions for both indexing and query) as environment variables.\n",
+    "In this example, we assume that you've created an account and a corpus, and added your `VECTARA_CORPUS_KEY` and `VECTARA_API_KEY` (created with permissions for both indexing and query) as environment variables.\n",
    "\n",
    "We further assume the corpus has 4 fields defined as filterable metadata attributes: `year`, `director`, `rating`, and `genre`"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
   "id": "9d3aa44f",
   "metadata": {},
   "outputs": [],
@@ -87,14 +99,10 @@
    "\n",
    "from langchain_core.documents import Document\n",
    "\n",
-    "os.environ[\"VECTARA_API_KEY\"] = \"<YOUR_VECTARA_API_KEY>\"\n",
-    "os.environ[\"VECTARA_CORPUS_ID\"] = \"<YOUR_VECTARA_CORPUS_ID>\"\n",
-    "os.environ[\"VECTARA_CUSTOMER_ID\"] = \"<YOUR_VECTARA_CUSTOMER_ID>\"\n",
+    "os.environ[\"VECTARA_API_KEY\"] = \"VECTARA_API_KEY\"\n",
+    "os.environ[\"VECTARA_CORPUS_KEY\"] = \"VECTARA_CORPUS_KEY\"\n",
    "\n",
-    "from langchain.chains.query_constructor.schema import AttributeInfo\n",
-    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain_community.vectorstores import Vectara\n",
-    "from langchain_openai.chat_models import ChatOpenAI"
+    "from langchain_vectara import Vectara"
   ]
  },
  {
@@ -109,7 +117,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
   "id": "bcbe04d9",
   "metadata": {
    "tags": []
@@ -148,9 +156,12 @@
    "    ),\n",
    "]\n",
    "\n",
+    "corpus_key = os.getenv(\"VECTARA_CORPUS_KEY\")\n",
    "vectara = Vectara()\n",
    "for doc in docs:\n",
-    "    vectara.add_texts([doc.page_content], doc_metadata=doc.metadata)"
+    "    vectara.add_texts(\n",
+    "        [doc.page_content], corpus_key=corpus_key, doc_metadata=doc.metadata\n",
+    "    )"
   ]
  },
  {
@@ -158,45 +169,32 @@
   "id": "5ecaab6d",
   "metadata": {},
   "source": [
-    "## Creating the self-querying retriever\n",
-    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.\n",
+    "## Self-query with Vectara\n",
+    " You don't need self-query via the LangChain mechanism—enabling `intelligent_query_rewriting` on the Vectara platform achieves the same result.\n",
+    "Vectara offers Intelligent Query Rewriting option which  enhances search precision by automatically generating metadata filter expressions from natural language queries. This capability analyzes user queries, extracts relevant metadata filters, and rephrases the query to focus on the core information need. For more [details](https://docs.vectara.com/docs/search-and-retrieval/intelligent-query-rewriting).\n",
    "\n",
-    "We then provide an llm (in this case OpenAI) and the `vectara` vectorstore as arguments:"
+    "Enable intelligent query rewriting on a per-query basis by setting the `intelligent_query_rewriting` parameter to `true` in `VectaraQueryConfig`."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
   "id": "86e34dbf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
-    "metadata_field_info = [\n",
-    "    AttributeInfo(\n",
-    "        name=\"genre\",\n",
-    "        description=\"The genre of the movie\",\n",
-    "        type=\"string or list[string]\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"year\",\n",
-    "        description=\"The year the movie was released\",\n",
-    "        type=\"integer\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"director\",\n",
-    "        description=\"The name of the movie director\",\n",
-    "        type=\"string\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
-    "    ),\n",
-    "]\n",
-    "document_content_description = \"Brief summary of a movie\"\n",
-    "llm = ChatOpenAI(temperature=0, model=\"gpt-4o\", max_tokens=4069)\n",
-    "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm, vectara, document_content_description, metadata_field_info, verbose=True\n",
+    "from langchain_vectara.vectorstores import (\n",
+    "    CorpusConfig,\n",
+    "    SearchConfig,\n",
+    "    VectaraQueryConfig,\n",
+    ")\n",
+    "\n",
+    "config = VectaraQueryConfig(\n",
+    "    search=SearchConfig(corpora=[CorpusConfig(corpus_key=corpus_key)]),\n",
+    "    generation=None,\n",
+    "    intelligent_query_rewriting=True,\n",
    ")"
   ]
  },
@@ -205,116 +203,31 @@
   "id": "ea9df8d4",
   "metadata": {},
   "source": [
-    "## Self-retrieval Queries\n",
-    "And now we can try actually using our retriever!"
+    "## Queries\n",
+    "And now we can try actually using our vectara_queries method!"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
   "id": "38a126e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}),\n",
-       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'}),\n",
-       " Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'}),\n",
-       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}),\n",
-       " Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'lang': 'eng', 'offset': '0', 'len': '82', 'year': '2019', 'director': 'Greta Gerwig', 'rating': '8.3', 'source': 'langchain'}),\n",
-       " Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'lang': 'eng', 'offset': '0', 'len': '76', 'year': '2010', 'director': 'Christopher Nolan', 'rating': '8.2', 'source': 'langchain'})]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a relevant query\n",
-    "retriever.invoke(\"What are movies about scientists\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fc3f1e6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'}),\n",
-       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'})]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a filter\n",
-    "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b19d4da0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'lang': 'eng', 'offset': '0', 'len': '82', 'year': '2019', 'director': 'Greta Gerwig', 'rating': '8.3', 'source': 'langchain'})]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a query and a filter\n",
-    "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f900e40e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'}),\n",
-       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'})]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a composite filter\n",
-    "retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "12a51522",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'}),\n",
-       " Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'})]"
+       "[(Document(metadata={'year': 1995, 'genre': 'animated', 'source': 'langchain'}, page_content='Toys come alive and have a blast doing so'),\n",
+       "  0.4141285717487335),\n",
+       " (Document(metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       "  0.4046250879764557),\n",
+       " (Document(metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2, 'source': 'langchain'}, page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...'),\n",
+       "  0.227469339966774),\n",
+       " (Document(metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3, 'source': 'langchain'}, page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them'),\n",
+       "  0.19208428263664246),\n",
+       " (Document(metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction', 'source': 'langchain'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
+       "  0.1902722418308258),\n",
+       " (Document(metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6, 'source': 'langchain'}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea'),\n",
+       "  0.08151976019144058)]"
      ]
     },
     "execution_count": 8,
@@ -323,74 +236,107 @@
    }
   ],
   "source": [
-    "# This example specifies a query and composite filter\n",
-    "retriever.invoke(\n",
-    "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
-   "metadata": {},
-   "source": [
-    "## Filter k\n",
-    "\n",
-    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
-    "\n",
-    "We can do this by passing `enable_limit=True` to the constructor."
+    "# This example only specifies a relevant query\n",
+    "vectara.vectara_query(\"What are movies about scientists\", config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
-   "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm,\n",
-    "    vectara,\n",
-    "    document_content_description,\n",
-    "    metadata_field_info,\n",
-    "    enable_limit=True,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00e8baad-a9d7-4498-bd8d-ca41d0691386",
+   "id": "fc3f1e6e",
   "metadata": {},
-   "source": [
-    "This is cool, we can include the number of results we would like to see in the query and the self retriever would correctly understand it. For example, let's look for "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2758d229-4f97-499c-819f-888acaf8ee10",
-   "metadata": {
-    "tags": []
-   },
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'}),\n",
-       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'})]"
+       "[(Document(metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6, 'source': 'langchain'}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea'),\n",
+       "  0.34279149770736694),\n",
+       " (Document(metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       "  0.242923304438591)]"
      ]
     },
-     "execution_count": 10,
+     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
-    "# This example only specifies a relevant query\n",
-    "retriever.invoke(\"what are two movies with a rating above 8.5\")"
+    "# This example only specifies a filter\n",
+    "vectara.vectara_query(\"I want to watch a movie rated higher than 8.5\", config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b19d4da0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(Document(metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3, 'source': 'langchain'}, page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them'),\n",
+       "  0.10141132771968842)]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a query and a filter\n",
+    "vectara.vectara_query(\"Has Greta Gerwig directed any movies about women\", config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f900e40e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(Document(metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       "  0.9508692026138306)]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a composite filter\n",
+    "vectara.vectara_query(\"What's a highly rated (above 8.5) science fiction film?\", config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "12a51522",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(Document(metadata={'year': 1995, 'genre': 'animated', 'source': 'langchain'}, page_content='Toys come alive and have a blast doing so'),\n",
+       "  0.7290377616882324),\n",
+       " (Document(metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction', 'source': 'langchain'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
+       "  0.4838160574436188)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a query and composite filter\n",
+    "vectara.vectara_query(\n",
+    "    \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\",\n",
+    "    config,\n",
+    ")"
   ]
  },
  {
@@ -418,7 +364,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.0"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/retrievers/vectorize.ipynb
+++ b/docs/docs/integrations/retrievers/vectorize.ipynb
@@ -0,0 +1,425 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "raw",
+   "source": [
+    "---\n",
+    "sidebar_label: Vectorize\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zvHrM3wa7IE1"
+   },
+   "source": [
+    "# VectorizeRetriever\n",
+    "\n",
+    "This notebook shows how to use the LangChain Vectorize retriever.\n",
+    "\n",
+    "> [Vectorize](https://vectorize.io/) helps you build AI apps faster and with less hassle.\n",
+    "> It automates data extraction, finds the best vectorization strategy using RAG evaluation,\n",
+    "> and lets you quickly deploy real-time RAG pipelines for your unstructured data.\n",
+    "> Your vector search indexes stay up-to-date, and it integrates with your existing vector database,\n",
+    "> so you maintain full control of your data.\n",
+    "> Vectorize handles the heavy lifting, freeing you to focus on building robust AI solutions without getting bogged down by data management.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "r-RswOO5o4K_"
+   },
+   "source": [
+    "## Setup\n",
+    "\n",
+    "In the following steps, we'll setup the Vectorize environment and create a RAG pipeline.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FhvmvFKh4Rlh"
+   },
+   "source": [
+    "### Create a Vectorize Account & Get Your Access Token\n",
+    "\n",
+    "Sign up for a free Vectorize account [here](https://platform.vectorize.io/)\n",
+    "Generate an access token in the [Access Token](https://docs.vectorize.io/rag-pipelines/retrieval-endpoint#access-tokens) section\n",
+    "Gather your organization ID. From the browser url, extract the UUID from the URL after /organization/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "L2SULMfWpWFX"
+   },
+   "source": [
+    "### Configure token and organization ID\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BnF8KoDZpg2O"
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "\n",
+    "VECTORIZE_ORG_ID = getpass.getpass(\"Enter Vectorize organization ID: \")\n",
+    "VECTORIZE_API_TOKEN = getpass.getpass(\"Enter Vectorize API Token: \")"
+   ]
+  },
+  {
+   "metadata": {
+    "id": "JdZ5vlzjoDVr"
+   },
+   "cell_type": "markdown",
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This retriever lives in the `langchain-vectorize` package:"
+   ]
+  },
+  {
+   "metadata": {
+    "id": "IJFmtvDLn5R3"
+   },
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "!pip install -qU langchain-vectorize"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Oj10Moznpz67"
+   },
+   "source": "### Download a PDF file"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "eLbbTPytrgNw"
+   },
+   "outputs": [],
+   "source": "!wget \"https://raw.githubusercontent.com/vectorize-io/vectorize-clients/refs/tags/python-0.1.3/tests/python/tests/research.pdf\""
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7g54J6awtshs"
+   },
+   "source": "### Initialize the vectorize client"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9Fr4yz5CrFWP"
+   },
+   "outputs": [],
+   "source": [
+    "import vectorize_client as v\n",
+    "\n",
+    "api = v.ApiClient(v.Configuration(access_token=VECTORIZE_API_TOKEN))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wPDoeqETxJrS"
+   },
+   "source": "### Create a File Upload Source Connector"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9yEARIcFue5N"
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "\n",
+    "import urllib3\n",
+    "\n",
+    "connectors_api = v.ConnectorsApi(api)\n",
+    "response = connectors_api.create_source_connector(\n",
+    "    VECTORIZE_ORG_ID, [{\"type\": \"FILE_UPLOAD\", \"name\": \"From API\"}]\n",
+    ")\n",
+    "source_connector_id = response.connectors[0].id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yU3lS6dpxZnQ"
+   },
+   "source": "### Upload the PDF file"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OIiMIZ8ZxUYF"
+   },
+   "outputs": [],
+   "source": [
+    "file_path = \"research.pdf\"\n",
+    "\n",
+    "http = urllib3.PoolManager()\n",
+    "uploads_api = v.UploadsApi(api)\n",
+    "metadata = {\"created-from-api\": True}\n",
+    "\n",
+    "upload_response = uploads_api.start_file_upload_to_connector(\n",
+    "    VECTORIZE_ORG_ID,\n",
+    "    source_connector_id,\n",
+    "    v.StartFileUploadToConnectorRequest(\n",
+    "        name=file_path.split(\"/\")[-1],\n",
+    "        content_type=\"application/pdf\",\n",
+    "        # add additional metadata that will be stored along with each chunk in the vector database\n",
+    "        metadata=json.dumps(metadata),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "with open(file_path, \"rb\") as f:\n",
+    "    response = http.request(\n",
+    "        \"PUT\",\n",
+    "        upload_response.upload_url,\n",
+    "        body=f,\n",
+    "        headers={\n",
+    "            \"Content-Type\": \"application/pdf\",\n",
+    "            \"Content-Length\": str(os.path.getsize(file_path)),\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "if response.status != 200:\n",
+    "    print(\"Upload failed: \", response.data)\n",
+    "else:\n",
+    "    print(\"Upload successful\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PdJJfOhfxiIo"
+   },
+   "source": "### Connect to the AI Platform and Vector Database"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0ZSGhXJfxjBb"
+   },
+   "outputs": [],
+   "source": [
+    "ai_platforms = connectors_api.get_ai_platform_connectors(VECTORIZE_ORG_ID)\n",
+    "builtin_ai_platform = [\n",
+    "    c.id for c in ai_platforms.ai_platform_connectors if c.type == \"VECTORIZE\"\n",
+    "][0]\n",
+    "\n",
+    "vector_databases = connectors_api.get_destination_connectors(VECTORIZE_ORG_ID)\n",
+    "builtin_vector_db = [\n",
+    "    c.id for c in vector_databases.destination_connectors if c.type == \"VECTORIZE\"\n",
+    "][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JWoL-kqQxs5H"
+   },
+   "source": "### Configure and Deploy the Pipeline"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hze9vJbQxvqA"
+   },
+   "outputs": [],
+   "source": [
+    "pipelines = v.PipelinesApi(api)\n",
+    "response = pipelines.create_pipeline(\n",
+    "    VECTORIZE_ORG_ID,\n",
+    "    v.PipelineConfigurationSchema(\n",
+    "        source_connectors=[\n",
+    "            v.SourceConnectorSchema(\n",
+    "                id=source_connector_id, type=\"FILE_UPLOAD\", config={}\n",
+    "            )\n",
+    "        ],\n",
+    "        destination_connector=v.DestinationConnectorSchema(\n",
+    "            id=builtin_vector_db, type=\"VECTORIZE\", config={}\n",
+    "        ),\n",
+    "        ai_platform=v.AIPlatformSchema(\n",
+    "            id=builtin_ai_platform, type=\"VECTORIZE\", config={}\n",
+    "        ),\n",
+    "        pipeline_name=\"My Pipeline From API\",\n",
+    "        schedule=v.ScheduleSchema(type=\"manual\"),\n",
+    "    ),\n",
+    ")\n",
+    "pipeline_id = response.data.id"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Configure tracing (optional)\n",
+    "\n",
+    "If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5ULion9wyj6T"
+   },
+   "source": "## Instantiation"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9D-QfiW7yoe0"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_vectorize.retrievers import VectorizeRetriever\n",
+    "\n",
+    "retriever = VectorizeRetriever(\n",
+    "    api_token=VECTORIZE_API_TOKEN,\n",
+    "    organization=VECTORIZE_ORG_ID,\n",
+    "    pipeline_id=pipeline_id,\n",
+    ")"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Usage\n",
+    "\n"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "query = \"Apple Shareholders equity\"\n",
+    "retriever.invoke(query, num_results=2)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Use within a chain\n",
+    "\n",
+    "Like other retrievers, VectorizeRetriever can be incorporated into LLM applications via [chains](/docs/how_to/sequence/).\n",
+    "\n",
+    "We will need a LLM or chat model:\n",
+    "\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" />"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"\"\"Answer the question based only on the context provided.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "Question: {question}\"\"\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "chain = (\n",
+    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "chain.invoke(\"...\")"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all VectorizeRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/vectorize/langchain_vectorize.retrievers.VectorizeRetriever.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- a/docs/docs/integrations/retrievers/wikipedia.ipynb
+++ b/docs/docs/integrations/retrievers/wikipedia.ipynb
@@ -1,262 +1,262 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "62727aaa-bcff-4087-891c-e539f824ee1f",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Wikipedia\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d62a16c1-10de-4f99-b392-c4ad2e6123a1",
-   "metadata": {},
-   "source": [
-    "# WikipediaRetriever\n",
-    "\n",
-    "## Overview\n",
-    ">[Wikipedia](https://wikipedia.org/) is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. `Wikipedia` is the largest and most-read reference work in history.\n",
-    "\n",
-    "This notebook shows how to retrieve wiki pages from `wikipedia.org` into the [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "import {ItemTable} from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"external_retrievers\" item=\"WikipediaRetriever\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb7d377c-168b-40e8-bd61-af6a4fb1b44f",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1bbc6013-2617-4f7e-9d8b-7453d09315c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51489529-5dcd-4b86-bda6-de0a39d8ffd1",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The integration lives in the `langchain-community` package. We also need to install the `wikipedia` python package itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a737220",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community wikipedia"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae622ac6-d18a-4754-a4bd-d30a078c19b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c15470b-a16b-4e0d-bc6a-6998bafbb5a4",
-   "metadata": {},
-   "source": [
-    "Now we can instantiate our retriever:\n",
-    "\n",
-    "`WikipediaRetriever` parameters include:\n",
-    "- optional `lang`: default=\"en\". Use it to search in a specific language part of Wikipedia\n",
-    "- optional `load_max_docs`: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.\n",
-    "- optional `load_all_available_meta`: default=False. By default only the most important fields downloaded: `Published` (date when document was published/last updated), `title`, `Summary`. If True, other fields also downloaded.\n",
-    "\n",
-    "`get_relevant_documents()` has one argument, `query`: free text which used to find documents in Wikipedia"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b78f0cd0-ffea-4fe3-9d1d-54639c4ef1ff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.retrievers import WikipediaRetriever\n",
-    "\n",
-    "retriever = WikipediaRetriever()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12aead36-7b97-4d9c-82e7-ec644a3127f9",
-   "metadata": {},
-   "source": [
-    "## Usage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "54a76605-6b1e-44bf-b8a2-7d48119290c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = retriever.invoke(\"TOKYO GHOUL\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "65ada2b7-3507-4dcb-9982-5f8f4e97a2e1",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tokyo Ghoul (Japanese: 東京喰種（トーキョーグール）, Hepburn: Tōkyō Gūru) is a Japanese dark fantasy manga series written and illustrated by Sui Ishida. It was serialized in Shueisha's seinen manga magazine Weekly Young Jump from September 2011 to September 2014, with its chapters collected in 14 tankōbon volumes. The story is set in an alternate version of Tokyo where humans coexist with ghouls, beings who loo\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].page_content[:400])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae3c3d16",
-   "metadata": {},
-   "source": [
-    "## Use within a chain\n",
-    "Like other retrievers, `WikipediaRetriever` can be incorporated into LLM applications via [chains](/docs/how_to/sequence/).\n",
-    "\n",
-    "We will need a LLM or chat model:\n",
-    "\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4bd3d268-eb8c-46e9-930a-18f5e2a50008",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9b52bc65-1b2e-4c30-ab43-41eaa5bf79c3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\n",
-    "    \"\"\"\n",
-    "    Answer the question based only on the context provided.\n",
-    "    Context: {context}\n",
-    "    Question: {question}\n",
-    "    \"\"\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "\n",
-    "chain = (\n",
-    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d268905-3b19-4338-ac10-223c0fe4d5e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The main character in Tokyo Ghoul is Ken Kaneki, who transforms into a ghoul after receiving an organ transplant from a ghoul named Rize.'"
+      "cell_type": "raw",
+      "id": "62727aaa-bcff-4087-891c-e539f824ee1f",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Wikipedia\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d62a16c1-10de-4f99-b392-c4ad2e6123a1",
+      "metadata": {},
+      "source": [
+        "# WikipediaRetriever\n",
+        "\n",
+        "## Overview\n",
+        ">[Wikipedia](https://wikipedia.org/) is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. `Wikipedia` is the largest and most-read reference work in history.\n",
+        "\n",
+        "This notebook shows how to retrieve wiki pages from `wikipedia.org` into the [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "import {ItemTable} from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"external_retrievers\" item=\"WikipediaRetriever\" />"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eb7d377c-168b-40e8-bd61-af6a4fb1b44f",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1bbc6013-2617-4f7e-9d8b-7453d09315c0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "51489529-5dcd-4b86-bda6-de0a39d8ffd1",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The integration lives in the `langchain-community` package. We also need to install the `wikipedia` python package itself."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1a737220",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community wikipedia"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ae622ac6-d18a-4754-a4bd-d30a078c19b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6c15470b-a16b-4e0d-bc6a-6998bafbb5a4",
+      "metadata": {},
+      "source": [
+        "Now we can instantiate our retriever:\n",
+        "\n",
+        "`WikipediaRetriever` parameters include:\n",
+        "- optional `lang`: default=\"en\". Use it to search in a specific language part of Wikipedia\n",
+        "- optional `load_max_docs`: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.\n",
+        "- optional `load_all_available_meta`: default=False. By default only the most important fields downloaded: `Published` (date when document was published/last updated), `title`, `Summary`. If True, other fields also downloaded.\n",
+        "\n",
+        "`get_relevant_documents()` has one argument, `query`: free text which used to find documents in Wikipedia"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "b78f0cd0-ffea-4fe3-9d1d-54639c4ef1ff",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.retrievers import WikipediaRetriever\n",
+        "\n",
+        "retriever = WikipediaRetriever()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12aead36-7b97-4d9c-82e7-ec644a3127f9",
+      "metadata": {},
+      "source": [
+        "## Usage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "54a76605-6b1e-44bf-b8a2-7d48119290c4",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "docs = retriever.invoke(\"TOKYO GHOUL\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "65ada2b7-3507-4dcb-9982-5f8f4e97a2e1",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tokyo Ghoul (Japanese: 東京喰種（トーキョーグール）, Hepburn: Tōkyō Gūru) is a Japanese dark fantasy manga series written and illustrated by Sui Ishida. It was serialized in Shueisha's seinen manga magazine Weekly Young Jump from September 2011 to September 2014, with its chapters collected in 14 tankōbon volumes. The story is set in an alternate version of Tokyo where humans coexist with ghouls, beings who loo\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].page_content[:400])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ae3c3d16",
+      "metadata": {},
+      "source": [
+        "## Use within a chain\n",
+        "Like other retrievers, `WikipediaRetriever` can be incorporated into LLM applications via [chains](/docs/how_to/sequence/).\n",
+        "\n",
+        "We will need a LLM or chat model:\n",
+        "\n",
+        "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+        "\n",
+        "<ChatModelTabs customVarName=\"llm\" />\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "4bd3d268-eb8c-46e9-930a-18f5e2a50008",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# | output: false\n",
+        "# | echo: false\n",
+        "\n",
+        "from langchain_openai import ChatOpenAI\n",
+        "\n",
+        "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "9b52bc65-1b2e-4c30-ab43-41eaa5bf79c3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.output_parsers import StrOutputParser\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "from langchain_core.runnables import RunnablePassthrough\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_template(\n",
+        "    \"\"\"\n",
+        "    Answer the question based only on the context provided.\n",
+        "    Context: {context}\n",
+        "    Question: {question}\n",
+        "    \"\"\"\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def format_docs(docs):\n",
+        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+        "\n",
+        "\n",
+        "chain = (\n",
+        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+        "    | prompt\n",
+        "    | llm\n",
+        "    | StrOutputParser()\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "0d268905-3b19-4338-ac10-223c0fe4d5e4",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'The main character in Tokyo Ghoul is Ken Kaneki, who transforms into a ghoul after receiving an organ transplant from a ghoul named Rize.'"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chain.invoke(\n",
+        "    \"Who is the main character in `Tokyo Ghoul` and does he transform into a ghoul?\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "236bbafb-ebd4-4165-9b8f-d47605f6eef3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `WikipediaRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html#langchain-community-retrievers-wikipedia-wikipediaretriever)."
      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "chain.invoke(\n",
-    "    \"Who is the main character in `Tokyo Ghoul` and does he transform into a ghoul?\"\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "236bbafb-ebd4-4165-9b8f-d47605f6eef3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `WikipediaRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html#langchain-community-retrievers-wikipedia-wikipediaretriever)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/retrievers/zotero.ipynb
+++ b/docs/docs/integrations/retrievers/zotero.ipynb
@@ -198,7 +198,7 @@
    "\n",
    "    Args:\n",
    "        query: str: The search query to be used. Try to keep this specific and short, e.g. a specific topic or author name\n",
-    "        itemType: Optional. Type of item to search for (e.g. \"book\" or \"journalArticle\"). Multiple types can be passed as a string seperated by \"||\", e.g. \"book || journalArticle\". Defaults to all types.\n",
+    "        itemType: Optional. Type of item to search for (e.g. \"book\" or \"journalArticle\"). Multiple types can be passed as a string separated by \"||\", e.g. \"book || journalArticle\". Defaults to all types.\n",
    "        tag: Optional. For searching over tags attached to library items. If documents tagged with multiple tags are to be retrieved, pass them as a list. If documents with any of the tags are to be retrieved, pass them as a string separated by \"||\", e.g. \"tag1 || tag2\"\n",
    "        qmode: Search mode to use. Changes what the query searches over. \"everything\" includes full-text content. \"titleCreatorYear\" to search over title, authors and year. Defaults to \"everything\".\n",
    "        since: Return only objects modified after the specified library version. Defaults to return everything.\n",
--- a/docs/docs/integrations/text_embedding/ai21.ipynb
+++ b/docs/docs/integrations/text_embedding/ai21.ipynb
@@ -1,272 +1,270 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: AI21\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# AI21Embeddings\n",
-    "\n",
-    ":::caution This service is deprecated. :::\n",
-    "\n",
-    "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"AI21\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access AI21 embedding models you'll need to create an AI21 account, get an API key, and install the `langchain-ai21` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [https://docs.ai21.com/](https://docs.ai21.com/) to sign up to AI21 and generate an API key. Once you've done this set the `AI21_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"AI21_API_KEY\"):\n",
-    "    os.environ[\"AI21_API_KEY\"] = getpass.getpass(\"Enter your AI21 API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain AI21 integration lives in the `langchain-ai21` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-ai21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_ai21 import AI21Embeddings\n",
-    "\n",
-    "embeddings = AI21Embeddings(\n",
-    "    # Can optionally increase or decrease the batch_size\n",
-    "    # to improve latency.\n",
-    "    # Use larger batch sizes with smaller documents, and\n",
-    "    # smaller batch sizes with larger documents.\n",
-    "    # batch_size=256,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: AI21\n",
+        "---"
      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.01913362182676792, 0.004960147198289633, -0.01582135073840618, -0.042474791407585144, 0.040200788\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# AI21Embeddings\n",
+        "\n",
+        ":::caution This service is deprecated. :::\n",
+        "\n",
+        "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"AI21\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access AI21 embedding models you'll need to create an AI21 account, get an API key, and install the `langchain-ai21` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [https://docs.ai21.com/](https://docs.ai21.com/) to sign up to AI21 and generate an API key. Once you've done this set the `AI21_API_KEY` environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.03029559925198555, 0.002908500377088785, -0.02700909972190857, -0.04616579785943031, 0.0382771529\n",
-      "[0.018214847892522812, 0.011460083536803722, -0.03329407051205635, -0.04951060563325882, 0.032756105\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"AI21_API_KEY\"):\n",
+        "    os.environ[\"AI21_API_KEY\"] = getpass.getpass(\"Enter your AI21 API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain AI21 integration lives in the `langchain-ai21` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-ai21"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_ai21 import AI21Embeddings\n",
+        "\n",
+        "embeddings = AI21Embeddings(\n",
+        "    # Can optionally increase or decrease the batch_size\n",
+        "    # to improve latency.\n",
+        "    # Use larger batch sizes with smaller documents, and\n",
+        "    # smaller batch sizes with larger documents.\n",
+        "    # batch_size=256,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.01913362182676792, 0.004960147198289633, -0.01582135073840618, -0.042474791407585144, 0.040200788\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.03029559925198555, 0.002908500377088785, -0.02700909972190857, -0.04616579785943031, 0.0382771529\n",
+            "[0.018214847892522812, 0.011460083536803722, -0.03329407051205635, -0.04951060563325882, 0.032756105\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/azureopenai.ipynb
+++ b/docs/docs/integrations/text_embedding/azureopenai.ipynb
@@ -1,280 +1,278 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: AzureOpenAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# AzureOpenAIEmbeddings\n",
-    "\n",
-    "This will help you get started with AzureOpenAI embedding models using LangChain. For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"AzureOpenAI\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access AzureOpenAI embedding models you'll need to create an Azure account, get an API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "You’ll need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this [guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).\n",
-    "\n",
-    "Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the “Keys and Endpoint” section of your instance.\n",
-    "\n",
-    "```bash\n",
-    "AZURE_OPENAI_ENDPOINT=<YOUR API ENDPOINT>\n",
-    "AZURE_OPENAI_API_KEY=<YOUR_KEY>\n",
-    "AZURE_OPENAI_API_VERSION=\"2024-02-01\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"AZURE_OPENAI_API_KEY\"):\n",
-    "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your AzureOpenAI API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain AzureOpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import AzureOpenAIEmbeddings\n",
-    "\n",
-    "embeddings = AzureOpenAIEmbeddings(\n",
-    "    model=\"text-embedding-3-large\",\n",
-    "    # dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models\n",
-    "    # azure_endpoint=\"https://<your-endpoint>.openai.azure.com/\", If not provided, will read env variable AZURE_OPENAI_ENDPOINT\n",
-    "    # api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY\n",
-    "    # openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: AzureOpenAI\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.0011676070280373096, 0.007125577889382839, -0.014674457721412182, -0.034061674028635025, 0.01128\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# AzureOpenAIEmbeddings\n",
+        "\n",
+        "This will help you get started with AzureOpenAI embedding models using LangChain. For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"AzureOpenAI\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access AzureOpenAI embedding models you'll need to create an Azure account, get an API key, and install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "You’ll need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following this [guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).\n",
+        "\n",
+        "Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the “Keys and Endpoint” section of your instance.\n",
+        "\n",
+        "```bash\n",
+        "AZURE_OPENAI_ENDPOINT=<YOUR API ENDPOINT>\n",
+        "AZURE_OPENAI_API_KEY=<YOUR_KEY>\n",
+        "AZURE_OPENAI_API_VERSION=\"2024-02-01\"\n",
+        "```"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.0011966148158535361, 0.007160289213061333, -0.014659193344414234, -0.03403077274560928, 0.011280\n",
-      "[-0.005595256108790636, 0.016757294535636902, -0.011055258102715015, -0.031094247475266457, -0.00363\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"AZURE_OPENAI_API_KEY\"):\n",
+        "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your AzureOpenAI API key: \"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain AzureOpenAI integration lives in the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import AzureOpenAIEmbeddings\n",
+        "\n",
+        "embeddings = AzureOpenAIEmbeddings(\n",
+        "    model=\"text-embedding-3-large\",\n",
+        "    # dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models\n",
+        "    # azure_endpoint=\"https://<your-endpoint>.openai.azure.com/\", If not provided, will read env variable AZURE_OPENAI_ENDPOINT\n",
+        "    # api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY\n",
+        "    # openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.0011676070280373096, 0.007125577889382839, -0.014674457721412182, -0.034061674028635025, 0.01128\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.0011966148158535361, 0.007160289213061333, -0.014659193344414234, -0.03403077274560928, 0.011280\n",
+            "[-0.005595256108790636, 0.016757294535636902, -0.011055258102715015, -0.031094247475266457, -0.00363\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/cohere.ipynb
+++ b/docs/docs/integrations/text_embedding/cohere.ipynb
@@ -1,267 +1,265 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Cohere\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# CohereEmbeddings\n",
-    "\n",
-    "This will help you get started with Cohere embedding models using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"Cohere\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Cohere embedding models you'll need to create a/an Cohere account, get an API key, and install the `langchain-cohere` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "\n",
-    "Head to [cohere.com](https://cohere.com) to sign up to Cohere and generate an API key. Once you’ve done this set the COHERE_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"COHERE_API_KEY\"):\n",
-    "    os.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Enter your Cohere API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Cohere integration lives in the `langchain-cohere` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-cohere"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_cohere import CohereEmbeddings\n",
-    "\n",
-    "embeddings = CohereEmbeddings(\n",
-    "    model=\"embed-english-v3.0\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Cohere\n",
+        "---"
      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.022979736, -0.030212402, -0.08886719, -0.08569336, 0.007030487, -0.0010671616, -0.033813477, 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# CohereEmbeddings\n",
+        "\n",
+        "This will help you get started with Cohere embedding models using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"Cohere\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Cohere embedding models you'll need to create a/an Cohere account, get an API key, and install the `langchain-cohere` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "\n",
+        "Head to [cohere.com](https://cohere.com) to sign up to Cohere and generate an API key. Once you’ve done this set the COHERE_API_KEY environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.028869629, -0.030410767, -0.099121094, -0.07116699, -0.012748718, -0.0059432983, -0.04360962, 0.\n",
-      "[-0.047332764, -0.049957275, -0.07458496, -0.034332275, -0.057922363, -0.0112838745, -0.06994629, 0.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"COHERE_API_KEY\"):\n",
+        "    os.environ[\"COHERE_API_KEY\"] = getpass.getpass(\"Enter your Cohere API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Cohere integration lives in the `langchain-cohere` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-cohere"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_cohere import CohereEmbeddings\n",
+        "\n",
+        "embeddings = CohereEmbeddings(\n",
+        "    model=\"embed-english-v3.0\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.022979736, -0.030212402, -0.08886719, -0.08569336, 0.007030487, -0.0010671616, -0.033813477, 0.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.028869629, -0.030410767, -0.099121094, -0.07116699, -0.012748718, -0.0059432983, -0.04360962, 0.\n",
+            "[-0.047332764, -0.049957275, -0.07458496, -0.034332275, -0.057922363, -0.0112838745, -0.06994629, 0.\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/fireworks.ipynb
+++ b/docs/docs/integrations/text_embedding/fireworks.ipynb
@@ -1,267 +1,265 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Fireworks\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# FireworksEmbeddings\n",
-    "\n",
-    "This will help you get started with Fireworks embedding models using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"Fireworks\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Fireworks embedding models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [fireworks.ai](https://fireworks.ai/) to sign up to Fireworks and generate an API key. Once you’ve done this set the FIREWORKS_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"FIREWORKS_API_KEY\"):\n",
-    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Fireworks integration lives in the `langchain-fireworks` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-fireworks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_fireworks import FireworksEmbeddings\n",
-    "\n",
-    "embeddings = FireworksEmbeddings(\n",
-    "    model=\"nomic-ai/nomic-embed-text-v1.5\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Fireworks\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.01666259765625, 0.011688232421875, -0.1181640625, -0.10205078125, 0.05438232421875, -0.0890502929\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# FireworksEmbeddings\n",
+        "\n",
+        "This will help you get started with Fireworks embedding models using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"Fireworks\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Fireworks embedding models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [fireworks.ai](https://fireworks.ai/) to sign up to Fireworks and generate an API key. Once you’ve done this set the FIREWORKS_API_KEY environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.016632080078125, 0.01165008544921875, -0.1181640625, -0.10186767578125, 0.05438232421875, -0.0890\n",
-      "[-0.02667236328125, 0.036651611328125, -0.1630859375, -0.0904541015625, -0.022430419921875, -0.09545\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"FIREWORKS_API_KEY\"):\n",
+        "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Fireworks integration lives in the `langchain-fireworks` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-fireworks"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_fireworks import FireworksEmbeddings\n",
+        "\n",
+        "embeddings = FireworksEmbeddings(\n",
+        "    model=\"nomic-ai/nomic-embed-text-v1.5\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.01666259765625, 0.011688232421875, -0.1181640625, -0.10205078125, 0.05438232421875, -0.0890502929\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.016632080078125, 0.01165008544921875, -0.1181640625, -0.10186767578125, 0.05438232421875, -0.0890\n",
+            "[-0.02667236328125, 0.036651611328125, -0.1630859375, -0.0904541015625, -0.022430419921875, -0.09545\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3fba556a-b53d-431c-b0c6-ffb1e2fa5a6e",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation of all `FireworksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "3fba556a-b53d-431c-b0c6-ffb1e2fa5a6e",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation of all `FireworksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
@@ -1,323 +1,321 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Google Vertex AI \n",
-    "keywords: [Vertex AI, vertexai , Google Cloud, embeddings]\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# Google Vertex AI Embeddings \n",
-    "\n",
-    "This will help you get started with Google Vertex AI Embeddings models using LangChain. For detailed documentation on `Google Vertex AI Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Provider | Package |\n",
-    "|:--------:|:-------:|\n",
-    "| [Google](https://python.langchain.com/docs/integrations/providers/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Google Vertex AI Embeddings models you'll need to \n",
-    "- Create a Google Cloud account \n",
-    "- Install the `langchain-google-vertexai` integration package.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "\n",
-    "Head to [Google Cloud](https://cloud.google.com/free/) to sign up to create an account. Once you've done this set the GOOGLE_APPLICATION_CREDENTIALS environment variable:\n",
-    "\n",
-    "For more information, see:\n",
-    "\n",
-    "https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
-    "https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caaba519-3476-423b-a5e4-d99a10929506",
-   "metadata": {},
-   "source": [
-    "**OPTIONAL : Authenticate your notebook environment (Colab only)**\n",
-    "\n",
-    "If you're running this notebook on Google Colab, run the cell below to authenticate your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b0770000-3667-439b-8c46-acc5af7c8e40",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "\n",
-    "if \"google.colab\" in sys.modules:\n",
-    "    from google.colab import auth\n",
-    "\n",
-    "    auth.authenticate_user()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fbd4a33-2480-4ad1-8d56-aec730b3662b",
-   "metadata": {},
-   "source": [
-    "**Set Google Cloud project information and initialize Vertex AI SDK**\n",
-    "\n",
-    "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
-    "\n",
-    "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
-    "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
-    "\n",
-    "import vertexai\n",
-    "\n",
-    "vertexai.init(project=PROJECT_ID, location=LOCATION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Google Vertex AI Embeddings integration lives in the `langchain-google-vertexai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-google-vertexai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate embeddings:\n",
-    ">Check the list of [Supported Models](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_vertexai import VertexAIEmbeddings\n",
-    "\n",
-    "# Initialize the a specific Embeddings Model version\n",
-    "embeddings = VertexAIEmbeddings(model_name=\"text-embedding-004\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Google Vertex AI\n",
+        "keywords: [Vertex AI, vertexai , Google Cloud, embeddings]\n",
+        "---"
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.02831101417541504, 0.022063178941607475, -0.07454229146242142, 0.006448323838412762, 0.001955120\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# Google Vertex AI Embeddings\n",
+        "\n",
+        "This will help you get started with Google Vertex AI Embeddings models using LangChain. For detailed documentation on `Google Vertex AI Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Provider | Package |\n",
+        "|:--------:|:-------:|\n",
+        "| [Google](https://python.langchain.com/docs/integrations/providers/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Google Vertex AI Embeddings models you'll need to\n",
+        "- Create a Google Cloud account\n",
+        "- Install the `langchain-google-vertexai` integration package.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "\n",
+        "Head to [Google Cloud](https://cloud.google.com/free/) to sign up to create an account. Once you've done this set the GOOGLE_APPLICATION_CREDENTIALS environment variable:\n",
+        "\n",
+        "For more information, see:\n",
+        "\n",
+        "https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+        "https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.01092718355357647, 0.01213780976831913, -0.05650627985596657, 0.006737854331731796, 0.0085973171\n",
-      "[0.010135706514120102, 0.01234869472682476, -0.07284046709537506, 0.00027134662377648056, 0.01546290\n"
-     ]
+      "cell_type": "markdown",
+      "id": "caaba519-3476-423b-a5e4-d99a10929506",
+      "metadata": {},
+      "source": [
+        "**OPTIONAL : Authenticate your notebook environment (Colab only)**\n",
+        "\n",
+        "If you're running this notebook on Google Colab, run the cell below to authenticate your environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b0770000-3667-439b-8c46-acc5af7c8e40",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9fbd4a33-2480-4ad1-8d56-aec730b3662b",
+      "metadata": {},
+      "source": [
+        "**Set Google Cloud project information and initialize Vertex AI SDK**\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
+        "\n",
+        "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
+        "\n",
+        "import vertexai\n",
+        "\n",
+        "vertexai.init(project=PROJECT_ID, location=LOCATION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Google Vertex AI Embeddings integration lives in the `langchain-google-vertexai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-google-vertexai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate embeddings:\n",
+        ">Check the list of [Supported Models](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_google_vertexai import VertexAIEmbeddings\n",
+        "\n",
+        "# Initialize the a specific Embeddings Model version\n",
+        "embeddings = VertexAIEmbeddings(model_name=\"text-embedding-004\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.02831101417541504, 0.022063178941607475, -0.07454229146242142, 0.006448323838412762, 0.001955120\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.01092718355357647, 0.01213780976831913, -0.05650627985596657, 0.006737854331731796, 0.0085973171\n",
+            "[0.010135706514120102, 0.01234869472682476, -0.07284046709537506, 0.00027134662377648056, 0.01546290\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `Google Vertex AI Embeddings\n",
+        "` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "environment": {
+      "kernel": "python310",
+      "name": "tf2-gpu.2-6.m104",
+      "type": "gcloud",
+      "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m104"
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.6"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `Google Vertex AI Embeddings\n",
-    "` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "environment": {
-   "kernel": "python310",
-   "name": "tf2-gpu.2-6.m104",
-   "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m104"
-  },
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/mistralai.ipynb
+++ b/docs/docs/integrations/text_embedding/mistralai.ipynb
@@ -1,266 +1,264 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: MistralAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# MistralAIEmbeddings\n",
-    "\n",
-    "This will help you get started with MistralAI embedding models using LangChain. For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"MistralAI\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access MistralAI embedding models you'll need to create a/an MistralAI account, get an API key, and install the `langchain-mistralai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [https://console.mistral.ai/](https://console.mistral.ai/) to sign up to MistralAI and generate an API key. Once you've done this set the MISTRALAI_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"MISTRALAI_API_KEY\"):\n",
-    "    os.environ[\"MISTRALAI_API_KEY\"] = getpass.getpass(\"Enter your MistralAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain MistralAI integration lives in the `langchain-mistralai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-mistralai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_mistralai import MistralAIEmbeddings\n",
-    "\n",
-    "embeddings = MistralAIEmbeddings(\n",
-    "    model=\"mistral-embed\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: MistralAI\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.04443359375, 0.01885986328125, 0.018035888671875, -0.00864410400390625, 0.049652099609375, -0.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# MistralAIEmbeddings\n",
+        "\n",
+        "This will help you get started with MistralAI embedding models using LangChain. For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"MistralAI\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access MistralAI embedding models you'll need to create a/an MistralAI account, get an API key, and install the `langchain-mistralai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [https://console.mistral.ai/](https://console.mistral.ai/) to sign up to MistralAI and generate an API key. Once you've done this set the MISTRALAI_API_KEY environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.04443359375, 0.01885986328125, 0.0180511474609375, -0.0086517333984375, 0.049652099609375, -0.00\n",
-      "[-0.02032470703125, 0.02606201171875, 0.051605224609375, -0.0281982421875, 0.055755615234375, 0.0019\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"MISTRALAI_API_KEY\"):\n",
+        "    os.environ[\"MISTRALAI_API_KEY\"] = getpass.getpass(\"Enter your MistralAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain MistralAI integration lives in the `langchain-mistralai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-mistralai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_mistralai import MistralAIEmbeddings\n",
+        "\n",
+        "embeddings = MistralAIEmbeddings(\n",
+        "    model=\"mistral-embed\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.04443359375, 0.01885986328125, 0.018035888671875, -0.00864410400390625, 0.049652099609375, -0.00\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.04443359375, 0.01885986328125, 0.0180511474609375, -0.0086517333984375, 0.049652099609375, -0.00\n",
+            "[-0.02032470703125, 0.02606201171875, 0.051605224609375, -0.0281982421875, 0.055755615234375, 0.0019\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/netmind.ipynb
+++ b/docs/docs/integrations/text_embedding/netmind.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Netmind\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3d6f34",
+   "metadata": {},
+   "source": [
+    "# NetmindEmbeddings\n",
+    "\n",
+    "This will help you get started with Netmind embedding models using LangChain. For detailed documentation on `NetmindEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [Netmind](/docs/integrations/providers/netmind/) | [langchain-netmind](https://python.langchain.com/api_reference/) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Netmind embedding models you'll need to create a/an Netmind account, get an API key, and install the `langchain-netmind` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "36521c2a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:53:29.982962Z",
+     "start_time": "2025-03-20T01:53:27.764291Z"
+    }
+   },
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NETMIND_API_KEY\"):\n",
+    "    os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84fb993",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "39a4953b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:53:32.143687Z",
+     "start_time": "2025-03-20T01:53:32.141858Z"
+    }
+   },
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Netmind integration lives in the `langchain-netmind` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "64853226",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:53:38.639440Z",
+     "start_time": "2025-03-20T01:53:36.171640Z"
+    }
+   },
+   "source": [
+    "%pip install -qU langchain-netmind"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.0.1\u001B[0m\r\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "9ea7a09b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:54:31.005334Z",
+     "start_time": "2025-03-20T01:54:30.146876Z"
+    }
+   },
+   "source": [
+    "from langchain_netmind import NetmindEmbeddings\n",
+    "\n",
+    "embeddings = NetmindEmbeddings(\n",
+    "    model=\"nvidia/NV-Embed-v2\",\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d271b6",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d817716b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:54:40.963137Z",
+     "start_time": "2025-03-20T01:54:34.500805Z"
+    }
+   },
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LangChain is the framework for building context-aware reasoning applications'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0d2befcd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:54:49.540750Z",
+     "start_time": "2025-03-20T01:54:45.196528Z"
+    }
+   },
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2f4d6e97",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T01:54:57.089847Z",
+     "start_time": "2025-03-20T01:54:52.468719Z"
+    }
+   },
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146\n",
+      "[0.022523142397403717, -0.002223758026957512, -0.008578270673751831, -0.006029821466654539, 0.008752\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `NetmindEmbeddings` features and configuration options, please refer to the:  \n",
+    "* [API reference](https://python.langchain.com/api_reference/)  \n",
+    "* [langchain-netmind](https://github.com/protagolabs/langchain-netmind)  \n",
+    "* [pypi](https://pypi.org/project/langchain-netmind/)\n"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "adb9e45c34733299"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/docs/integrations/text_embedding/nomic.ipynb
+++ b/docs/docs/integrations/text_embedding/nomic.ipynb
@@ -1,287 +1,285 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Nomic\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# NomicEmbeddings\n",
-    "\n",
-    "This will help you get started with Nomic embedding models using LangChain. For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"Nomic\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Nomic embedding models you'll need to create a/an Nomic account, get an API key, and install the `langchain-nomic` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [https://atlas.nomic.ai/](https://atlas.nomic.ai/) to sign up to Nomic and generate an API key. Once you've done this set the `NOMIC_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"NOMIC_API_KEY\"):\n",
-    "    os.environ[\"NOMIC_API_KEY\"] = getpass.getpass(\"Enter your Nomic API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Nomic integration lives in the `langchain-nomic` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-nomic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_nomic import NomicEmbeddings\n",
-    "\n",
-    "embeddings = NomicEmbeddings(\n",
-    "    model=\"nomic-embed-text-v1.5\",\n",
-    "    # dimensionality=256,\n",
-    "    # Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka)\n",
-    "    # to enable variable-length embeddings with a single model.\n",
-    "    # This means that you can specify the dimensionality of the embeddings at inference time.\n",
-    "    # The model supports dimensionality from 64 to 768.\n",
-    "    # inference_mode=\"remote\",\n",
-    "    # One of `remote`, `local` (Embed4All), or `dynamic` (automatic). Defaults to `remote`.\n",
-    "    # api_key=... , # if using remote inference,\n",
-    "    # device=\"cpu\",\n",
-    "    # The device to use for local embeddings. Choices include\n",
-    "    # `cpu`, `gpu`, `nvidia`, `amd`, or a specific device name. See\n",
-    "    # the docstring for `GPT4All.__init__` for more info. Typically\n",
-    "    # defaults to CPU. Do not use on macOS.\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Nomic\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.024642944, 0.029083252, -0.14013672, -0.09082031, 0.058898926, -0.07489014, -0.0138168335, 0.0037\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# NomicEmbeddings\n",
+        "\n",
+        "This will help you get started with Nomic embedding models using LangChain. For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"Nomic\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Nomic embedding models you'll need to create a/an Nomic account, get an API key, and install the `langchain-nomic` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [https://atlas.nomic.ai/](https://atlas.nomic.ai/) to sign up to Nomic and generate an API key. Once you've done this set the `NOMIC_API_KEY` environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.012771606, 0.023727417, -0.12365723, -0.083740234, 0.06530762, -0.07110596, -0.021896362, -0.0068\n",
-      "[-0.019058228, 0.04058838, -0.15222168, -0.06842041, -0.012130737, -0.07128906, -0.04534912, 0.00522\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"NOMIC_API_KEY\"):\n",
+        "    os.environ[\"NOMIC_API_KEY\"] = getpass.getpass(\"Enter your Nomic API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Nomic integration lives in the `langchain-nomic` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-nomic"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_nomic import NomicEmbeddings\n",
+        "\n",
+        "embeddings = NomicEmbeddings(\n",
+        "    model=\"nomic-embed-text-v1.5\",\n",
+        "    # dimensionality=256,\n",
+        "    # Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka)\n",
+        "    # to enable variable-length embeddings with a single model.\n",
+        "    # This means that you can specify the dimensionality of the embeddings at inference time.\n",
+        "    # The model supports dimensionality from 64 to 768.\n",
+        "    # inference_mode=\"remote\",\n",
+        "    # One of `remote`, `local` (Embed4All), or `dynamic` (automatic). Defaults to `remote`.\n",
+        "    # api_key=... , # if using remote inference,\n",
+        "    # device=\"cpu\",\n",
+        "    # The device to use for local embeddings. Choices include\n",
+        "    # `cpu`, `gpu`, `nvidia`, `amd`, or a specific device name. See\n",
+        "    # the docstring for `GPT4All.__init__` for more info. Typically\n",
+        "    # defaults to CPU. Do not use on macOS.\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.024642944, 0.029083252, -0.14013672, -0.09082031, 0.058898926, -0.07489014, -0.0138168335, 0.0037\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.012771606, 0.023727417, -0.12365723, -0.083740234, 0.06530762, -0.07110596, -0.021896362, -0.0068\n",
+            "[-0.019058228, 0.04058838, -0.15222168, -0.06842041, -0.012130737, -0.07128906, -0.04534912, 0.00522\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/ollama.ipynb
+++ b/docs/docs/integrations/text_embedding/ollama.ipynb
@@ -1,276 +1,274 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Ollama\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# OllamaEmbeddings\n",
-    "\n",
-    "This will help you get started with Ollama embedding models using LangChain. For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"Ollama\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
-    "\n",
-    "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
-    "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
-    "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
-    "    * e.g., `ollama pull llama3`\n",
-    "* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
-    "\n",
-    "> On Mac, the models will be download to `~/.ollama/models`\n",
-    "> \n",
-    "> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
-    "\n",
-    "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
-    "* To view all pulled models, use `ollama list`\n",
-    "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
-    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
-    "\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "There is no built-in auth mechanism for Ollama."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Ollama integration lives in the `langchain-ollama` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-ollama"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate embeddings:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_ollama import OllamaEmbeddings\n",
-    "\n",
-    "embeddings = OllamaEmbeddings(\n",
-    "    model=\"llama3\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Ollama\n",
+        "---"
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.001288981, 0.006547121, 0.018376578, 0.025603496, 0.009599175, -0.0042578303, -0.023250086, -0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# OllamaEmbeddings\n",
+        "\n",
+        "This will help you get started with Ollama embedding models using LangChain. For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"Ollama\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
+        "\n",
+        "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
+        "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
+        "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
+        "    * e.g., `ollama pull llama3`\n",
+        "* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
+        "\n",
+        "> On Mac, the models will be download to `~/.ollama/models`\n",
+        ">\n",
+        "> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
+        "\n",
+        "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
+        "* To view all pulled models, use `ollama list`\n",
+        "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
+        "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
+        "\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "There is no built-in auth mechanism for Ollama."
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.0013138362, 0.006438795, 0.018304596, 0.025530428, 0.009717592, -0.004225636, -0.023363983, -0.0\n",
-      "[-0.010317663, 0.01632489, 0.0070348927, 0.017076202, 0.008924255, 0.007399284, -0.023064945, -0.003\n"
-     ]
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Ollama integration lives in the `langchain-ollama` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-ollama"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_ollama import OllamaEmbeddings\n",
+        "\n",
+        "embeddings = OllamaEmbeddings(\n",
+        "    model=\"llama3\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.001288981, 0.006547121, 0.018376578, 0.025603496, 0.009599175, -0.0042578303, -0.023250086, -0.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.0013138362, 0.006438795, 0.018304596, 0.025530428, 0.009717592, -0.004225636, -0.023363983, -0.0\n",
+            "[-0.010317663, 0.01632489, 0.0070348927, 0.017076202, 0.008924255, 0.007399284, -0.023064945, -0.003\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/openai.ipynb
+++ b/docs/docs/integrations/text_embedding/openai.ipynb
@@ -1,272 +1,270 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: OpenAI\n",
-    "keywords: [openaiembeddings]\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# OpenAIEmbeddings\n",
-    "\n",
-    "This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"OpenAI\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access OpenAI embedding models you'll need to create a/an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [platform.openai.com](https://platform.openai.com) to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"OPENAI_API_KEY\"):\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain OpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import OpenAIEmbeddings\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings(\n",
-    "    model=\"text-embedding-3-large\",\n",
-    "    # With the `text-embedding-3` class\n",
-    "    # of models, you can specify the size\n",
-    "    # of the embeddings you want returned.\n",
-    "    # dimensions=1024\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: OpenAI\n",
+        "keywords: [openaiembeddings]\n",
+        "---"
      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.019276829436421394, 0.0037708976306021214, -0.03294256329536438, 0.0037671267054975033, 0.008175\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# OpenAIEmbeddings\n",
+        "\n",
+        "This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n",
+        "\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"OpenAI\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access OpenAI embedding models you'll need to create a/an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [platform.openai.com](https://platform.openai.com) to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.019260549917817116, 0.0037612367887049913, -0.03291035071015358, 0.003757466096431017, 0.0082049\n",
-      "[-0.010181212797760963, 0.023419594392180443, -0.04215526953339577, -0.001532090245746076, -0.023573\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"OPENAI_API_KEY\"):\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain OpenAI integration lives in the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import OpenAIEmbeddings\n",
+        "\n",
+        "embeddings = OpenAIEmbeddings(\n",
+        "    model=\"text-embedding-3-large\",\n",
+        "    # With the `text-embedding-3` class\n",
+        "    # of models, you can specify the size\n",
+        "    # of the embeddings you want returned.\n",
+        "    # dimensions=1024\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.019276829436421394, 0.0037708976306021214, -0.03294256329536438, 0.0037671267054975033, 0.008175\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.019260549917817116, 0.0037612367887049913, -0.03291035071015358, 0.003757466096431017, 0.0082049\n",
+            "[-0.010181212797760963, 0.023419594392180443, -0.04215526953339577, -0.001532090245746076, -0.023573\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/sambanova.ipynb
+++ b/docs/docs/integrations/text_embedding/sambanova.ipynb
@@ -10,7 +10,7 @@
   },
   "source": [
    "---\n",
-    "sidebar_label: SambaStudio\n",
+    "sidebar_label: SambaNovaCloud\n",
    "---"
   ]
  },
@@ -19,11 +19,11 @@
   "id": "9a3d6f34",
   "metadata": {},
   "source": [
-    "# SambaStudioEmbeddings\n",
+    "# SambaNovaCloudEmbeddings\n",
    "\n",
-    "This will help you get started with SambaNova's SambaStudio embedding models using LangChain. For detailed documentation on `SambaStudioEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
+    "This will help you getting started with SambaNovaCloud embedding models using LangChain. For detailed documentation on `SambaNovaCloudEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
    "\n",
-    "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n",
+    "**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
    "\n",
    "## Overview\n",
    "### Integration details\n",
@@ -34,7 +34,7 @@
    "\n",
    "## Setup\n",
    "\n",
-    "To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_sambanova` integration package.\n",
+    "To access ChatSambaNovaCloud models you will need to create a [SambaNovaCloud](https://cloud.sambanova.ai/) account, get an API key, install the `langchain_sambanova` integration package.\n",
    "\n",
    "```bash\n",
    "pip install langchain-sambanova\n",
@@ -42,11 +42,10 @@
    "\n",
    "### Credentials\n",
    "\n",
-    "Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
+    "Get an API Key from [cloud.sambanova.ai](https://cloud.sambanova.ai/apis) and add it to your environment variables:\n",
    "\n",
    "``` bash\n",
-    "export SAMBASTUDIO_URL=\"sambastudio-url-key-here\"\n",
-    "export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
+    "export SAMBANOVA_API_KEY=\"your-api-key-here\"\n",
    "```"
   ]
  },
@@ -60,10 +59,8 @@
    "import getpass\n",
    "import os\n",
    "\n",
-    "if not os.getenv(\"SAMBASTUDIO_API_KEY\"):\n",
-    "    os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your SambaNova API key: \"\n",
-    "    )"
+    "if not os.getenv(\"SAMBANOVA_API_KEY\"):\n",
+    "    os.environ[\"SAMBANOVA_API_KEY\"] = getpass.getpass(\"Enter your SambaNova API key: \")"
   ]
  },
  {
@@ -122,10 +119,10 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langchain_sambanova import SambaStudioEmbeddings\n",
+    "from langchain_sambanova import SambaNovaCloudEmbeddings\n",
    "\n",
-    "embeddings = SambaStudioEmbeddings(\n",
-    "    model=\"e5-mistral-7b-instruct\",\n",
+    "embeddings = SambaNovaCloudEmbeddings(\n",
+    "    model=\"E5-Mistral-7B-Instruct\",\n",
    ")"
   ]
  },
@@ -227,7 +224,7 @@
   "source": [
    "## API Reference\n",
    "\n",
-    "For detailed documentation on `SambaStudio` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/api-ref-landing.html).\n"
+    "For detailed documentation on `SambaNovaCloud` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n"
   ]
  }
 ],
--- a/docs/docs/integrations/text_embedding/sambastudio.ipynb
+++ b/docs/docs/integrations/text_embedding/sambastudio.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: SambaStudio\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3d6f34",
+   "metadata": {},
+   "source": [
+    "# SambaStudioEmbeddings\n",
+    "\n",
+    "This will help you get started with SambaNova's SambaStudio embedding models using LangChain. For detailed documentation on `SambaStudioEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
+    "\n",
+    "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [SambaNova](/docs/integrations/providers/sambanova/) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access SambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_sambanova` integration package.\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-sambanova\n",
+    "```\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
+    "\n",
+    "``` bash\n",
+    "export SAMBASTUDIO_URL=\"sambastudio-url-key-here\"\n",
+    "export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36521c2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"SAMBASTUDIO_URL\"):\n",
+    "    os.environ[\"SAMBASTUDIO_URL\"] = getpass.getpass(\n",
+    "        \"Enter your SambaStudio endpoint URL: \"\n",
+    "    )\n",
+    "\n",
+    "if not os.getenv(\"SAMBASTUDIO_API_KEY\"):\n",
+    "    os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your SambaStudio API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84fb993",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39a4953b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain SambaNova integration lives in the `langchain-sambanova` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64853226",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-sambanova"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ea7a09b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_sambanova import SambaStudioEmbeddings\n",
+    "\n",
+    "embeddings = SambaStudioEmbeddings(\n",
+    "    model=\"e5-mistral-7b-instruct\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d271b6",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d817716b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `SambaStudio` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/api-ref-landing.html).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/docs/integrations/text_embedding/together.ipynb
+++ b/docs/docs/integrations/text_embedding/together.ipynb
@@ -1,277 +1,275 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Together AI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# TogetherEmbeddings\n",
-    "\n",
-    "This will help you get started with Together embedding models using LangChain. For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "import { ItemTable } from \"@theme/FeatureTables\";\n",
-    "\n",
-    "<ItemTable category=\"text_embedding\" item=\"Together\" />\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Together embedding models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [https://api.together.xyz/](https://api.together.xyz/) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"TOGETHER_API_KEY\"):\n",
-    "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Together integration lives in the `langchain-together` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-together"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_together import TogetherEmbeddings\n",
-    "\n",
-    "embeddings = TogetherEmbeddings(\n",
-    "    model=\"togethercomputer/m2-bert-80M-8k-retrieval\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Together AI\n",
+        "---"
      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# TogetherEmbeddings\n",
+        "\n",
+        "This will help you get started with Together embedding models using LangChain. For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "import { ItemTable } from \"@theme/FeatureTables\";\n",
+        "\n",
+        "<ItemTable category=\"text_embedding\" item=\"Together\" />\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access Together embedding models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [https://api.together.xyz/](https://api.together.xyz/) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n",
-      "[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"TOGETHER_API_KEY\"):\n",
+        "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Together integration lives in the `langchain-together` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-together"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_together import TogetherEmbeddings\n",
+        "\n",
+        "embeddings = TogetherEmbeddings(\n",
+        "    model=\"togethercomputer/m2-bert-80M-8k-retrieval\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n",
+            "[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/text_embedding/zhipuai.ipynb
+++ b/docs/docs/integrations/text_embedding/zhipuai.ipynb
@@ -1,279 +1,277 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: ZhipuAI\n",
-    "keywords: [zhipuaiembeddings]\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# ZhipuAIEmbeddings\n",
-    "\n",
-    "This will help you get started with ZhipuAI embedding models using LangChain. For detailed documentation on `ZhipuAIEmbeddings` features and configuration options, please refer to the [API reference](https://bigmodel.cn/dev/api#vector).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Provider | Package |\n",
-    "|:--------:|:-------:|\n",
-    "| [ZhipuAI](/docs/integrations/providers/zhipuai/) | [langchain-community](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access ZhipuAI embedding models you'll need to create a/an ZhipuAI account, get an API key, and install the `zhipuai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [https://bigmodel.cn/](https://bigmodel.cn/usercenter/apikeys) to sign up to ZhipuAI and generate an API key. Once you've done this set the ZHIPUAI_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"ZHIPUAI_API_KEY\"):\n",
-    "    os.environ[\"ZHIPUAI_API_KEY\"] = getpass.getpass(\"Enter your ZhipuAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "39a4953b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain ZhipuAI integration lives in the `zhipuai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU zhipuai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.embeddings import ZhipuAIEmbeddings\n",
-    "\n",
-    "embeddings = ZhipuAIEmbeddings(\n",
-    "    model=\"embedding-3\",\n",
-    "    # With the `embedding-3` class\n",
-    "    # of models, you can specify the size\n",
-    "    # of the embeddings you want returned.\n",
-    "    # dimensions=1024\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
-    "\n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d817716b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: ZhipuAI\n",
+        "keywords: [zhipuaiembeddings]\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
-    "\n",
-    "You can directly call these methods to get embeddings for your own use cases.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "9a3d6f34",
+      "metadata": {},
+      "source": [
+        "# ZhipuAIEmbeddings\n",
+        "\n",
+        "This will help you get started with ZhipuAI embedding models using LangChain. For detailed documentation on `ZhipuAIEmbeddings` features and configuration options, please refer to the [API reference](https://bigmodel.cn/dev/api#vector).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Provider | Package |\n",
+        "|:--------:|:-------:|\n",
+        "| [ZhipuAI](/docs/integrations/providers/zhipuai/) | [langchain-community](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html) |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access ZhipuAI embedding models you'll need to create a/an ZhipuAI account, get an API key, and install the `zhipuai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [https://bigmodel.cn/](https://bigmodel.cn/usercenter/apikeys) to sign up to ZhipuAI and generate an API key. Once you've done this set the ZHIPUAI_API_KEY environment variable:"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246\n",
-      "[-0.02330017, -0.013916016, 0.00022411346, 0.017196655, -0.034240723, 0.011131287, 0.011497498, -0.0\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "36521c2a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"ZHIPUAI_API_KEY\"):\n",
+        "    os.environ[\"ZHIPUAI_API_KEY\"] = getpass.getpass(\"Enter your ZhipuAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c84fb993",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "39a4953b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d9664366",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain ZhipuAI integration lives in the `zhipuai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "64853226",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU zhipuai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "45dd1724",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "9ea7a09b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.embeddings import ZhipuAIEmbeddings\n",
+        "\n",
+        "embeddings = ZhipuAIEmbeddings(\n",
+        "    model=\"embedding-3\",\n",
+        "    # With the `embedding-3` class\n",
+        "    # of models, you can specify the size\n",
+        "    # of the embeddings you want returned.\n",
+        "    # dimensions=1024\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77d271b6",
+      "metadata": {},
+      "source": [
+        "## Indexing and Retrieval\n",
+        "\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "\n",
+        "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "d817716b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a vector store with a sample text\n",
+        "from langchain_core.vectorstores import InMemoryVectorStore\n",
+        "\n",
+        "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+        "\n",
+        "vectorstore = InMemoryVectorStore.from_texts(\n",
+        "    [text],\n",
+        "    embedding=embeddings,\n",
+        ")\n",
+        "\n",
+        "# Use the vectorstore as a retriever\n",
+        "retriever = vectorstore.as_retriever()\n",
+        "\n",
+        "# Retrieve the most similar text\n",
+        "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+        "\n",
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e02b9855",
+      "metadata": {},
+      "source": [
+        "## Direct Usage\n",
+        "\n",
+        "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+        "\n",
+        "You can directly call these methods to get embeddings for your own use cases.\n",
+        "\n",
+        "### Embed single texts\n",
+        "\n",
+        "You can embed single texts or documents with `embed_query`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "0d2befcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246\n"
+          ]
+        }
+      ],
+      "source": [
+        "single_vector = embeddings.embed_query(text)\n",
+        "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5a7d03",
+      "metadata": {},
+      "source": [
+        "### Embed multiple texts\n",
+        "\n",
+        "You can embed multiple texts with `embed_documents`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "2f4d6e97",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246\n",
+            "[-0.02330017, -0.013916016, 0.00022411346, 0.017196655, -0.034240723, 0.011131287, 0.011497498, -0.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "text2 = (\n",
+        "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+        ")\n",
+        "two_vectors = embeddings.embed_documents([text, text2])\n",
+        "for vector in two_vectors:\n",
+        "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98785c12",
+      "metadata": {},
+      "source": [
+        "## API Reference\n",
+        "\n",
+        "For detailed documentation on `ZhipuAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For detailed documentation on `ZhipuAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/tools/cassandra_database.ipynb
+++ b/docs/docs/integrations/tools/cassandra_database.ipynb
@@ -70,7 +70,7 @@
    "Gathers all schema information for the connected database or a specific schema. Critical for the agent when determining actions. \n",
    "\n",
    "### `cassandra_db_select_table_data`\n",
-    "Selects data from a specific keyspace and table. The agent can pass paramaters for a predicate and limits on the number of returned records. \n",
+    "Selects data from a specific keyspace and table. The agent can pass parameters for a predicate and limits on the number of returned records. \n",
    "\n",
    "### `cassandra_db_query`\n",
    "Expiriemental alternative to `cassandra_db_select_table_data` which takes a query string completely formed by the agent instead of parameters. *Warning*: This can lead to unusual queries that may not be as performant(or even work). This may be removed in future releases. If it does something cool, we want to know about that too. You never know!"
--- a/docs/docs/integrations/tools/cdp_agentkit.ipynb
+++ b/docs/docs/integrations/tools/cdp_agentkit.ipynb
@@ -1,332 +1,330 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: CDP\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: CDP\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# CDP Agentkit Toolkit\n",
+        "\n",
+        "The `CDP Agentkit` toolkit contains tools that enable an LLM agent to interact with the [Coinbase Developer Platform](https://docs.cdp.coinbase.com/). The toolkit provides a wrapper around the CDP SDK, allowing agents to perform onchain operations like transfers, trades, and smart contract interactions.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Serializable | JS support |  Package latest |\n",
+        "| :--- | :--- | :---: | :---: | :---: |\n",
+        "| CdpToolkit | `cdp-langchain` | ❌ | ❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/cdp-langchain?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Tool features\n",
+        "\n",
+        "The toolkit provides the following tools:\n",
+        "\n",
+        "1. **get_wallet_details** - Get details about the MPC Wallet\n",
+        "2. **get_balance** - Get balance for specific assets\n",
+        "3. **request_faucet_funds** - Request test tokens from faucet\n",
+        "4. **transfer** - Transfer assets between addresses\n",
+        "5. **trade** - Trade assets (Mainnet only)\n",
+        "6. **deploy_token** - Deploy ERC-20 token contracts\n",
+        "7. **mint_nft** - Mint NFTs from existing contracts\n",
+        "8. **deploy_nft** - Deploy new NFT contracts\n",
+        "9. **register_basename** - Register a basename for the wallet\n",
+        "\n",
+        "We encourage you to add your own tools, both using CDP and web2 APIs, to create an agent that is tailored to your needs.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "At a high-level, we will:\n",
+        "\n",
+        "1. Install the langchain package\n",
+        "2. Set up your CDP API credentials\n",
+        "3. Initialize the CDP wrapper and toolkit\n",
+        "4. Pass the tools to your agent with `toolkit.get_tools()`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b",
+      "metadata": {},
+      "source": "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "This toolkit lives in the `cdp-langchain` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU cdp-langchain"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65",
+      "metadata": {},
+      "source": [
+        "#### Set Environment Variables\n",
+        "\n",
+        "To use this toolkit, you must first set the following environment variables to access the [CDP APIs](https://docs.cdp.coinbase.com/mpc-wallet/docs/quickstart) to create wallets and interact onchain. You can sign up for an API key for free on the [CDP Portal](https://cdp.coinbase.com/):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "for env_var in [\n",
+        "    \"CDP_API_KEY_NAME\",\n",
+        "    \"CDP_API_KEY_PRIVATE_KEY\",\n",
+        "]:\n",
+        "    if not os.getenv(env_var):\n",
+        "        os.environ[env_var] = getpass.getpass(f\"Enter your {env_var}: \")\n",
+        "\n",
+        "# Optional: Set network (defaults to base-sepolia)\n",
+        "os.environ[\"NETWORK_ID\"] = \"base-sepolia\"  # or \"base-mainnet\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5c5f2839",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our toolkit:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "51a60dbe",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from cdp_langchain.agent_toolkits import CdpToolkit\n",
+        "from cdp_langchain.utils import CdpAgentkitWrapper\n",
+        "\n",
+        "# Initialize CDP wrapper\n",
+        "cdp = CdpAgentkitWrapper()\n",
+        "\n",
+        "# Create toolkit from wrapper\n",
+        "toolkit = CdpToolkit.from_cdp_agentkit_wrapper(cdp)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d11245ad",
+      "metadata": {},
+      "source": [
+        "## Tools\n",
+        "\n",
+        "View [available tools](#tool-features):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "310bf18e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tools = toolkit.get_tools()\n",
+        "for tool in tools:\n",
+        "    print(tool.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "23e11cc9",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "We will need a LLM or chat model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d1ee55bc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import ChatOpenAI\n",
+        "\n",
+        "llm = ChatOpenAI(model=\"gpt-4o-mini\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca",
+      "metadata": {},
+      "source": [
+        "Initialize the agent with the tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f8a2c4b1",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "tools = toolkit.get_tools()\n",
+        "agent_executor = create_react_agent(llm, tools)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b4a7c9d2",
+      "metadata": {},
+      "source": [
+        "Example usage:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c9a8e4f3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "example_query = \"Send 0.005 ETH to john2879.base.eth\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e5a7c9d4",
+      "metadata": {},
+      "source": [
+        "Expected output:\n",
+        "```\n",
+        "Transferred 0.005 of eth to john2879.base.eth.\n",
+        "Transaction hash for the transfer: 0x78c7c2878659a0de216d0764fc87eff0d38b47f3315fa02ba493a83d8e782d1e\n",
+        "Transaction link for the transfer: https://sepolia.basescan.org/tx/0x78c7c2878659a0de216d0764fc87eff0d38b47f3315fa02ba493a83d8e782d1\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f5a7c9d5",
+      "metadata": {},
+      "source": [
+        "## CDP Toolkit Specific Features\n",
+        "\n",
+        "### Wallet Management\n",
+        "\n",
+        "The toolkit maintains an MPC wallet. The wallet data can be exported and imported to persist between sessions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "g5a7c9d6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Export wallet data\n",
+        "wallet_data = cdp.export_wallet()\n",
+        "\n",
+        "# Import wallet data\n",
+        "values = {\"cdp_wallet_data\": wallet_data}\n",
+        "cdp = CdpAgentkitWrapper(**values)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "h5a7c9d7",
+      "metadata": {},
+      "source": [
+        "### Network Support\n",
+        "\n",
+        "The toolkit supports [multiple networks](https://docs.cdp.coinbase.com/cdp-sdk/docs/networks)\n",
+        "\n",
+        "### Gasless Transactions\n",
+        "\n",
+        "Some operations support gasless transactions on Base Mainnet:\n",
+        "- USDC transfers\n",
+        "- EURC transfers\n",
+        "- cbBTC transfers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "i5a7c9d8",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all CDP features and configurations head to the [CDP docs](https://docs.cdp.coinbase.com/mpc-wallet/docs/welcome)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# CDP Agentkit Toolkit\n",
-    "\n",
-    "The `CDP Agentkit` toolkit contains tools that enable an LLM agent to interact with the [Coinbase Developer Platform](https://docs.cdp.coinbase.com/). The toolkit provides a wrapper around the CDP SDK, allowing agents to perform onchain operations like transfers, trades, and smart contract interactions.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Serializable | JS support |  Package latest |\n",
-    "| :--- | :--- | :---: | :---: | :---: |\n",
-    "| CdpToolkit | `cdp-langchain` | ❌ | ❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/cdp-langchain?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Tool features\n",
-    "\n",
-    "The toolkit provides the following tools:\n",
-    "\n",
-    "1. **get_wallet_details** - Get details about the MPC Wallet\n",
-    "2. **get_balance** - Get balance for specific assets\n",
-    "3. **request_faucet_funds** - Request test tokens from faucet\n",
-    "4. **transfer** - Transfer assets between addresses\n",
-    "5. **trade** - Trade assets (Mainnet only)\n",
-    "6. **deploy_token** - Deploy ERC-20 token contracts\n",
-    "7. **mint_nft** - Mint NFTs from existing contracts\n",
-    "8. **deploy_nft** - Deploy new NFT contracts\n",
-    "9. **register_basename** - Register a basename for the wallet\n",
-    "\n",
-    "We encourage you to add your own tools, both using CDP and web2 APIs, to create an agent that is tailored to your needs.\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "At a high-level, we will:\n",
-    "\n",
-    "1. Install the langchain package\n",
-    "2. Set up your CDP API credentials\n",
-    "3. Initialize the CDP wrapper and toolkit\n",
-    "4. Pass the tools to your agent with `toolkit.get_tools()`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "This toolkit lives in the `cdp-langchain` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU cdp-langchain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65",
-   "metadata": {},
-   "source": [
-    "#### Set Environment Variables\n",
-    "\n",
-    "To use this toolkit, you must first set the following environment variables to access the [CDP APIs](https://docs.cdp.coinbase.com/mpc-wallet/docs/quickstart) to create wallets and interact onchain. You can sign up for an API key for free on the [CDP Portal](https://cdp.coinbase.com/):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "for env_var in [\n",
-    "    \"CDP_API_KEY_NAME\",\n",
-    "    \"CDP_API_KEY_PRIVATE_KEY\",\n",
-    "]:\n",
-    "    if not os.getenv(env_var):\n",
-    "        os.environ[env_var] = getpass.getpass(f\"Enter your {env_var}: \")\n",
-    "\n",
-    "# Optional: Set network (defaults to base-sepolia)\n",
-    "os.environ[\"NETWORK_ID\"] = \"base-sepolia\"  # or \"base-mainnet\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c5f2839",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our toolkit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51a60dbe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cdp_langchain.agent_toolkits import CdpToolkit\n",
-    "from cdp_langchain.utils import CdpAgentkitWrapper\n",
-    "\n",
-    "# Initialize CDP wrapper\n",
-    "cdp = CdpAgentkitWrapper()\n",
-    "\n",
-    "# Create toolkit from wrapper\n",
-    "toolkit = CdpToolkit.from_cdp_agentkit_wrapper(cdp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d11245ad",
-   "metadata": {},
-   "source": [
-    "## Tools\n",
-    "\n",
-    "View [available tools](#tool-features):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "310bf18e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = toolkit.get_tools()\n",
-    "for tool in tools:\n",
-    "    print(tool.name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23e11cc9",
-   "metadata": {},
-   "source": [
-    "## Use within an agent\n",
-    "\n",
-    "We will need a LLM or chat model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1ee55bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca",
-   "metadata": {},
-   "source": [
-    "Initialize the agent with the tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f8a2c4b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "tools = toolkit.get_tools()\n",
-    "agent_executor = create_react_agent(llm, tools)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4a7c9d2",
-   "metadata": {},
-   "source": [
-    "Example usage:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9a8e4f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "example_query = \"Send 0.005 ETH to john2879.base.eth\"\n",
-    "\n",
-    "events = agent_executor.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5a7c9d4",
-   "metadata": {},
-   "source": [
-    "Expected output:\n",
-    "```\n",
-    "Transferred 0.005 of eth to john2879.base.eth.\n",
-    "Transaction hash for the transfer: 0x78c7c2878659a0de216d0764fc87eff0d38b47f3315fa02ba493a83d8e782d1e\n",
-    "Transaction link for the transfer: https://sepolia.basescan.org/tx/0x78c7c2878659a0de216d0764fc87eff0d38b47f3315fa02ba493a83d8e782d1\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5a7c9d5",
-   "metadata": {},
-   "source": [
-    "## CDP Toolkit Specific Features\n",
-    "\n",
-    "### Wallet Management\n",
-    "\n",
-    "The toolkit maintains an MPC wallet. The wallet data can be exported and imported to persist between sessions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "g5a7c9d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Export wallet data\n",
-    "wallet_data = cdp.export_wallet()\n",
-    "\n",
-    "# Import wallet data\n",
-    "values = {\"cdp_wallet_data\": wallet_data}\n",
-    "cdp = CdpAgentkitWrapper(**values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "h5a7c9d7",
-   "metadata": {},
-   "source": [
-    "### Network Support\n",
-    "\n",
-    "The toolkit supports [multiple networks](https://docs.cdp.coinbase.com/cdp-sdk/docs/networks)\n",
-    "\n",
-    "### Gasless Transactions\n",
-    "\n",
-    "Some operations support gasless transactions on Base Mainnet:\n",
-    "- USDC transfers\n",
-    "- EURC transfers\n",
-    "- cbBTC transfers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "i5a7c9d8",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all CDP features and configurations head to the [CDP docs](https://docs.cdp.coinbase.com/mpc-wallet/docs/welcome)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/tools/github.ipynb
+++ b/docs/docs/integrations/tools/github.ipynb
@@ -1,360 +1,358 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Github Toolkit\n",
-    "\n",
-    "The `Github` toolkit contains tools that enable an LLM agent to interact with a github repository. \n",
-    "The tool is a wrapper for the [PyGitHub](https://github.com/PyGithub/PyGithub) library. \n",
-    "\n",
-    "For detailed documentation of all GithubToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html).\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "At a high-level, we will:\n",
-    "\n",
-    "1. Install the pygithub library\n",
-    "2. Create a Github app\n",
-    "3. Set your environmental variables\n",
-    "4. Pass the tools to your agent with `toolkit.get_tools()`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "#### 1. Install dependencies\n",
-    "\n",
-    "This integration is implemented in `langchain-community`. We will also need the `pygithub` dependency:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  pygithub langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2. Create a Github App\n",
-    "\n",
-    "[Follow the instructions here](https://docs.github.com/en/apps/creating-github-apps/registering-a-github-app/registering-a-github-app) to create and register a Github app. Make sure your app has the following [repository permissions:](https://docs.github.com/en/rest/overview/permissions-required-for-github-apps?apiVersion=2022-11-28)\n",
-    "\n",
-    "* Commit statuses (read only)\n",
-    "* Contents (read and write)\n",
-    "* Issues (read and write)\n",
-    "* Metadata (read only)\n",
-    "* Pull requests (read and write)\n",
-    "\n",
-    "Once the app has been registered, you must give your app permission to access each of the repositories you whish it to act upon. Use the App settings on [github.com here](https://github.com/settings/installations).\n",
-    "\n",
-    "\n",
-    "#### 3. Set Environment Variables\n",
-    "\n",
-    "Before initializing your agent, the following environment variables need to be set:\n",
-    "\n",
-    "* **GITHUB_APP_ID**- A six digit number found in your app's general settings\n",
-    "* **GITHUB_APP_PRIVATE_KEY**- The location of your app's private key .pem file, or the full text of that file as a string.\n",
-    "* **GITHUB_REPOSITORY**- The name of the Github repository you want your bot to act upon. Must follow the format \\{username\\}/\\{repo-name\\}. *Make sure the app has been added to this repository first!*\n",
-    "* Optional: **GITHUB_BRANCH**- The branch where the bot will make its commits. Defaults to `repo.default_branch`.\n",
-    "* Optional: **GITHUB_BASE_BRANCH**- The base branch of your repo upon which PRs will based from. Defaults to `repo.default_branch`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "for env_var in [\n",
-    "    \"GITHUB_APP_ID\",\n",
-    "    \"GITHUB_APP_PRIVATE_KEY\",\n",
-    "    \"GITHUB_REPOSITORY\",\n",
-    "]:\n",
-    "    if not os.getenv(env_var):\n",
-    "        os.environ[env_var] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our toolkit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit\n",
-    "from langchain_community.utilities.github import GitHubAPIWrapper\n",
-    "\n",
-    "github = GitHubAPIWrapper()\n",
-    "toolkit = GitHubToolkit.from_github_api_wrapper(github)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tools\n",
-    "\n",
-    "View available tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Get Issues\n",
-      "Get Issue\n",
-      "Comment on Issue\n",
-      "List open pull requests (PRs)\n",
-      "Get Pull Request\n",
-      "Overview of files included in PR\n",
-      "Create Pull Request\n",
-      "List Pull Requests' Files\n",
-      "Create File\n",
-      "Read File\n",
-      "Update File\n",
-      "Delete File\n",
-      "Overview of existing files in Main branch\n",
-      "Overview of files in current working branch\n",
-      "List branches in this repository\n",
-      "Set active branch\n",
-      "Create a new branch\n",
-      "Get files from a directory\n",
-      "Search issues and pull requests\n",
-      "Search code\n",
-      "Create review request\n"
-     ]
-    }
-   ],
-   "source": [
-    "tools = toolkit.get_tools()\n",
-    "\n",
-    "for tool in tools:\n",
-    "    print(tool.name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The purpose of these tools is as follows:\n",
-    "\n",
-    "Each of these steps will be explained in great detail below.\n",
-    "\n",
-    "1. **Get Issues**- fetches issues from the repository.\n",
-    "\n",
-    "2. **Get Issue**- fetches details about a specific issue.\n",
-    "\n",
-    "3. **Comment on Issue**- posts a comment on a specific issue.\n",
-    "\n",
-    "4. **Create Pull Request**- creates a pull request from the bot's working branch to the base branch.\n",
-    "\n",
-    "5. **Create File**- creates a new file in the repository.\n",
-    "\n",
-    "6. **Read File**- reads a file from the repository.\n",
-    "\n",
-    "7. **Update File**- updates a file in the repository.\n",
-    "\n",
-    "8. **Delete File**- deletes a file from the repository."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Include release tools\n",
-    "\n",
-    "By default, the toolkit does not include release-related tools. You can include them by setting `include_release_tools=True` when initializing the toolkit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "toolkit = GitHubToolkit.from_github_api_wrapper(github, include_release_tools=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Settings `include_release_tools=True` will include the following tools:\n",
-    "\n",
-    "* **Get Latest Release**- fetches the latest release from the repository.\n",
-    "\n",
-    "* **Get Releases**- fetches the latest 5 releases from the repository.\n",
-    "\n",
-    "* **Get Release**- fetches a specific release from the repository by tag name, e.g. `v1.0.0`.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use within an agent\n",
-    "\n",
-    "We will need a LLM or chat model:\n",
-    "\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize the agent with a subset of tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "tools = [tool for tool in toolkit.get_tools() if tool.name == \"Get Issue\"]\n",
-    "assert len(tools) == 1\n",
-    "tools[0].name = \"get_issue\"\n",
-    "\n",
-    "agent_executor = create_react_agent(llm, tools)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And issue it a query:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Github Toolkit\n",
+        "\n",
+        "The `Github` toolkit contains tools that enable an LLM agent to interact with a github repository. \n",
+        "The tool is a wrapper for the [PyGitHub](https://github.com/PyGithub/PyGithub) library. \n",
+        "\n",
+        "For detailed documentation of all GithubToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html).\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "At a high-level, we will:\n",
+        "\n",
+        "1. Install the pygithub library\n",
+        "2. Create a Github app\n",
+        "3. Set your environmental variables\n",
+        "4. Pass the tools to your agent with `toolkit.get_tools()`"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What is the title of issue 24888?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_issue (call_iSYJVaM7uchfNHOMJoVPQsOi)\n",
-      " Call ID: call_iSYJVaM7uchfNHOMJoVPQsOi\n",
-      "  Args:\n",
-      "    issue_number: 24888\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: get_issue\n",
-      "\n",
-      "{\"number\": 24888, \"title\": \"Standardize KV-Store Docs\", \"body\": \"To make our KV-store integrations as easy to use as possible we need to make sure the docs for them are thorough and standardized. There are two parts to this: updating the KV-store docstrings and updating the actual integration docs.\\r\\n\\r\\nThis needs to be done for each KV-store integration, ideally with one PR per KV-store.\\r\\n\\r\\nRelated to broader issues #21983 and #22005.\\r\\n\\r\\n## Docstrings\\r\\nEach KV-store class docstring should have the sections shown in the [Appendix](#appendix) below. The sections should have input and output code blocks when relevant.\\r\\n\\r\\nTo build a preview of the API docs for the package you're working on run (from root of repo):\\r\\n\\r\\n```shell\\r\\nmake api_docs_clean; make api_docs_quick_preview API_PKG=openai\\r\\n```\\r\\n\\r\\nwhere `API_PKG=` should be the parent directory that houses the edited package (e.g. community, openai, anthropic, huggingface, together, mistralai, groq, fireworks, etc.). This should be quite fast for all the partner packages.\\r\\n\\r\\n## Doc pages\\r\\nEach KV-store [docs page](https://python.langchain.com/docs/integrations/stores/) should follow [this template](https://github.com/langchain-ai/langchain/blob/master/libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb).\\r\\n\\r\\nHere is an example: https://python.langchain.com/docs/integrations/stores/in_memory/\\r\\n\\r\\nYou can use the `langchain-cli` to quickly get started with a new chat model integration docs page (run from root of repo):\\r\\n\\r\\n```shell\\r\\npoetry run pip install -e libs/cli\\r\\npoetry run langchain-cli integration create-doc --name \\\"foo-bar\\\" --name-class FooBar --component-type kv_store --destination-dir ./docs/docs/integrations/stores/\\r\\n```\\r\\n\\r\\nwhere `--name` is the integration package name without the \\\"langchain-\\\" prefix and `--name-class` is the class name without the \\\"ByteStore\\\" suffix. This will create a template doc with some autopopulated fields at docs/docs/integrations/stores/foo_bar.ipynb.\\r\\n\\r\\nTo build a preview of the docs you can run (from root):\\r\\n\\r\\n```shell\\r\\nmake docs_clean\\r\\nmake docs_build\\r\\ncd docs/build/output-new\\r\\nyarn\\r\\nyarn start\\r\\n```\\r\\n\\r\\n## Appendix\\r\\nExpected sections for the KV-store class docstring.\\r\\n\\r\\n```python\\r\\n    \\\"\\\"\\\"__ModuleName__ completion KV-store integration.\\r\\n\\r\\n    # TODO: Replace with relevant packages, env vars.\\r\\n    Setup:\\r\\n        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.\\r\\n\\r\\n        .. code-block:: bash\\r\\n\\r\\n            pip install -U __package_name__\\r\\n            export __MODULE_NAME___API_KEY=\\\"your-api-key\\\"\\r\\n\\r\\n    # TODO: Populate with relevant params.\\r\\n    Key init args \\u2014 client params:\\r\\n        api_key: Optional[str]\\r\\n            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.\\r\\n\\r\\n    See full list of supported init args and their descriptions in the params section.\\r\\n\\r\\n    # TODO: Replace with relevant init params.\\r\\n    Instantiate:\\r\\n        .. code-block:: python\\r\\n\\r\\n            from __module_name__ import __ModuleName__ByteStore\\r\\n\\r\\n            kv_store = __ModuleName__ByteStore(\\r\\n                # api_key=\\\"...\\\",\\r\\n                # other params...\\r\\n            )\\r\\n\\r\\n    Set keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_pairs = [\\r\\n                [\\\"key1\\\", \\\"value1\\\"],\\r\\n                [\\\"key2\\\", \\\"value2\\\"],\\r\\n            ]\\r\\n\\r\\n            kv_store.mset(kv_pairs)\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n    Get keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_store.mget([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n            # TODO: Example output.\\r\\n\\r\\n    Delete keys:\\r\\n        ..code-block:: python\\r\\n\\r\\n            kv_store.mdelete([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        ..code-block:: python\\r\\n    \\\"\\\"\\\"  # noqa: E501\\r\\n```\", \"comments\": \"[]\", \"opened_by\": \"jacoblee93\"}\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The title of issue 24888 is \"Standardize KV-Store Docs\".\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "#### 1. Install dependencies\n",
+        "\n",
+        "This integration is implemented in `langchain-community`. We will also need the `pygithub` dependency:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install --upgrade --quiet  pygithub langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### 2. Create a Github App\n",
+        "\n",
+        "[Follow the instructions here](https://docs.github.com/en/apps/creating-github-apps/registering-a-github-app/registering-a-github-app) to create and register a Github app. Make sure your app has the following [repository permissions:](https://docs.github.com/en/rest/overview/permissions-required-for-github-apps?apiVersion=2022-11-28)\n",
+        "\n",
+        "* Commit statuses (read only)\n",
+        "* Contents (read and write)\n",
+        "* Issues (read and write)\n",
+        "* Metadata (read only)\n",
+        "* Pull requests (read and write)\n",
+        "\n",
+        "Once the app has been registered, you must give your app permission to access each of the repositories you whish it to act upon. Use the App settings on [github.com here](https://github.com/settings/installations).\n",
+        "\n",
+        "\n",
+        "#### 3. Set Environment Variables\n",
+        "\n",
+        "Before initializing your agent, the following environment variables need to be set:\n",
+        "\n",
+        "* **GITHUB_APP_ID**- A six digit number found in your app's general settings\n",
+        "* **GITHUB_APP_PRIVATE_KEY**- The location of your app's private key .pem file, or the full text of that file as a string.\n",
+        "* **GITHUB_REPOSITORY**- The name of the Github repository you want your bot to act upon. Must follow the format \\{username\\}/\\{repo-name\\}. *Make sure the app has been added to this repository first!*\n",
+        "* Optional: **GITHUB_BRANCH**- The branch where the bot will make its commits. Defaults to `repo.default_branch`.\n",
+        "* Optional: **GITHUB_BASE_BRANCH**- The base branch of your repo upon which PRs will based from. Defaults to `repo.default_branch`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "for env_var in [\n",
+        "    \"GITHUB_APP_ID\",\n",
+        "    \"GITHUB_APP_PRIVATE_KEY\",\n",
+        "    \"GITHUB_REPOSITORY\",\n",
+        "]:\n",
+        "    if not os.getenv(env_var):\n",
+        "        os.environ[env_var] = getpass.getpass()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our toolkit:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit\n",
+        "from langchain_community.utilities.github import GitHubAPIWrapper\n",
+        "\n",
+        "github = GitHubAPIWrapper()\n",
+        "toolkit = GitHubToolkit.from_github_api_wrapper(github)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tools\n",
+        "\n",
+        "View available tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Get Issues\n",
+            "Get Issue\n",
+            "Comment on Issue\n",
+            "List open pull requests (PRs)\n",
+            "Get Pull Request\n",
+            "Overview of files included in PR\n",
+            "Create Pull Request\n",
+            "List Pull Requests' Files\n",
+            "Create File\n",
+            "Read File\n",
+            "Update File\n",
+            "Delete File\n",
+            "Overview of existing files in Main branch\n",
+            "Overview of files in current working branch\n",
+            "List branches in this repository\n",
+            "Set active branch\n",
+            "Create a new branch\n",
+            "Get files from a directory\n",
+            "Search issues and pull requests\n",
+            "Search code\n",
+            "Create review request\n"
+          ]
+        }
+      ],
+      "source": [
+        "tools = toolkit.get_tools()\n",
+        "\n",
+        "for tool in tools:\n",
+        "    print(tool.name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The purpose of these tools is as follows:\n",
+        "\n",
+        "Each of these steps will be explained in great detail below.\n",
+        "\n",
+        "1. **Get Issues**- fetches issues from the repository.\n",
+        "\n",
+        "2. **Get Issue**- fetches details about a specific issue.\n",
+        "\n",
+        "3. **Comment on Issue**- posts a comment on a specific issue.\n",
+        "\n",
+        "4. **Create Pull Request**- creates a pull request from the bot's working branch to the base branch.\n",
+        "\n",
+        "5. **Create File**- creates a new file in the repository.\n",
+        "\n",
+        "6. **Read File**- reads a file from the repository.\n",
+        "\n",
+        "7. **Update File**- updates a file in the repository.\n",
+        "\n",
+        "8. **Delete File**- deletes a file from the repository."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Include release tools\n",
+        "\n",
+        "By default, the toolkit does not include release-related tools. You can include them by setting `include_release_tools=True` when initializing the toolkit:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "toolkit = GitHubToolkit.from_github_api_wrapper(github, include_release_tools=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Settings `include_release_tools=True` will include the following tools:\n",
+        "\n",
+        "* **Get Latest Release**- fetches the latest release from the repository.\n",
+        "\n",
+        "* **Get Releases**- fetches the latest 5 releases from the repository.\n",
+        "\n",
+        "* **Get Release**- fetches a specific release from the repository by tag name, e.g. `v1.0.0`.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "We will need a LLM or chat model:\n",
+        "\n",
+        "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+        "\n",
+        "<ChatModelTabs customVarName=\"llm\" />\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# | output: false\n",
+        "# | echo: false\n",
+        "\n",
+        "from langchain_openai import ChatOpenAI\n",
+        "\n",
+        "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Initialize the agent with a subset of tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "tools = [tool for tool in toolkit.get_tools() if tool.name == \"Get Issue\"]\n",
+        "assert len(tools) == 1\n",
+        "tools[0].name = \"get_issue\"\n",
+        "\n",
+        "agent_executor = create_react_agent(llm, tools)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And issue it a query:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "What is the title of issue 24888?\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  get_issue (call_iSYJVaM7uchfNHOMJoVPQsOi)\n",
+            " Call ID: call_iSYJVaM7uchfNHOMJoVPQsOi\n",
+            "  Args:\n",
+            "    issue_number: 24888\n",
+            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+            "Name: get_issue\n",
+            "\n",
+            "{\"number\": 24888, \"title\": \"Standardize KV-Store Docs\", \"body\": \"To make our KV-store integrations as easy to use as possible we need to make sure the docs for them are thorough and standardized. There are two parts to this: updating the KV-store docstrings and updating the actual integration docs.\\r\\n\\r\\nThis needs to be done for each KV-store integration, ideally with one PR per KV-store.\\r\\n\\r\\nRelated to broader issues #21983 and #22005.\\r\\n\\r\\n## Docstrings\\r\\nEach KV-store class docstring should have the sections shown in the [Appendix](#appendix) below. The sections should have input and output code blocks when relevant.\\r\\n\\r\\nTo build a preview of the API docs for the package you're working on run (from root of repo):\\r\\n\\r\\n```shell\\r\\nmake api_docs_clean; make api_docs_quick_preview API_PKG=openai\\r\\n```\\r\\n\\r\\nwhere `API_PKG=` should be the parent directory that houses the edited package (e.g. community, openai, anthropic, huggingface, together, mistralai, groq, fireworks, etc.). This should be quite fast for all the partner packages.\\r\\n\\r\\n## Doc pages\\r\\nEach KV-store [docs page](https://python.langchain.com/docs/integrations/stores/) should follow [this template](https://github.com/langchain-ai/langchain/blob/master/libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb).\\r\\n\\r\\nHere is an example: https://python.langchain.com/docs/integrations/stores/in_memory/\\r\\n\\r\\nYou can use the `langchain-cli` to quickly get started with a new chat model integration docs page (run from root of repo):\\r\\n\\r\\n```shell\\r\\npoetry run pip install -e libs/cli\\r\\npoetry run langchain-cli integration create-doc --name \\\"foo-bar\\\" --name-class FooBar --component-type kv_store --destination-dir ./docs/docs/integrations/stores/\\r\\n```\\r\\n\\r\\nwhere `--name` is the integration package name without the \\\"langchain-\\\" prefix and `--name-class` is the class name without the \\\"ByteStore\\\" suffix. This will create a template doc with some autopopulated fields at docs/docs/integrations/stores/foo_bar.ipynb.\\r\\n\\r\\nTo build a preview of the docs you can run (from root):\\r\\n\\r\\n```shell\\r\\nmake docs_clean\\r\\nmake docs_build\\r\\ncd docs/build/output-new\\r\\nyarn\\r\\nyarn start\\r\\n```\\r\\n\\r\\n## Appendix\\r\\nExpected sections for the KV-store class docstring.\\r\\n\\r\\n```python\\r\\n    \\\"\\\"\\\"__ModuleName__ completion KV-store integration.\\r\\n\\r\\n    # TODO: Replace with relevant packages, env vars.\\r\\n    Setup:\\r\\n        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.\\r\\n\\r\\n        .. code-block:: bash\\r\\n\\r\\n            pip install -U __package_name__\\r\\n            export __MODULE_NAME___API_KEY=\\\"your-api-key\\\"\\r\\n\\r\\n    # TODO: Populate with relevant params.\\r\\n    Key init args \\u2014 client params:\\r\\n        api_key: Optional[str]\\r\\n            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.\\r\\n\\r\\n    See full list of supported init args and their descriptions in the params section.\\r\\n\\r\\n    # TODO: Replace with relevant init params.\\r\\n    Instantiate:\\r\\n        .. code-block:: python\\r\\n\\r\\n            from __module_name__ import __ModuleName__ByteStore\\r\\n\\r\\n            kv_store = __ModuleName__ByteStore(\\r\\n                # api_key=\\\"...\\\",\\r\\n                # other params...\\r\\n            )\\r\\n\\r\\n    Set keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_pairs = [\\r\\n                [\\\"key1\\\", \\\"value1\\\"],\\r\\n                [\\\"key2\\\", \\\"value2\\\"],\\r\\n            ]\\r\\n\\r\\n            kv_store.mset(kv_pairs)\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n    Get keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_store.mget([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n            # TODO: Example output.\\r\\n\\r\\n    Delete keys:\\r\\n        ..code-block:: python\\r\\n\\r\\n            kv_store.mdelete([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        ..code-block:: python\\r\\n    \\\"\\\"\\\"  # noqa: E501\\r\\n```\", \"comments\": \"[]\", \"opened_by\": \"jacoblee93\"}\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "The title of issue 24888 is \"Standardize KV-Store Docs\".\n"
+          ]
+        }
+      ],
+      "source": [
+        "example_query = \"What is the title of issue 24888?\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `GithubToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.1"
    }
-   ],
-   "source": [
-    "example_query = \"What is the title of issue 24888?\"\n",
-    "\n",
-    "events = agent_executor.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `GithubToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
--- a/docs/docs/integrations/tools/gmail.ipynb
+++ b/docs/docs/integrations/tools/gmail.ipynb
@@ -1,269 +1,267 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gmail Toolkit\n",
-    "\n",
-    "This will help you getting started with the GMail [toolkit](/docs/concepts/tools/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To use this toolkit, you will need to set up your credentials explained in the [Gmail API docs](https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application). Once you've downloaded the `credentials.json` file, you can start using the Gmail API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "This toolkit lives in the `langchain-google-community` package. We'll need the `gmail` extra:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-google-community\\[gmail\\]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "By default the toolkit reads the local `credentials.json` file. You can also manually provide a `Credentials` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_community import GmailToolkit\n",
-    "\n",
-    "toolkit = GmailToolkit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Customizing Authentication\n",
-    "\n",
-    "Behind the scenes, a `googleapi` resource is created using the following methods. \n",
-    "you can manually build a `googleapi` resource for more auth control. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_google_community.gmail.utils import (\n",
-    "    build_resource_service,\n",
-    "    get_gmail_credentials,\n",
-    ")\n",
-    "\n",
-    "# Can review scopes here https://developers.google.com/gmail/api/auth/scopes\n",
-    "# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'\n",
-    "credentials = get_gmail_credentials(\n",
-    "    token_file=\"token.json\",\n",
-    "    scopes=[\"https://mail.google.com/\"],\n",
-    "    client_secrets_file=\"credentials.json\",\n",
-    ")\n",
-    "api_resource = build_resource_service(credentials=credentials)\n",
-    "toolkit = GmailToolkit(api_resource=api_resource)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tools\n",
-    "\n",
-    "View available tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "[GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
-       " GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
-       " GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
-       " GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
-       " GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Gmail Toolkit\n",
+        "\n",
+        "This will help you getting started with the GMail [toolkit](/docs/concepts/tools/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To use this toolkit, you will need to set up your credentials explained in the [Gmail API docs](https://developers.google.com/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application). Once you've downloaded the `credentials.json` file, you can start using the Gmail API."
      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools = toolkit.get_tools()\n",
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- [GmailCreateDraft](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.create_draft.GmailCreateDraft.html)\n",
-    "- [GmailSendMessage](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.send_message.GmailSendMessage.html)\n",
-    "- [GmailSearch](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.search.GmailSearch.html)\n",
-    "- [GmailGetMessage](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.get_message.GmailGetMessage.html)\n",
-    "- [GmailGetThread](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.get_thread.GmailGetThread.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use within an agent\n",
-    "\n",
-    "Below we show how to incorporate the toolkit into an [agent](/docs/tutorials/agents).\n",
-    "\n",
-    "We will need a LLM or chat model:\n",
-    "\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "agent_executor = create_react_agent(llm, tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Draft an email to fake@fake.com thanking them for coffee.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)\n",
-      " Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M\n",
-      "  Args:\n",
-      "    message: Dear Fake,\n",
-      "\n",
-      "I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!\n",
-      "\n",
-      "Best regards,\n",
-      "[Your Name]\n",
-      "    to: ['fake@fake.com']\n",
-      "    subject: Thank You for the Coffee\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: create_gmail_draft\n",
-      "\n",
-      "Draft created. Draft Id: r-7233782721440261513\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject \"Thank You for the Coffee\".\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "This toolkit lives in the `langchain-google-community` package. We'll need the `gmail` extra:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-google-community\\[gmail\\]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "By default the toolkit reads the local `credentials.json` file. You can also manually provide a `Credentials` object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_google_community import GmailToolkit\n",
+        "\n",
+        "toolkit = GmailToolkit()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Customizing Authentication\n",
+        "\n",
+        "Behind the scenes, a `googleapi` resource is created using the following methods. \n",
+        "you can manually build a `googleapi` resource for more auth control. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_google_community.gmail.utils import (\n",
+        "    build_resource_service,\n",
+        "    get_gmail_credentials,\n",
+        ")\n",
+        "\n",
+        "# Can review scopes here https://developers.google.com/gmail/api/auth/scopes\n",
+        "# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'\n",
+        "credentials = get_gmail_credentials(\n",
+        "    token_file=\"token.json\",\n",
+        "    scopes=[\"https://mail.google.com/\"],\n",
+        "    client_secrets_file=\"credentials.json\",\n",
+        ")\n",
+        "api_resource = build_resource_service(credentials=credentials)\n",
+        "toolkit = GmailToolkit(api_resource=api_resource)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tools\n",
+        "\n",
+        "View available tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+              " GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+              " GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+              " GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),\n",
+              " GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tools = toolkit.get_tools()\n",
+        "tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "- [GmailCreateDraft](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.create_draft.GmailCreateDraft.html)\n",
+        "- [GmailSendMessage](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.send_message.GmailSendMessage.html)\n",
+        "- [GmailSearch](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.search.GmailSearch.html)\n",
+        "- [GmailGetMessage](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.get_message.GmailGetMessage.html)\n",
+        "- [GmailGetThread](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.get_thread.GmailGetThread.html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "Below we show how to incorporate the toolkit into an [agent](/docs/tutorials/agents).\n",
+        "\n",
+        "We will need a LLM or chat model:\n",
+        "\n",
+        "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+        "\n",
+        "<ChatModelTabs customVarName=\"llm\" />\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# | output: false\n",
+        "# | echo: false\n",
+        "\n",
+        "from langchain_openai import ChatOpenAI\n",
+        "\n",
+        "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "agent_executor = create_react_agent(llm, tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "Draft an email to fake@fake.com thanking them for coffee.\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)\n",
+            " Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M\n",
+            "  Args:\n",
+            "    message: Dear Fake,\n",
+            "\n",
+            "I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!\n",
+            "\n",
+            "Best regards,\n",
+            "[Your Name]\n",
+            "    to: ['fake@fake.com']\n",
+            "    subject: Thank You for the Coffee\n",
+            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+            "Name: create_gmail_draft\n",
+            "\n",
+            "Draft created. Draft Id: r-7233782721440261513\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject \"Thank You for the Coffee\".\n"
+          ]
+        }
+      ],
+      "source": [
+        "example_query = \"Draft an email to fake@fake.com thanking them for coffee.\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `GmailToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.gmail.toolkit.GmailToolkit.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
    }
-   ],
-   "source": [
-    "example_query = \"Draft an email to fake@fake.com thanking them for coffee.\"\n",
-    "\n",
-    "events = agent_executor.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `GmailToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.gmail.toolkit.GmailToolkit.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
--- a/docs/docs/integrations/tools/goat.ipynb
+++ b/docs/docs/integrations/tools/goat.ipynb
@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: GOAT\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# GOAT\n",
+    "\n",
+    "[GOAT](https://github.com/goat-sdk/goat) is the finance toolkit for AI agents.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Create agents that can:\n",
+    "\n",
+    "- Send and receive payments\n",
+    "- Purchase physical and digital goods and services\n",
+    "- Engage in various investment strategies:\n",
+    "  - Earn yield\n",
+    "  - Bet on prediction markets\n",
+    "- Purchase crypto assets\n",
+    "- Tokenize any asset\n",
+    "- Get financial insights\n",
+    "\n",
+    "### How it works\n",
+    "GOAT leverages blockchains, cryptocurrencies (such as stablecoins), and wallets as the infrastructure to enable agents to become economic actors:\n",
+    "\n",
+    "1. Give your agent a [wallet](https://github.com/goat-sdk/goat/tree/main#chains-and-wallets)\n",
+    "2. Allow it to transact [anywhere](https://github.com/goat-sdk/goat/tree/main#chains-and-wallets)\n",
+    "3. Use more than [+200 tools](https://github.com/goat-sdk/goat/tree/main#tools)\n",
+    "\n",
+    "See everything GOAT supports [here](https://github.com/goat-sdk/goat/tree/main#chains-and-wallets).\n",
+    "\n",
+    "**Lightweight and extendable**\n",
+    "Different from other toolkits, GOAT is designed to be lightweight and extendable by keeping its core minimal and allowing you to install only the tools you need.\n",
+    "\n",
+    "If you don't find what you need on our more than 200 integrations you can easily:\n",
+    "\n",
+    "- Create your own plugin\n",
+    "- Integrate a new chain\n",
+    "- Integrate a new wallet\n",
+    "- Integrate a new agent framework\n",
+    "\n",
+    "See how to do it [here](https://github.com/goat-sdk/goat/tree/main#-contributing)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Quickstarts\n",
+    "\n",
+    "The best way to get started is by using the quickstarts below. See how you can configure GOAT to achieve any of the use cases below.\n",
+    "\n",
+    "- **By use case**\n",
+    "  - **Money transmission**\n",
+    "    - Send and receive payments [[EVM](https://github.com/goat-sdk/goat/tree/main/python/examples/by-use-case/evm-send-and-receive-tokens), [Solana](https://github.com/goat-sdk/goat/tree/main/python/examples/by-use-case/solana-send-and-receive-tokens)]\n",
+    "  - **Investing**\n",
+    "    - Generate yield [[Solana](https://github.com/goat-sdk/goat/tree/main/python/examples/by-use-case/solana-usdc-yield-deposit)]\n",
+    "    - Purchase crypto assets [[EVM](https://github.com/goat-sdk/goat/tree/main/python/examples/by-use-case/evm-swap-tokens), [Solana](https://github.com/goat-sdk/goat/tree/main/python/examples/by-use-case/solana-swap-tokens)]\n",
+    "- **By wallet**\n",
+    "  - [Crossmint](https://github.com/goat-sdk/goat/tree/main/python/examples/by-wallet/crossmint)\n",
+    "- **See all python quickstarts [here](https://github.com/goat-sdk/goat/tree/main/python/examples).**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abd26764",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "1. Install the core package and langchain adapter:\n",
+    "\n",
+    "```bash\n",
+    "pip install goat-sdk goat-sdk-adapter-langchain\n",
+    "```\n",
+    "\n",
+    "2. Install the type of wallet you want to use (e.g solana):\n",
+    "\n",
+    "```bash\n",
+    "pip install goat-sdk-wallet-solana\n",
+    "```\n",
+    "\n",
+    "3. Install the plugins you want to use in that chain:\n",
+    "\n",
+    "```bash\n",
+    "pip install goat-sdk-plugin-spl-token\n",
+    "```\n",
+    "\n",
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our toolkit:\n",
+    "\n",
+    "```python\n",
+    "from goat_adapters.langchain import get_on_chain_tools\n",
+    "from goat_wallets.solana import solana, send_solana\n",
+    "from goat_plugins.spl_token import spl_token, SplTokenPluginOptions\n",
+    "from goat_plugins.spl_token.tokens import SPL_TOKENS\n",
+    "\n",
+    "# Initialize Solana client\n",
+    "client = SolanaClient(os.getenv(\"SOLANA_RPC_ENDPOINT\"))\n",
+    "\n",
+    "# Initialize regular Solana wallet\n",
+    "keypair = Keypair.from_base58_string(os.getenv(\"SOLANA_WALLET_SEED\") or \"\")\n",
+    "wallet = solana(client, keypair)\n",
+    "\n",
+    "tools = get_on_chain_tools(\n",
+    "        wallet=wallet,\n",
+    "        plugins=[\n",
+    "            send_solana(),\n",
+    "            spl_token(SplTokenPluginOptions(\n",
+    "                network=\"mainnet\",  # Using devnet as specified in .env\n",
+    "                tokens=SPL_TOKENS\n",
+    "            )),\n",
+    "        ],\n",
+    "    )\n",
+    "```\n",
+    "\n",
+    "## Invocation\n",
+    "```python\n",
+    "tools[\"get_balance\"].invoke({ \"address\": \"0x1234567890123456789012345678901234567890\" })\n",
+    "```\n",
+    "\n",
+    "## Use within an agent\n",
+    "\n",
+    "```python\n",
+    "import os\n",
+    "import asyncio\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "# Load environment variables\n",
+    "load_dotenv()\n",
+    "\n",
+    "from solana.rpc.api import Client as SolanaClient\n",
+    "from solders.keypair import Keypair\n",
+    "\n",
+    "from goat_adapters.langchain import get_on_chain_tools\n",
+    "from goat_wallets.solana import solana, send_solana\n",
+    "from goat_plugins.spl_token import spl_token, SplTokenPluginOptions\n",
+    "from goat_plugins.spl_token.tokens import SPL_TOKENS\n",
+    "\n",
+    "# Initialize Solana client\n",
+    "client = SolanaClient(os.getenv(\"SOLANA_RPC_ENDPOINT\"))\n",
+    "\n",
+    "# Initialize regular Solana wallet\n",
+    "keypair = Keypair.from_base58_string(os.getenv(\"SOLANA_WALLET_SEED\") or \"\")\n",
+    "wallet = solana(client, keypair)\n",
+    "\n",
+    "# Initialize LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "\n",
+    "def main():\n",
+    "    # Initialize tools with Solana wallet\n",
+    "    tools = get_on_chain_tools(\n",
+    "        wallet=wallet,\n",
+    "        plugins=[\n",
+    "            send_solana(),\n",
+    "            spl_token(SplTokenPluginOptions(\n",
+    "                network=\"mainnet\",  # Using devnet as specified in .env\n",
+    "                tokens=SPL_TOKENS\n",
+    "            )),\n",
+    "        ],\n",
+    "    )\n",
+    "\n",
+    "    # Initialize agent\n",
+    "    # Your agent code here\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n",
+    "```\n",
+    "\n",
+    "## API reference\n",
+    "\n",
+    "- For a complete list of tools, see the [GOAT SDK documentation](https://github.com/goat-sdk/goat).\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/docs/integrations/tools/ibm_watsonx.ipynb
+++ b/docs/docs/integrations/tools/ibm_watsonx.ipynb
@@ -0,0 +1,612 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: IBM watsonx.ai\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IBM watsonx.ai\n",
+    "\n",
+    ">WatsonxToolkit is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) Toolkit.\n",
+    "\n",
+    "This example shows how to use `watsonx.ai` Toolkit using `LangChain`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/toolkits/ibm/) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: | :---: | :---: |\n",
+    "| [WatsonxToolkit](https://python.langchain.com/api_reference/ibm/toolkit/langchain_ibm.toolkit.WatsonxToolkit.html) | [langchain-ibm](https://python.langchain.com/api_reference/ibm/index.html) | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "To access IBM watsonx.ai toolkit you'll need to create an IBM watsonx.ai account, get an API key, and install the `langchain-ibm` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "This cell defines the WML credentials required to work with watsonx Toolkit.\n",
+    "\n",
+    "**Action:** Provide the IBM Cloud user API key. For details, see\n",
+    "[documentation](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "watsonx_api_key = getpass()\n",
+    "os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additionaly you are able to pass additional secrets as an environment variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
+    "os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the service instance\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain IBM integration lives in the `langchain-ibm` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -qU langchain-ibm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the `WatsonxToolkit` class.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_ibm import WatsonxToolkit\n",
+    "\n",
+    "watsonx_toolkit = WatsonxToolkit(\n",
+    "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For certain requirements, there is an option to pass the IBM's [`APIClient`](https://ibm.github.io/watsonx-ai-python-sdk/base.html#apiclient) object into the `WatsonxToolkit` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ibm_watsonx_ai import APIClient\n",
+    "\n",
+    "api_client = APIClient(...)\n",
+    "\n",
+    "watsonx_toolkit = WatsonxToolkit(\n",
+    "    watsonx_client=api_client,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tools\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get all tools\n",
+    "It is possible to get all available tools as a list of `WatsonxTool` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[WatsonxTool(name='GoogleSearch', description='Search for online trends, news, current events, real-time information, or research topics.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Search for online trends, news, current events, real-time information, or research topics.', tool_config_schema={'title': 'config schema for GoogleSearch tool', 'type': 'object', 'properties': {'maxResults': {'title': 'Max number of results to return', 'type': 'integer', 'minimum': 1, 'maximum': 20}}}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+       " WatsonxTool(name='WebCrawler', description='Useful for when you need to summarize a webpage. Do not use for Web search.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Useful for when you need to summarize a webpage. Do not use for Web search.', tool_input_schema={'type': 'object', 'properties': {'url': {'title': 'url', 'description': 'URL for the webpage to be scraped', 'type': 'string', 'pattern': '^(https?:\\\\/\\\\/)?([\\\\da-z\\\\.-]+)\\\\.([a-z\\\\.]{2,6})([\\\\/\\\\w \\\\.-]*)*\\\\/?$'}}, 'required': ['url']}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+       " WatsonxTool(name='PythonInterpreter', description='Run Python code generated by the agent model.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Run Python code and return the console output. Use for isolated calculations, computations or data manipulation. In Python, the following modules are available: Use numpy, pandas, scipy and sympy for working with data. Use matplotlib to plot charts. Other Python libraries are also available -- however, prefer using the ones above. Prefer using qualified imports -- `import library; library.thing()` instead of `import thing from library`. Do not attempt to install libraries manually -- it will not work. Do not use this tool multiple times in a row, always write the full code you want to run in a single invocation. If you get an error running Python code, try to generate a better one that will pass. If the tool returns result that starts with IMAGE(, follow instructions for rendering images.', watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+       " WatsonxTool(name='SDXLTurbo', description='Generate an image from text using Stability.ai', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Generate an image from text. Not for image refining. Use very precise language about the desired image, including setting, lighting, style, filters and lenses used. Do not ask the tool to refine an image.', watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+       " WatsonxTool(name='Weather', description='Find the weather for a city.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Find the weather for a city.', tool_input_schema={'type': 'object', 'properties': {'location': {'title': 'location', 'description': 'Name of the location', 'type': 'string'}, 'country': {'title': 'country', 'description': 'Name of the state or country', 'type': 'string'}}, 'required': ['location']}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+       " WatsonxTool(name='RAGQuery', description='Search the documents in a vector index.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Search information in documents to provide context to a user query. Useful when asked to ground the answer in specific knowledge about {indexName}', tool_config_schema={'title': 'config schema for RAGQuery tool', 'type': 'object', 'properties': {'vectorIndexId': {'title': 'Vector index identifier', 'type': 'string'}, 'projectId': {'title': 'Project identifier', 'type': 'string'}, 'spaceId': {'title': 'Space identifier', 'type': 'string'}}, 'required': ['vectorIndexId'], 'oneOf': [{'required': ['projectId']}, {'required': ['spaceId']}]}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>)]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "watsonx_toolkit.get_tools()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get a tool\n",
+    "You can also get a specific `WatsonxTool` by name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "google_search = watsonx_toolkit.get_tool(tool_name=\"GoogleSearch\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Invoke the tool with a simple input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': '[{\"title\":\"IBM - United States\",\"description\":\"Technology & Consulting. From next-generation AI to cutting edge hybrid cloud solutions to the deep expertise of IBM Consulting, IBM has what it takes to help\\xa0...\",\"url\":\"https://www.ibm.com/us-en\"},{\"title\":\"IBM - Wikipedia\",\"description\":\"International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in\\xa0...\",\"url\":\"https://en.wikipedia.org/wiki/IBM\"},{\"title\":\"IBM Envizi ESG Suite\",\"description\":\"Envizi systemizes the capture, transformation and consolidation of disparate sustainability data into a single source of truth and delivers actionable insights.\",\"url\":\"https://www.ibm.com/products/envizi\"},{\"title\":\"IBM Research\",\"description\":\"Tools + Code · BeeAI Framework. Open-source framework for building, deploying, and serving powerful agentic workflows at scale. · Docling. An open-source tool\\xa0...\",\"url\":\"https://research.ibm.com/\"},{\"title\":\"IBM SkillsBuild: Free Skills-Based Learning From Technology Experts\",\"description\":\"IBM SkillsBuildPower your future in tech with job skills, courses, and credentials—for free. Power your future in tech with job skills, courses, and credentials\\xa0...\",\"url\":\"https://skillsbuild.org/\"},{\"title\":\"IBM | LinkedIn\",\"description\":\"Locations · Primary. International Business Machines Corp. · 590 Madison Ave · 90 Grayston Dr · Plaza Independencia 721 · 388 Phahon Yothin Road · Jalan Prof.\",\"url\":\"https://www.linkedin.com/company/ibm\"},{\"title\":\"International Business Machines Corporation (IBM) Stock Price ...\",\"description\":\"Mar 18, 2025 ... International Business Machines Corporation (IBM) · 1.19% · -2.77% · 11.49% · 12.00% · 29.04% · 138.39% · 3,238.84%. Key Events. Mountain.\",\"url\":\"https://finance.yahoo.com/quote/IBM/\"},{\"title\":\"Zurich - IBM Research\",\"description\":\"The location in Zurich is one of IBM\\'s 12 global research labs. IBM has maintained a research laboratory in Switzerland since 1956.\",\"url\":\"https://research.ibm.com/labs/zurich\"},{\"title\":\"IBM Newsroom\",\"description\":\"News and press releases from around the IBM world. Media contacts. Sources by topic and by region. IBM Media center. Explore IBM\\'s latest and most popular\\xa0...\",\"url\":\"https://newsroom.ibm.com/\"},{\"title\":\"IBM (@ibm) • Instagram photos and videos\",\"description\":\"Science, Technology & Engineering. We partner with developers, data scientists, CTOs and other creators to make the world work better.\",\"url\":\"https://www.instagram.com/ibm/?hl=en\"}]'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_result = google_search.invoke(input=\"IBM\")\n",
+    "search_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To fetch a list of received results, you can execute the below cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "output = json.loads(search_result.get(\"output\"))\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Invoke the tool with a configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To check if a tool has a config schema and view its properties you can look at the tool's `tool_config_schema`.\n",
+    "\n",
+    "In this example, the tool has a config schema that contains `maxResults` parameter to set maximum number of results to be returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'title': 'config schema for GoogleSearch tool',\n",
+       " 'type': 'object',\n",
+       " 'properties': {'maxResults': {'title': 'Max number of results to return',\n",
+       "   'type': 'integer',\n",
+       "   'minimum': 1,\n",
+       "   'maximum': 20}}}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "google_search.tool_config_schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To set `tool_config` parameters, you need to use `set_tool_config()` method and pass correct `dict` according to above `tool_config_schema`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "config = {\"maxResults\": 3}\n",
+    "google_search.set_tool_config(config)\n",
+    "\n",
+    "search_result = google_search.invoke(input=\"IBM\")\n",
+    "output = json.loads(search_result.get(\"output\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is supposed to be maximum 3 results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(output))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Invoke the tool with an input schema\n",
+    "\n",
+    "We need to get another tool (with an input schema) for the example purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_tool = watsonx_toolkit.get_tool(\"Weather\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To check if a tool has an input schema and view its properties, you can look at the tool's `tool_input_schema`.\n",
+    "\n",
+    "In this example, the tool has an input schema that contains one required and one optional parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'object',\n",
+       " 'properties': {'location': {'title': 'location',\n",
+       "   'description': 'Name of the location',\n",
+       "   'type': 'string'},\n",
+       "  'country': {'title': 'country',\n",
+       "   'description': 'Name of the state or country',\n",
+       "   'type': 'string'}},\n",
+       " 'required': ['location']}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather_tool.tool_input_schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To correctly pass an input to `invoke()`, you need to create an `invoke_input` dictionary with required parameter as a key with its value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': 'Current weather in New York:\\nTemperature: 12.1°C\\nRain: 0mm\\nRelative humidity: 36%\\nWind: 8.1km/h\\n'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "invoke_input = {\n",
+    "    \"location\": \"New York\",\n",
+    "}\n",
+    "\n",
+    "weather_result = weather_tool.invoke(input=invoke_input)\n",
+    "weather_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This time the output is a single string value. To fetch and print it you can execute the below cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current weather in New York:\n",
+      "Temperature: 12.1°C\n",
+      "Rain: 0mm\n",
+      "Relative humidity: 36%\n",
+      "Wind: 8.1km/h\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "output = weather_result.get(\"output\")\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Invoke the tool with a ToolCall\n",
+    "\n",
+    "We can also invoke the tool with a ToolCall, in which case a ToolMessage will be returned:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ToolMessage(content='{\"output\": \"Current weather in Los Angeles:\\\\nTemperature: 13.3°C\\\\nRain: 0mm\\\\nRelative humidity: 89%\\\\nWind: 3.3km/h\\\\n\"}', name='Weather', tool_call_id='1')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "invoke_input = {\n",
+    "    \"location\": \"Los Angeles\",\n",
+    "}\n",
+    "tool_call = dict(\n",
+    "    args=invoke_input,\n",
+    "    id=\"1\",\n",
+    "    name=weather_tool.name,\n",
+    "    type=\"tool_call\",\n",
+    ")\n",
+    "weather_tool.invoke(input=tool_call)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use within an agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_ibm import ChatWatsonx\n",
+    "\n",
+    "llm = ChatWatsonx(\n",
+    "    model_id=\"meta-llama/llama-3-3-70b-instruct\",\n",
+    "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
+    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "tools = [weather_tool]\n",
+    "agent = create_react_agent(llm, tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "What is the weather in Boston?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  Weather (chatcmpl-tool-fe07ca2f5b5a4fd584eaedc889bea5b7)\n",
+      " Call ID: chatcmpl-tool-fe07ca2f5b5a4fd584eaedc889bea5b7\n",
+      "  Args:\n",
+      "    location: Boston\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: Weather\n",
+      "\n",
+      "{\"output\": \"Current weather in Boston:\\nTemperature: 9.1°C\\nRain: 0mm\\nRelative humidity: 41%\\nWind: 24.5km/h\\n\"}\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The current weather in Boston is:\n",
+      "Temperature: 9.1°C\n",
+      "Rain: 0mm\n",
+      "Relative humidity: 41%\n",
+      "Wind: 24.5km/h\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_query = \"What is the weather in Boston?\"\n",
+    "\n",
+    "events = agent.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `WatsonxToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/ibm/toolkit/langchain_ibm.toolkit.WatsonxToolkit.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-ibm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/docs/docs/integrations/tools/memgraph.ipynb
+++ b/docs/docs/integrations/tools/memgraph.ipynb
@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Memgraph\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# MemgraphToolkit\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This will help you getting started with the Memgraph [toolkit](/docs/concepts/tools/#toolkits). \n",
+    "\n",
+    "Tools within `MemgraphToolkit` are designed for the interaction with the `Memgraph` database.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To be able tot follow the steps below, make sure you have a running Memgraph instance on your local host. For more details on how to run Memgraph, take a look at [Memgraph docs](https://memgraph.com/docs/getting-started)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This toolkit lives in the `langchain-memgraph` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-memgraph "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our toolkit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain_memgraph import MemgraphToolkit\n",
+    "from langchain_memgraph.graphs.memgraph import Memgraph\n",
+    "\n",
+    "db = Memgraph(url=url, username=username, password=password)\n",
+    "\n",
+    "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\")\n",
+    "\n",
+    "toolkit = MemgraphToolkit(\n",
+    "    db=db,  # Memgraph instance\n",
+    "    llm=llm,  # LLM chat model for LLM operations\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "\n",
+    "View available tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toolkit.get_tools()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "608af19d",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "Tools can be individually called by passing an arguments, for QueryMemgraphTool it would be: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffa944db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_memgraph.tools import QueryMemgraphTool\n",
+    "\n",
+    "# Rest of the code omitted for brevity\n",
+    "\n",
+    "tool.invoke({QueryMemgraphTool({\"query\": \"MATCH (n) RETURN n LIMIT 5\"})})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+   "metadata": {},
+   "source": [
+    "## Use within an agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310bf18e-6c9a-4072-b86e-47bc1fcca29d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent_executor = create_react_agent(llm, tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_query = \"MATCH (n) RETURN n LIMIT 1\"\n",
+    "\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29ca615b",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For more details on API visit [Memgraph integration docs](https://memgraph.com/docs/ai-ecosystem/integrations#langchain)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/docs/integrations/tools/requests.ipynb
+++ b/docs/docs/integrations/tools/requests.ipynb
@@ -1,351 +1,349 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cfe4185a-34dc-4cdc-b831-001954f2d6e8",
-   "metadata": {},
-   "source": [
-    "# Requests Toolkit\n",
-    "\n",
-    "We can use the Requests [toolkit](/docs/concepts/tools/#toolkits) to construct agents that generate HTTP requests.\n",
-    "\n",
-    "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html).\n",
-    "\n",
-    "## ⚠️ Security note ⚠️\n",
-    "There are inherent risks in giving models discretion to execute real-world actions. Take precautions to mitigate these risks:\n",
-    "\n",
-    "- Make sure that permissions associated with the tools are narrowly-scoped (e.g., for database operations or API requests);\n",
-    "- When desired, make use of human-in-the-loop workflows."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d968e982-f370-4614-8469-c1bc71ee3e32",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "### Installation\n",
-    "\n",
-    "This toolkit lives in the `langchain-community` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f74f05fb-3f24-4c0b-a17f-cf4edeedbb9a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36a178eb-1f2c-411e-bf25-0240ead4c62a",
-   "metadata": {},
-   "source": [
-    "Note that if you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e68d0cd-6233-481c-b048-e8d95cba4c35",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7e2f64a-a72e-4fef-be52-eaf7c5072d24",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "First we will demonstrate a minimal example.\n",
-    "\n",
-    "**NOTE**: There are inherent risks in giving models discretion to execute real-world actions. We must \"opt-in\" to these risks by setting `allow_dangerous_request=True` to use these tools.\n",
-    "**This can be dangerous for calling unwanted requests**. Please make sure your custom OpenAPI spec (yaml) is safe and that permissions associated with the tools are narrowly-scoped."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "018bd070-9fc8-459b-8d28-b4a3e283e640",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ALLOW_DANGEROUS_REQUEST = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a024f7b3-5437-4878-bd16-c4783bff394c",
-   "metadata": {},
-   "source": [
-    "We can use the [JSONPlaceholder](https://jsonplaceholder.typicode.com) API as a testing ground.\n",
-    "\n",
-    "Let's create (a subset of) its API spec:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2dcbcf92-2ad5-49c3-94ac-91047ccc8c5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Any, Dict, Union\n",
-    "\n",
-    "import requests\n",
-    "import yaml\n",
-    "\n",
-    "\n",
-    "def _get_schema(response_json: Union[dict, list]) -> dict:\n",
-    "    if isinstance(response_json, list):\n",
-    "        response_json = response_json[0] if response_json else {}\n",
-    "    return {key: type(value).__name__ for key, value in response_json.items()}\n",
-    "\n",
-    "\n",
-    "def _get_api_spec() -> str:\n",
-    "    base_url = \"https://jsonplaceholder.typicode.com\"\n",
-    "    endpoints = [\n",
-    "        \"/posts\",\n",
-    "        \"/comments\",\n",
-    "    ]\n",
-    "    common_query_parameters = [\n",
-    "        {\n",
-    "            \"name\": \"_limit\",\n",
-    "            \"in\": \"query\",\n",
-    "            \"required\": False,\n",
-    "            \"schema\": {\"type\": \"integer\", \"example\": 2},\n",
-    "            \"description\": \"Limit the number of results\",\n",
-    "        }\n",
-    "    ]\n",
-    "    openapi_spec: Dict[str, Any] = {\n",
-    "        \"openapi\": \"3.0.0\",\n",
-    "        \"info\": {\"title\": \"JSONPlaceholder API\", \"version\": \"1.0.0\"},\n",
-    "        \"servers\": [{\"url\": base_url}],\n",
-    "        \"paths\": {},\n",
-    "    }\n",
-    "    # Iterate over the endpoints to construct the paths\n",
-    "    for endpoint in endpoints:\n",
-    "        response = requests.get(base_url + endpoint)\n",
-    "        if response.status_code == 200:\n",
-    "            schema = _get_schema(response.json())\n",
-    "            openapi_spec[\"paths\"][endpoint] = {\n",
-    "                \"get\": {\n",
-    "                    \"summary\": f\"Get {endpoint[1:]}\",\n",
-    "                    \"parameters\": common_query_parameters,\n",
-    "                    \"responses\": {\n",
-    "                        \"200\": {\n",
-    "                            \"description\": \"Successful response\",\n",
-    "                            \"content\": {\n",
-    "                                \"application/json\": {\n",
-    "                                    \"schema\": {\"type\": \"object\", \"properties\": schema}\n",
-    "                                }\n",
-    "                            },\n",
-    "                        }\n",
-    "                    },\n",
-    "                }\n",
-    "            }\n",
-    "    return yaml.dump(openapi_spec, sort_keys=False)\n",
-    "\n",
-    "\n",
-    "api_spec = _get_api_spec()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db3d6148-ae65-4a1d-91a6-59ee3e4e6efa",
-   "metadata": {},
-   "source": [
-    "Next we can instantiate the toolkit. We require no authorization or other headers for this API:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "63a630b3-45bb-4525-865b-083f322b944b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit\n",
-    "from langchain_community.utilities.requests import TextRequestsWrapper\n",
-    "\n",
-    "toolkit = RequestsToolkit(\n",
-    "    requests_wrapper=TextRequestsWrapper(headers={}),\n",
-    "    allow_dangerous_requests=ALLOW_DANGEROUS_REQUEST,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4224a64-843a-479d-8a7b-84719e4b9d0c",
-   "metadata": {},
-   "source": [
-    "## Tools\n",
-    "\n",
-    "View available tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "70ea0f4e-9f10-4906-894b-08df832fd515",
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "[RequestsGetTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
-       " RequestsPostTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
-       " RequestsPatchTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
-       " RequestsPutTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
-       " RequestsDeleteTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True)]"
+      "cell_type": "markdown",
+      "id": "cfe4185a-34dc-4cdc-b831-001954f2d6e8",
+      "metadata": {},
+      "source": [
+        "# Requests Toolkit\n",
+        "\n",
+        "We can use the Requests [toolkit](/docs/concepts/tools/#toolkits) to construct agents that generate HTTP requests.\n",
+        "\n",
+        "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html).\n",
+        "\n",
+        "## ⚠️ Security note ⚠️\n",
+        "There are inherent risks in giving models discretion to execute real-world actions. Take precautions to mitigate these risks:\n",
+        "\n",
+        "- Make sure that permissions associated with the tools are narrowly-scoped (e.g., for database operations or API requests);\n",
+        "- When desired, make use of human-in-the-loop workflows."
      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools = toolkit.get_tools()\n",
-    "\n",
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a21a6ca4-d650-4b7d-a944-1a8771b5293a",
-   "metadata": {},
-   "source": [
-    "- [RequestsGetTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsGetTool.html)\n",
-    "- [RequestsPostTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPostTool.html)\n",
-    "- [RequestsPatchTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPatchTool.html)\n",
-    "- [RequestsPutTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPutTool.html)\n",
-    "- [RequestsDeleteTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsDeleteTool.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2dbb304-abf2-472a-9130-f03150a40549",
-   "metadata": {},
-   "source": [
-    "## Use within an agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "db062da7-f22c-4f36-9df8-1da96c9f7538",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "system_message = \"\"\"\n",
-    "You have access to an API to help answer user queries.\n",
-    "Here is documentation on the API:\n",
-    "{api_spec}\n",
-    "\"\"\".format(api_spec=api_spec)\n",
-    "\n",
-    "agent_executor = create_react_agent(llm, tools, prompt=system_message)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c1e47be9-374a-457c-928a-48f02b5530e3",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Fetch the top two posts. What are their titles?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  requests_get (call_RV2SOyzCnV5h2sm4WPgG8fND)\n",
-      " Call ID: call_RV2SOyzCnV5h2sm4WPgG8fND\n",
-      "  Args:\n",
-      "    url: https://jsonplaceholder.typicode.com/posts?_limit=2\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: requests_get\n",
-      "\n",
-      "[\n",
-      "  {\n",
-      "    \"userId\": 1,\n",
-      "    \"id\": 1,\n",
-      "    \"title\": \"sunt aut facere repellat provident occaecati excepturi optio reprehenderit\",\n",
-      "    \"body\": \"quia et suscipit\\nsuscipit recusandae consequuntur expedita et cum\\nreprehenderit molestiae ut ut quas totam\\nnostrum rerum est autem sunt rem eveniet architecto\"\n",
-      "  },\n",
-      "  {\n",
-      "    \"userId\": 1,\n",
-      "    \"id\": 2,\n",
-      "    \"title\": \"qui est esse\",\n",
-      "    \"body\": \"est rerum tempore vitae\\nsequi sint nihil reprehenderit dolor beatae ea dolores neque\\nfugiat blanditiis voluptate porro vel nihil molestiae ut reiciendis\\nqui aperiam non debitis possimus qui neque nisi nulla\"\n",
-      "  }\n",
-      "]\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The titles of the top two posts are:\n",
-      "1. \"sunt aut facere repellat provident occaecati excepturi optio reprehenderit\"\n",
-      "2. \"qui est esse\"\n"
-     ]
+      "cell_type": "markdown",
+      "id": "d968e982-f370-4614-8469-c1bc71ee3e32",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "This toolkit lives in the `langchain-community` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f74f05fb-3f24-4c0b-a17f-cf4edeedbb9a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "36a178eb-1f2c-411e-bf25-0240ead4c62a",
+      "metadata": {},
+      "source": "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8e68d0cd-6233-481c-b048-e8d95cba4c35",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a7e2f64a-a72e-4fef-be52-eaf7c5072d24",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "First we will demonstrate a minimal example.\n",
+        "\n",
+        "**NOTE**: There are inherent risks in giving models discretion to execute real-world actions. We must \"opt-in\" to these risks by setting `allow_dangerous_request=True` to use these tools.\n",
+        "**This can be dangerous for calling unwanted requests**. Please make sure your custom OpenAPI spec (yaml) is safe and that permissions associated with the tools are narrowly-scoped."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "018bd070-9fc8-459b-8d28-b4a3e283e640",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ALLOW_DANGEROUS_REQUEST = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a024f7b3-5437-4878-bd16-c4783bff394c",
+      "metadata": {},
+      "source": [
+        "We can use the [JSONPlaceholder](https://jsonplaceholder.typicode.com) API as a testing ground.\n",
+        "\n",
+        "Let's create (a subset of) its API spec:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "2dcbcf92-2ad5-49c3-94ac-91047ccc8c5b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from typing import Any, Dict, Union\n",
+        "\n",
+        "import requests\n",
+        "import yaml\n",
+        "\n",
+        "\n",
+        "def _get_schema(response_json: Union[dict, list]) -> dict:\n",
+        "    if isinstance(response_json, list):\n",
+        "        response_json = response_json[0] if response_json else {}\n",
+        "    return {key: type(value).__name__ for key, value in response_json.items()}\n",
+        "\n",
+        "\n",
+        "def _get_api_spec() -> str:\n",
+        "    base_url = \"https://jsonplaceholder.typicode.com\"\n",
+        "    endpoints = [\n",
+        "        \"/posts\",\n",
+        "        \"/comments\",\n",
+        "    ]\n",
+        "    common_query_parameters = [\n",
+        "        {\n",
+        "            \"name\": \"_limit\",\n",
+        "            \"in\": \"query\",\n",
+        "            \"required\": False,\n",
+        "            \"schema\": {\"type\": \"integer\", \"example\": 2},\n",
+        "            \"description\": \"Limit the number of results\",\n",
+        "        }\n",
+        "    ]\n",
+        "    openapi_spec: Dict[str, Any] = {\n",
+        "        \"openapi\": \"3.0.0\",\n",
+        "        \"info\": {\"title\": \"JSONPlaceholder API\", \"version\": \"1.0.0\"},\n",
+        "        \"servers\": [{\"url\": base_url}],\n",
+        "        \"paths\": {},\n",
+        "    }\n",
+        "    # Iterate over the endpoints to construct the paths\n",
+        "    for endpoint in endpoints:\n",
+        "        response = requests.get(base_url + endpoint)\n",
+        "        if response.status_code == 200:\n",
+        "            schema = _get_schema(response.json())\n",
+        "            openapi_spec[\"paths\"][endpoint] = {\n",
+        "                \"get\": {\n",
+        "                    \"summary\": f\"Get {endpoint[1:]}\",\n",
+        "                    \"parameters\": common_query_parameters,\n",
+        "                    \"responses\": {\n",
+        "                        \"200\": {\n",
+        "                            \"description\": \"Successful response\",\n",
+        "                            \"content\": {\n",
+        "                                \"application/json\": {\n",
+        "                                    \"schema\": {\"type\": \"object\", \"properties\": schema}\n",
+        "                                }\n",
+        "                            },\n",
+        "                        }\n",
+        "                    },\n",
+        "                }\n",
+        "            }\n",
+        "    return yaml.dump(openapi_spec, sort_keys=False)\n",
+        "\n",
+        "\n",
+        "api_spec = _get_api_spec()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "db3d6148-ae65-4a1d-91a6-59ee3e4e6efa",
+      "metadata": {},
+      "source": [
+        "Next we can instantiate the toolkit. We require no authorization or other headers for this API:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "63a630b3-45bb-4525-865b-083f322b944b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit\n",
+        "from langchain_community.utilities.requests import TextRequestsWrapper\n",
+        "\n",
+        "toolkit = RequestsToolkit(\n",
+        "    requests_wrapper=TextRequestsWrapper(headers={}),\n",
+        "    allow_dangerous_requests=ALLOW_DANGEROUS_REQUEST,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f4224a64-843a-479d-8a7b-84719e4b9d0c",
+      "metadata": {},
+      "source": [
+        "## Tools\n",
+        "\n",
+        "View available tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "70ea0f4e-9f10-4906-894b-08df832fd515",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[RequestsGetTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
+              " RequestsPostTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
+              " RequestsPatchTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
+              " RequestsPutTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True),\n",
+              " RequestsDeleteTool(requests_wrapper=TextRequestsWrapper(headers={}, aiosession=None, auth=None, response_content_type='text', verify=True), allow_dangerous_requests=True)]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tools = toolkit.get_tools()\n",
+        "\n",
+        "tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a21a6ca4-d650-4b7d-a944-1a8771b5293a",
+      "metadata": {},
+      "source": [
+        "- [RequestsGetTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsGetTool.html)\n",
+        "- [RequestsPostTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPostTool.html)\n",
+        "- [RequestsPatchTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPatchTool.html)\n",
+        "- [RequestsPutTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPutTool.html)\n",
+        "- [RequestsDeleteTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsDeleteTool.html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e2dbb304-abf2-472a-9130-f03150a40549",
+      "metadata": {},
+      "source": [
+        "## Use within an agent"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "db062da7-f22c-4f36-9df8-1da96c9f7538",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import ChatOpenAI\n",
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+        "\n",
+        "system_message = \"\"\"\n",
+        "You have access to an API to help answer user queries.\n",
+        "Here is documentation on the API:\n",
+        "{api_spec}\n",
+        "\"\"\".format(api_spec=api_spec)\n",
+        "\n",
+        "agent_executor = create_react_agent(llm, tools, prompt=system_message)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "c1e47be9-374a-457c-928a-48f02b5530e3",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "Fetch the top two posts. What are their titles?\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  requests_get (call_RV2SOyzCnV5h2sm4WPgG8fND)\n",
+            " Call ID: call_RV2SOyzCnV5h2sm4WPgG8fND\n",
+            "  Args:\n",
+            "    url: https://jsonplaceholder.typicode.com/posts?_limit=2\n",
+            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+            "Name: requests_get\n",
+            "\n",
+            "[\n",
+            "  {\n",
+            "    \"userId\": 1,\n",
+            "    \"id\": 1,\n",
+            "    \"title\": \"sunt aut facere repellat provident occaecati excepturi optio reprehenderit\",\n",
+            "    \"body\": \"quia et suscipit\\nsuscipit recusandae consequuntur expedita et cum\\nreprehenderit molestiae ut ut quas totam\\nnostrum rerum est autem sunt rem eveniet architecto\"\n",
+            "  },\n",
+            "  {\n",
+            "    \"userId\": 1,\n",
+            "    \"id\": 2,\n",
+            "    \"title\": \"qui est esse\",\n",
+            "    \"body\": \"est rerum tempore vitae\\nsequi sint nihil reprehenderit dolor beatae ea dolores neque\\nfugiat blanditiis voluptate porro vel nihil molestiae ut reiciendis\\nqui aperiam non debitis possimus qui neque nisi nulla\"\n",
+            "  }\n",
+            "]\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "The titles of the top two posts are:\n",
+            "1. \"sunt aut facere repellat provident occaecati excepturi optio reprehenderit\"\n",
+            "2. \"qui est esse\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "example_query = \"Fetch the top two posts. What are their titles?\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "01ec4886-de3d-4fda-bd05-e3f254810969",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
    }
-   ],
-   "source": [
-    "example_query = \"Fetch the top two posts. What are their titles?\"\n",
-    "\n",
-    "events = agent_executor.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    event[\"messages\"][-1].pretty_print()"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "01ec4886-de3d-4fda-bd05-e3f254810969",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/tools/slack.ipynb
+++ b/docs/docs/integrations/tools/slack.ipynb
@@ -1,274 +1,272 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Slack Toolkit\n",
-    "\n",
-    "This will help you getting started with the Slack [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To use this toolkit, you will need to get a token as explained in the [Slack API docs](https://api.slack.com/tutorials/tracks/getting-a-token). Once you've received a SLACK_USER_TOKEN, you can input it as an environment variable below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"SLACK_USER_TOKEN\"):\n",
-    "    os.environ[\"SLACK_USER_TOKEN\"] = getpass.getpass(\"Enter your Slack user token: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "This toolkit lives in the `langchain-community` package. We will also need the Slack SDK:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community slack_sdk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optionally, we can install beautifulsoup4 to assist in parsing HTML messages:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU beautifulsoup4 # This is optional but is useful for parsing HTML messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our toolkit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.agent_toolkits import SlackToolkit\n",
-    "\n",
-    "toolkit = SlackToolkit()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tools\n",
-    "\n",
-    "View available tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "[SlackGetChannel(client=<slack_sdk.web.client.WebClient object at 0x113caa8c0>),\n",
-       " SlackGetMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa4d0>),\n",
-       " SlackScheduleMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa440>),\n",
-       " SlackSendMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa410>)]"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Slack Toolkit\n",
+        "\n",
+        "This will help you getting started with the Slack [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To use this toolkit, you will need to get a token as explained in the [Slack API docs](https://api.slack.com/tutorials/tracks/getting-a-token). Once you've received a SLACK_USER_TOKEN, you can input it as an environment variable below."
      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools = toolkit.get_tools()\n",
-    "\n",
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This toolkit loads:\n",
-    "\n",
-    "- [SlackGetChannel](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.get_channel.SlackGetChannel.html)\n",
-    "- [SlackGetMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.get_message.SlackGetMessage.html)\n",
-    "- [SlackScheduleMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.schedule_message.SlackScheduleMessage.html)\n",
-    "- [SlackSendMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.send_message.SlackSendMessage.html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use within an agent\n",
-    "\n",
-    "Let's equip an agent with the Slack toolkit and query for information about a channel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "agent_executor = create_react_agent(llm, tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "When was the #general channel created?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_channelid_name_dict (call_NXDkALjoOx97uF1v0CoZTqtJ)\n",
-      " Call ID: call_NXDkALjoOx97uF1v0CoZTqtJ\n",
-      "  Args:\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The #general channel was created on timestamp 1671043305.\n"
-     ]
-    }
-   ],
-   "source": [
-    "example_query = \"When was the #general channel created?\"\n",
-    "\n",
-    "events = agent_executor.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    message = event[\"messages\"][-1]\n",
-    "    if message.type != \"tool\":  # mask sensitive information\n",
-    "        event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"SLACK_USER_TOKEN\"):\n",
+        "    os.environ[\"SLACK_USER_TOKEN\"] = getpass.getpass(\"Enter your Slack user token: \")"
+      ]
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Send a friendly greeting to channel C072Q1LP4QM.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  send_message (call_xQxpv4wFeAZNZgSBJRIuaizi)\n",
-      " Call ID: call_xQxpv4wFeAZNZgSBJRIuaizi\n",
-      "  Args:\n",
-      "    message: Hello! Have a great day!\n",
-      "    channel: C072Q1LP4QM\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "I have sent a friendly greeting to the channel C072Q1LP4QM.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "This toolkit lives in the `langchain-community` package. We will also need the Slack SDK:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community slack_sdk"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Optionally, we can install beautifulsoup4 to assist in parsing HTML messages:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU beautifulsoup4 # This is optional but is useful for parsing HTML messages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our toolkit:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.agent_toolkits import SlackToolkit\n",
+        "\n",
+        "toolkit = SlackToolkit()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tools\n",
+        "\n",
+        "View available tools:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[SlackGetChannel(client=<slack_sdk.web.client.WebClient object at 0x113caa8c0>),\n",
+              " SlackGetMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa4d0>),\n",
+              " SlackScheduleMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa440>),\n",
+              " SlackSendMessage(client=<slack_sdk.web.client.WebClient object at 0x113caa410>)]"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tools = toolkit.get_tools()\n",
+        "\n",
+        "tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This toolkit loads:\n",
+        "\n",
+        "- [SlackGetChannel](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.get_channel.SlackGetChannel.html)\n",
+        "- [SlackGetMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.get_message.SlackGetMessage.html)\n",
+        "- [SlackScheduleMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.schedule_message.SlackScheduleMessage.html)\n",
+        "- [SlackSendMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.send_message.SlackSendMessage.html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Use within an agent\n",
+        "\n",
+        "Let's equip an agent with the Slack toolkit and query for information about a channel."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import ChatOpenAI\n",
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+        "\n",
+        "agent_executor = create_react_agent(llm, tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "When was the #general channel created?\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  get_channelid_name_dict (call_NXDkALjoOx97uF1v0CoZTqtJ)\n",
+            " Call ID: call_NXDkALjoOx97uF1v0CoZTqtJ\n",
+            "  Args:\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "The #general channel was created on timestamp 1671043305.\n"
+          ]
+        }
+      ],
+      "source": [
+        "example_query = \"When was the #general channel created?\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    message = event[\"messages\"][-1]\n",
+        "    if message.type != \"tool\":  # mask sensitive information\n",
+        "        event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "Send a friendly greeting to channel C072Q1LP4QM.\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  send_message (call_xQxpv4wFeAZNZgSBJRIuaizi)\n",
+            " Call ID: call_xQxpv4wFeAZNZgSBJRIuaizi\n",
+            "  Args:\n",
+            "    message: Hello! Have a great day!\n",
+            "    channel: C072Q1LP4QM\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "I have sent a friendly greeting to the channel C072Q1LP4QM.\n"
+          ]
+        }
+      ],
+      "source": [
+        "example_query = \"Send a friendly greeting to channel C072Q1LP4QM.\"\n",
+        "\n",
+        "events = agent_executor.stream(\n",
+        "    {\"messages\": [(\"user\", example_query)]},\n",
+        "    stream_mode=\"values\",\n",
+        ")\n",
+        "for event in events:\n",
+        "    message = event[\"messages\"][-1]\n",
+        "    if message.type != \"tool\":  # mask sensitive information\n",
+        "        event[\"messages\"][-1].pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `SlackToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
    }
-   ],
-   "source": [
-    "example_query = \"Send a friendly greeting to channel C072Q1LP4QM.\"\n",
-    "\n",
-    "events = agent_executor.stream(\n",
-    "    {\"messages\": [(\"user\", example_query)]},\n",
-    "    stream_mode=\"values\",\n",
-    ")\n",
-    "for event in events:\n",
-    "    message = event[\"messages\"][-1]\n",
-    "    if message.type != \"tool\":  # mask sensitive information\n",
-    "        event[\"messages\"][-1].pretty_print()"
-   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `SlackToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
--- a/docs/docs/integrations/tools/sql_database.ipynb
+++ b/docs/docs/integrations/tools/sql_database.ipynb
--- a/docs/docs/integrations/tools/tavily_extract.ipynb
+++ b/docs/docs/integrations/tools/tavily_extract.ipynb
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -0,0 +1 @@`
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				`@@ -1 +0,0 @@`
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