add chain

.github/scripts/check_diff.py

@@ -95,15 +95,6 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         # declare deps in funny way
         max_python = "3.11"
 
-    if dir_ in ["libs/community", "libs/langchain"] and job == "extended-tests":
-        # community extended test resolution in 3.12 is slow
-        # even in uv
-        max_python = "3.11"
-
-    if dir_ == "libs/community" and job == "compile-integration-tests":
-        # community integration deps are slow in 3.12
-        max_python = "3.11"
-
     return [
         {"working-directory": dir_, "python-version": min_python},
         {"working-directory": dir_, "python-version": max_python},

cookbook/README.md

@@ -36,7 +36,6 @@ Notebook | Description
 [llm_symbolic_math.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_symbolic_math.ipynb) | Solve algebraic equations with the help of llms (language learning models) and sympy, a python library for symbolic mathematics.
 [meta_prompt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/meta_prompt.ipynb) | Implement the meta-prompt concept, which is a method for building self-improving agents that reflect on their own performance and modify their instructions accordingly.
 [multi_modal_output_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_modal_output_agent.ipynb) | Generate multi-modal outputs, specifically images and text.
-[multi_modal_RAG_vdms.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_modal_RAG_vdms.ipynb) | Perform retrieval-augmented generation (rag) on documents including text and images, using unstructured for parsing, Intel's Visual Data Management System (VDMS) as the vectorstore, and chains.
 [multi_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_player_dnd.ipynb) | Simulate multi-player dungeons & dragons games, with a custom function determining the speaking schedule of the agents.
 [multiagent_authoritarian.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_authoritarian.ipynb) | Implement a multi-agent simulation where a privileged agent controls the conversation, including deciding who speaks and when the conversation ends, in the context of a simulated news network.
 [multiagent_bidding.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_bidding.ipynb) | Implement a multi-agent simulation where agents bid to speak, with the highest bidder speaking next, demonstrated through a fictitious presidential debate example.

cookbook/multi_modal_RAG_vdms.ipynb

@@ -18,7 +18,26 @@
     "* Use of multimodal embeddings (such as [CLIP](https://openai.com/research/clip)) to embed images and text\n",
     "* Use of [VDMS](https://github.com/IntelLabs/vdms/blob/master/README.md) as a vector store with support for multi-modal\n",
     "* Retrieval of both images and text using similarity search\n",
-    "* Passing raw images and text chunks to a multimodal LLM for answer synthesis "
+    "* Passing raw images and text chunks to a multimodal LLM for answer synthesis \n",
+    "\n",
+    "\n",
+    "## Packages\n",
+    "\n",
+    "For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# (newest versions required for multi-modal)\n",
+    "! pip install --quiet -U vdms langchain-experimental\n",
+    "\n",
+    "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
+    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
    ]
   },
   {
@@ -34,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "5f483872",
    "metadata": {},
    "outputs": [
@@ -42,7 +61,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "a1b9206b08ef626e15b356bf9e031171f7c7eb8f956a2733f196f0109246fe2b\n"
+      "docker: Error response from daemon: Conflict. The container name \"/vdms_rag_nb\" is already in use by container \"0c19ed281463ac10d7efe07eb815643e3e534ddf24844357039453ad2b0c27e8\". You have to remove (or rename) that container to be able to reuse that name.\n",
+      "See 'docker run --help'.\n"
      ]
     }
    ],
@@ -55,32 +75,9 @@
     "vdms_client = VDMS_Client(port=55559)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "2498a0a1",
-   "metadata": {},
-   "source": [
-    "## Packages\n",
-    "\n",
-    "For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install --quiet -U vdms langchain-experimental\n",
-    "\n",
-    "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
-    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "78ac6543",
    "metadata": {},
    "outputs": [],
@@ -98,9 +95,14 @@
     "\n",
     "### Partition PDF text and images\n",
     "  \n",
-    "Let's use famous photographs from the PDF version of Library of Congress Magazine in this example.\n",
+    "Let's look at an example pdf containing interesting images.\n",
     "\n",
-    "We can use `partition_pdf` from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images."
+    "Famous photographs from library of congress:\n",
+    "\n",
+    "* https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\n",
+    "* We'll use this as an example below\n",
+    "\n",
+    "We can use `partition_pdf` below from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images."
    ]
   },
   {
@@ -114,8 +116,8 @@
     "\n",
     "import requests\n",
     "\n",
-    "# Folder to store pdf and extracted images\n",
-    "datapath = Path(\"./data/multimodal_files\").resolve()\n",
+    "# Folder with pdf and extracted images\n",
+    "datapath = Path(\"./multimodal_files\").resolve()\n",
     "datapath.mkdir(parents=True, exist_ok=True)\n",
     "\n",
     "pdf_url = \"https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\"\n",
@@ -172,8 +174,14 @@
    "source": [
     "## Multi-modal embeddings with our document\n",
     "\n",
-    "In this section, we initialize the VDMS vector store for both text and images. For better performance, we use model `ViT-g-14` from [OpenClip multimodal embeddings](https://python.langchain.com/docs/integrations/text_embedding/open_clip).\n",
-    "The images are stored as base64 encoded strings with `vectorstore.add_images`.\n"
+    "We will use [OpenClip multimodal embeddings](https://python.langchain.com/docs/integrations/text_embedding/open_clip).\n",
+    "\n",
+    "We use a larger model for better performance (set in `langchain_experimental.open_clip.py`).\n",
+    "\n",
+    "```\n",
+    "model_name = \"ViT-g-14\"\n",
+    "checkpoint = \"laion2b_s34b_b88k\"\n",
+    "```"
    ]
   },
   {
@@ -192,7 +200,9 @@
     "vectorstore = VDMS(\n",
     "    client=vdms_client,\n",
     "    collection_name=\"mm_rag_clip_photos\",\n",
-    "    embedding=OpenCLIPEmbeddings(model_name=\"ViT-g-14\", checkpoint=\"laion2b_s34b_b88k\"),\n",
+    "    embedding_function=OpenCLIPEmbeddings(\n",
+    "        model_name=\"ViT-g-14\", checkpoint=\"laion2b_s34b_b88k\"\n",
+    "    ),\n",
     ")\n",
     "\n",
     "# Get image URIs with .jpg extension only\n",
@@ -223,7 +233,7 @@
    "source": [
     "## RAG\n",
     "\n",
-    "Here we define helper functions for image results."
+    "`vectorstore.add_images` will store / retrieve images as base64 encoded strings."
    ]
   },
   {
@@ -382,8 +392,7 @@
    "id": "1566096d-97c2-4ddc-ba4a-6ef88c525e4e",
    "metadata": {},
    "source": [
-    "## Test retrieval and run RAG\n",
-    "Now let's query for a `woman with children` and retrieve the top results."
+    "## Test retrieval and run RAG"
    ]
   },
   {
@@ -443,14 +452,6 @@
     "        print(doc.page_content)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "15e9b54d",
-   "metadata": {},
-   "source": [
-    "Now let's use the `multi_modal_rag_chain` to process the same query and display the response."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -461,10 +462,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The image depicts a woman with several children. The woman appears to be of Cherokee heritage, as suggested by the text provided. The image is described as having been initially regretted by the subject, Florence Owens Thompson, due to her feeling that it did not accurately represent her leadership qualities.\n",
-      "The historical and cultural context of the image is tied to the Great Depression and the Dust Bowl, both of which affected the Cherokee people in Oklahoma. The photograph was taken during this period, and its subject, Florence Owens Thompson, was a leader within her community who worked tirelessly to help those affected by these crises.\n",
-      "The image's symbolism and meaning can be interpreted as a representation of resilience and strength in the face of adversity. The woman is depicted with multiple children, which could signify her role as a caregiver and protector during difficult times.\n",
-      "Connections between the image and the related text include Florence Owens Thompson's leadership qualities and her regretted feelings about the photograph. Additionally, the mention of Dorothea Lange, the photographer who took this photo, ties the image to its historical context and the broader narrative of the Great Depression and Dust Bowl in Oklahoma. \n"
+      "1. Detailed description of the visual elements in the image: The image features a woman with children, likely a mother and her family, standing together outside. They appear to be poor or struggling financially, as indicated by their attire and surroundings.\n",
+      "2. Historical and cultural context of the image: The photo was taken in 1936 during the Great Depression, when many families struggled to make ends meet. Dorothea Lange, a renowned American photographer, took this iconic photograph that became an emblem of poverty and hardship experienced by many Americans at that time.\n",
+      "3. Interpretation of the image's symbolism and meaning: The image conveys a sense of unity and resilience despite adversity. The woman and her children are standing together, displaying their strength as a family unit in the face of economic challenges. The photograph also serves as a reminder of the importance of empathy and support for those who are struggling.\n",
+      "4. Connections between the image and the related text: The text provided offers additional context about the woman in the photo, her background, and her feelings towards the photograph. It highlights the historical backdrop of the Great Depression and emphasizes the significance of this particular image as a representation of that time period.\n"
      ]
     }
    ],
@@ -491,6 +492,14 @@
    "source": [
     "! docker kill vdms_rag_nb"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ba652da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -509,7 +518,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

docs/Makefile

@@ -42,8 +42,6 @@ generate-files:
 
 	$(PYTHON) scripts/document_loader_feat_table.py $(INTERMEDIATE_DIR)
 
-	$(PYTHON) scripts/partner_pkg_table.py $(INTERMEDIATE_DIR)
-
 	$(PYTHON) scripts/copy_templates.py $(INTERMEDIATE_DIR)
 
 	wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O $(INTERMEDIATE_DIR)/langserve.md

docs/docs/concepts.mdx

@@ -90,7 +90,7 @@ LCEL aims to provide consistency around behavior and customization over legacy s
 `ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety
 of viable models emerge, customization has become more and more important.
 
-If you are currently using one of these legacy chains, please see [this guide for guidance on how to migrate](/docs/versions/migrating_chains).
+If you are currently using one of these legacy chains, please see [this guide for guidance on how to migrate](/docs/how_to/migrate_chains/).
 
 For guides on how to do specific tasks with LCEL, check out [the relevant how-to guides](/docs/how_to/#langchain-expression-language-lcel).
 
@@ -165,7 +165,7 @@ Some important things to note:
 ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the API reference for that model.
 
 :::important
-Some chat models have been fine-tuned for **tool calling** and provide a dedicated API for it.
+**Tool Calling** Some chat models have been fine-tuned for tool calling and provide a dedicated API for tool calling.
 Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling.
 Please see the [tool calling section](/docs/concepts/#functiontool-calling) for more information.
 :::
@@ -255,7 +255,7 @@ This represents the result of a tool call. In addition to `role` and `content`,
 
 #### (Legacy) FunctionMessage
 
-This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.
+This is a legacy message type, corresponding to OpenAI's legacy function-calling API. ToolMessage should be used instead to correspond to the updated tool-calling API.
 
 This represents the result of a function call. In addition to `role` and `content`, this message has a `name` parameter which conveys the name of the function that was called to produce this result.
 
@@ -498,29 +498,6 @@ Retrievers accept a string query as input and return a list of Document's as out
 
 For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
 
-### Key-value stores
-
-For some techniques, such as [indexing and retrieval with multiple vectors per document](/docs/how_to/multi_vector/), having some sort of key-value (KV) storage is helpful.
-
-LangChain includes a [`BaseStore`](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.BaseStore.html) interface,
-which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
-more specific `BaseStore[str, bytes]` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
-encoding and decoding data for their specific needs.
-
-This means that as a user, you only need to think about one type of store rather than different ones for different types of data.
-
-#### Interface
-
-All [`BaseStores`](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
-for modifying **multiple** key-value pairs at once:
-
-- `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
-- `mset(key_value_pairs: Sequence[Tuple[str, bytes]]) -> None`: set the contents of multiple keys
-- `mdelete(key: Sequence[str]) -> None`: delete multiple keys
-- `yield_keys(prefix: Optional[str] = None) -> Iterator[str]`: yield all keys in the store, optionally filtering by a prefix
-
-For key-value store implementations, see [this section](/docs/integrations/stores/).
-
 ### Tools
 <span data-heading-keywords="tool,tools"></span>
 
@@ -849,61 +826,6 @@ units (like words or subwords) that carry meaning, rather than individual charac
 to learn and understand the structure of the language, including grammar and context.
 Furthermore, using tokens can also improve efficiency, since the model processes fewer units of text compared to character-level processing.
 
-### Function/tool calling
-
-:::info
-We use the term tool calling interchangeably with function calling. Although
-function calling is sometimes meant to refer to invocations of a single function,
-we treat all models as though they can return multiple tool or function calls in
-each message.
-:::
-
-Tool calling allows a [chat model](/docs/concepts/#chat-models) to respond to a given prompt by generating output that
-matches a user-defined schema.
-
-While the name implies that the model is performing
-some action, this is actually not the case! The model only generates the arguments to a tool, and actually running the tool (or not) is up to the user.
-One common example where you **wouldn't** want to call a function with the generated arguments
-is if you want to [extract structured output matching some schema](/docs/concepts/#structured-output)
-from unstructured text. You would give the model an "extraction" tool that takes
-parameters matching the desired schema, then treat the generated output as your final
-result.
-
-![Diagram of a tool call by a chat model](/img/tool_call.png)
-
-Tool calling is not universal, but is supported by many popular LLM providers, including [Anthropic](/docs/integrations/chat/anthropic/), 
-[Cohere](/docs/integrations/chat/cohere/), [Google](/docs/integrations/chat/google_vertex_ai_palm/), 
-[Mistral](/docs/integrations/chat/mistralai/), [OpenAI](/docs/integrations/chat/openai/), and even for locally-running models via [Ollama](/docs/integrations/chat/ollama/).
-
-LangChain provides a standardized interface for tool calling that is consistent across different models.
-
-The standard interface consists of:
-
-* `ChatModel.bind_tools()`: a method for specifying which tools are available for a model to call. This method accepts [LangChain tools](/docs/concepts/#tools) as well as [Pydantic](https://pydantic.dev/) objects.
-* `AIMessage.tool_calls`: an attribute on the `AIMessage` returned from the model for accessing the tool calls requested by the model.
-
-#### Tool usage
-
-After the model calls tools, you can use the tool by invoking it, then passing the arguments back to the model.
-LangChain provides the [`Tool`](/docs/concepts/#tools) abstraction to help you handle this.
-
-The general flow is this:
-
-1. Generate tool calls with a chat model in response to a query.
-2. Invoke the appropriate tools using the generated tool call as arguments.
-3. Format the result of the tool invocations as [`ToolMessages`](/docs/concepts/#toolmessage).
-4. Pass the entire list of messages back to the model so that it can generate a final answer (or call more tools).
-
-![Diagram of a complete tool calling flow](/img/tool_calling_flow.png)
-
-This is how tool calling [agents](/docs/concepts/#agents) perform tasks and answer queries.
-
-Check out some more focused guides below:
-
-- [How to use chat models to call tools](/docs/how_to/tool_calling/)
-- [How to pass tool outputs to chat models](/docs/how_to/tool_results_pass_to_model/)
-- [Building an agent with LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
-
 ### Structured output
 
 LLMs are capable of generating arbitrary text. This enables the model to respond appropriately to a wide
@@ -1036,48 +958,48 @@ chain.invoke({ "question": "What is the powerhouse of the cell?" })
 
 For a full list of model providers that support JSON mode, see [this table](/docs/integrations/chat/#advanced-features).
 
-#### Tool calling {#structured-output-tool-calling}
+#### Function/tool calling
 
-For models that support it, [tool calling](/docs/concepts/#functiontool-calling) can be very convenient for structured output. It removes the
-guesswork around how best to prompt schemas in favor of a built-in model feature.
+:::info
+We use the term tool calling interchangeably with function calling. Although
+function calling is sometimes meant to refer to invocations of a single function,
+we treat all models as though they can return multiple tool or function calls in
+each message
+:::
 
-It works by first binding the desired schema either directly or via a [LangChain tool](/docs/concepts/#tools) to a
-[chat model](/docs/concepts/#chat-models) using the `.bind_tools()` method. The model will then generate an `AIMessage` containing
-a `tool_calls` field containing `args` that match the desired shape.
+Tool calling allows a model to respond to a given prompt by generating output that
+matches a user-defined schema. While the name implies that the model is performing
+some action, this is actually not the case! The model is coming up with the
+arguments to a tool, and actually running the tool (or not) is up to the user -
+for example, if you want to [extract output matching some schema](/docs/tutorials/extraction)
+from unstructured text, you could give the model an "extraction" tool that takes
+parameters matching the desired schema, then treat the generated output as your final
+result.
 
-There are several acceptable formats you can use to bind tools to a model in LangChain. Here's one example:
+For models that support it, tool calling can be very convenient. It removes the
+guesswork around how best to prompt schemas in favor of a built-in model feature. It can also
+more naturally support agentic flows, since you can just pass multiple tool schemas instead
+of fiddling with enums or unions.
 
-```python
-from langchain_core.pydantic_v1 import BaseModel, Field
-from langchain_openai import ChatOpenAI
+Many LLM providers, including [Anthropic](https://www.anthropic.com/),
+[Cohere](https://cohere.com/), [Google](https://cloud.google.com/vertex-ai),
+[Mistral](https://mistral.ai/), [OpenAI](https://openai.com/), and others,
+support variants of a tool calling feature. These features typically allow requests
+to the LLM to include available tools and their schemas, and for responses to include
+calls to these tools. For instance, given a search engine tool, an LLM might handle a
+query by first issuing a call to the search engine. The system calling the LLM can
+receive the tool call, execute it, and return the output to the LLM to inform its
+response. LangChain includes a suite of [built-in tools](/docs/integrations/tools/)
+and supports several methods for defining your own [custom tools](/docs/how_to/custom_tools).
 
-class ResponseFormatter(BaseModel):
-    """Always use this tool to structure your response to the user."""
+LangChain provides a standardized interface for tool calling that is consistent across different models.
 
-    answer: str = Field(description="The answer to the user's question")
-    followup_question: str = Field(description="A followup question the user could ask")
+The standard interface consists of:
 
-model = ChatOpenAI(
-    model="gpt-4o",
-    temperature=0,
-)
+* `ChatModel.bind_tools()`: a method for specifying which tools are available for a model to call. This method accepts [LangChain tools](/docs/concepts/#tools) here.
+* `AIMessage.tool_calls`: an attribute on the `AIMessage` returned from the model for accessing the tool calls requested by the model.
 
-model_with_tools = model.bind_tools([ResponseFormatter])
-
-ai_msg = model_with_tools.invoke("What is the powerhouse of the cell?")
-
-ai_msg.tool_calls[0]["args"]
-```
-
-```
-{'answer': "The powerhouse of the cell is the mitochondrion. It generates most of the cell's supply of adenosine triphosphate (ATP), which is used as a source of chemical energy.",
- 'followup_question': 'How do mitochondria generate ATP?'}
-```
-
-Tool calling is a generally consistent way to get a model to generate structured output, and is the default technique
-used for the [`.with_structured_output()`](/docs/concepts/#with_structured_output) method when a model supports it.
-
-The following how-to guides are good practical resources for using function/tool calling for structured output:
+The following how-to guides are good practical resources for using function/tool calling:
 
 - [How to return structured data from an LLM](/docs/how_to/structured_output/)
 - [How to use a model to call tools](/docs/how_to/tool_calling)

docs/docs/how_to/chat_model_rate_limiting.ipynb

@@ -1,146 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dcf87b32",
-   "metadata": {},
-   "source": [
-    "# How to handle rate limits\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/#chat-models)\n",
-    "- [LLMs](/docs/concepts/#llms)\n",
-    ":::\n",
-    "\n",
-    "\n",
-    "You may find yourself in a situation where you are getting rate limited by the model provider API because you're making too many requests.\n",
-    "\n",
-    "For example, this might happen if you are running many parallel queries to benchmark the chat model on a test dataset.\n",
-    "\n",
-    "If you are facing such a situation, you can use a rate limiter to help match the rate at which you're making request to the rate allowed\n",
-    "by the API.\n",
-    "\n",
-    ":::info Requires ``langchain-core >= 0.2.24``\n",
-    "\n",
-    "This functionality was added in ``langchain-core == 0.2.24``. Please make sure your package is up to date.\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cbc3c873-6109-4e03-b775-b73c1003faea",
-   "metadata": {},
-   "source": [
-    "## Initialize a rate limiter\n",
-    "\n",
-    "Langchain comes with a built-in in memory rate limiter. This rate limiter is thread safe and can be shared by multiple threads in the same process.\n",
-    "\n",
-    "The provided rate limiter can only limit the number of requests per unit time. It will not help if you need to also limited based on the size\n",
-    "of the requests."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "aa9c3c8c-0464-4190-a8c5-d69d173505a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.rate_limiters import InMemoryRateLimiter\n",
-    "\n",
-    "rate_limiter = InMemoryRateLimiter(\n",
-    "    requests_per_second=0.1,  # <-- Super slow! We can only make a request once every 10 seconds!!\n",
-    "    check_every_n_seconds=0.1,  # Wake up every 100 ms to check whether allowed to make a request,\n",
-    "    max_bucket_size=10,  # Controls the maximum burst size.\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e058bde-9413-4b08-8cc6-0c9cb638f19f",
-   "metadata": {},
-   "source": [
-    "## Choose a model\n",
-    "\n",
-    "Choose any model and pass to it the rate_limiter via the `rate_limiter` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0f880a3a-c047-4e94-a323-fff2a4c0e96d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import time\n",
-    "from getpass import getpass\n",
-    "\n",
-    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
-    "\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "model = ChatAnthropic(model_name=\"claude-3-opus-20240229\", rate_limiter=rate_limiter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80c9ab3a-299a-460f-985c-90280a046f52",
-   "metadata": {},
-   "source": [
-    "Let's confirm that the rate limiter works. We should only be able to invoke the model once per 10 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d074265c-9f32-4c5f-b914-944148993c4d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11.599073648452759\n",
-      "10.7502121925354\n",
-      "10.244257926940918\n",
-      "8.83088755607605\n",
-      "11.645203590393066\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _ in range(5):\n",
-    "    tic = time.time()\n",
-    "    model.invoke(\"hello\")\n",
-    "    toc = time.time()\n",
-    "    print(toc - tic)"
-   ]
-  }
- ],
- "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
-}

docs/docs/how_to/code_splitter.ipynb

@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2bb9c73f-9d00-4a19-a81f-cab2f0fd921a",
+   "id": "9e4144de-d925-4d4c-91c3-685ef8baa57c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "a9e37aa1",
    "metadata": {},
    "outputs": [],
@@ -718,44 +718,8 @@
     "php_splitter = RecursiveCharacterTextSplitter.from_language(\n",
     "    language=Language.PHP, chunk_size=50, chunk_overlap=0\n",
     ")\n",
-    "php_docs = php_splitter.create_documents([PHP_CODE])\n",
-    "php_docs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9fa62c1",
-   "metadata": {},
-   "source": [
-    "## PowerShell\n",
-    "Here's an example using the PowerShell text splitter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e6893ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "POWERSHELL_CODE = \"\"\"\n",
-    "$directoryPath = Get-Location\n",
-    "\n",
-    "$items = Get-ChildItem -Path $directoryPath\n",
-    "\n",
-    "$files = $items | Where-Object { -not $_.PSIsContainer }\n",
-    "\n",
-    "$sortedFiles = $files | Sort-Object LastWriteTime\n",
-    "\n",
-    "foreach ($file in $sortedFiles) {\n",
-    "    Write-Output (\"Name: \" + $file.Name + \" | Last Write Time: \" + $file.LastWriteTime)\n",
-    "}\n",
-    "\"\"\"\n",
-    "powershell_splitter = RecursiveCharacterTextSplitter.from_language(\n",
-    "    language=Language.POWERSHELL, chunk_size=100, chunk_overlap=0\n",
-    ")\n",
-    "powershell_docs = powershell_splitter.create_documents([POWERSHELL_CODE])\n",
-    "powershell_docs"
+    "haskell_docs = php_splitter.create_documents([PHP_CODE])\n",
+    "haskell_docs"
    ]
   }
  ],
@@ -775,7 +739,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/docs/how_to/index.mdx

@@ -31,8 +31,6 @@ This highlights functionality that is core to using LangChain.
 
 [**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
 
-[**Migration guide**](/docs/versions/migrating_chains): For migrating legacy chain abstractions to LCEL.
-
 - [How to: chain runnables](/docs/how_to/sequence)
 - [How to: stream runnables](/docs/how_to/streaming)
 - [How to: invoke runnables in parallel](/docs/how_to/parallel/)
@@ -45,6 +43,7 @@ This highlights functionality that is core to using LangChain.
 - [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
 - [How to: inspect runnables](/docs/how_to/inspect)
 - [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
+- [How to: migrate chains to LCEL](/docs/how_to/migrate_chains)
 - [How to: pass runtime secrets to a runnable](/docs/how_to/runnable_runtime_secrets)
 
 ## Components
@@ -84,7 +83,6 @@ These are the core building blocks you can use when building applications.
 - [How to: track response metadata across providers](/docs/how_to/response_metadata)
 - [How to: use chat model to call tools](/docs/how_to/tool_calling)
 - [How to: stream tool calls](/docs/how_to/tool_streaming)
-- [How to: handle rate limits](/docs/how_to/chat_model_rate_limiting)
 - [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot)
 - [How to: bind model-specific formatted tools](/docs/how_to/tools_model_specific)
 - [How to: force a specific tool call](/docs/how_to/tool_choice)

docs/docs/how_to/migrate_agent.ipynb

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "662fac50",
    "metadata": {},
    "outputs": [],
@@ -50,26 +50,6 @@
     "%pip install -U langgraph langchain langchain-openai"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6f8ec38f",
-   "metadata": {},
-   "source": [
-    "Then, set your OpenAI API key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5fca87ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8e50635c-1671-46e6-be65-ce95f8167c2f",
@@ -82,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "1e425fea-2796-4b99-bee6-9a6ffe73f756",
    "metadata": {},
    "outputs": [],
@@ -115,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "03ea357c-9c36-4464-b2cc-27bd150e1554",
    "metadata": {},
    "outputs": [
@@ -126,7 +106,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "53a3737a-d167-4255-89bf-20ac37f89a3e",
    "metadata": {},
    "outputs": [
@@ -173,7 +153,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "74ecebe3-512e-409c-a661-bdd5b0a2b782",
    "metadata": {},
    "outputs": [
@@ -201,10 +181,10 @@
      "data": {
       "text/plain": [
        "{'input': 'Pardon?',\n",
-       " 'output': 'The value you get when you apply `magic_function` to the input 3 is 5.'}"
+       " 'output': 'The result of applying `magic_function` to the input 3 is 5.'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -243,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "a9a11ccd-75e2-4c11-844d-a34870b0ff91",
    "metadata": {},
    "outputs": [
@@ -254,7 +234,7 @@
        " 'output': 'El valor de `magic_function(3)` es 5.'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,19 +263,19 @@
    "source": [
     "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
     "\n",
-    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`state_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies the graph state before the llm is called, and can be one of four values:\n",
+    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies messages before they are passed into the model, and can be one of four values:\n",
     "\n",
     "- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
     "- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
-    "- A `Callable`, which should take in full graph state. The output is then passed to the language model.\n",
-    "- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should take in full graph state. The output is then passed to the language model.\n",
+    "- A `Callable`, which should take in a list of messages. The output is then passed to the language model.\n",
+    "- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should should take in a list of messages. The output is then passed to the language model.\n",
     "\n",
     "Here's how it looks in action:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "a9486805-676a-4d19-a5c4-08b41b172989",
    "metadata": {},
    "outputs": [],
@@ -307,7 +287,7 @@
     "# This could also be a SystemMessage object\n",
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=system_message)\n",
+    "app = create_react_agent(model, tools, messages_modifier=system_message)\n",
     "\n",
     "\n",
     "messages = app.invoke({\"messages\": [(\"user\", query)]})"
@@ -324,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "d369ab45-0c82-45f4-9d3e-8efb8dd47e2c",
    "metadata": {},
    "outputs": [
@@ -337,8 +317,8 @@
     }
    ],
    "source": [
+    "from langchain_core.messages import AnyMessage\n",
     "from langgraph.prebuilt import create_react_agent\n",
-    "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -348,13 +328,13 @@
     ")\n",
     "\n",
     "\n",
-    "def _modify_state_messages(state: AgentState):\n",
-    "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages() + [\n",
+    "def _modify_messages(messages: list[AnyMessage]):\n",
+    "    return prompt.invoke({\"messages\": messages}).to_messages() + [\n",
     "        (\"user\", \"Also say 'Pandamonium!' after the answer.\")\n",
     "    ]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
     "\n",
     "\n",
     "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
@@ -386,8 +366,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "b97beba5-8f74-430c-9399-91b77c8fa15c",
+   "execution_count": 8,
+   "id": "1fb52a2c",
    "metadata": {},
    "outputs": [
     {
@@ -396,7 +376,7 @@
      "text": [
       "Hi Polly! The output of the magic function for the input 3 is 5.\n",
       "---\n",
-      "Yes, your name is Polly!\n",
+      "Yes, I remember your name, Polly! How can I assist you further?\n",
       "---\n",
       "The output of the magic function for the input 3 is 5.\n"
      ]
@@ -404,14 +384,14 @@
    ],
    "source": [
     "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_core.chat_history import InMemoryChatMessageHistory\n",
+    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
     "from langchain_core.tools import tool\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "memory = InMemoryChatMessageHistory(session_id=\"test-session\")\n",
+    "memory = ChatMessageHistory(session_id=\"test-session\")\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
     "        (\"system\", \"You are a helpful assistant.\"),\n",
@@ -476,23 +456,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "baca3dc6-678b-4509-9275-2fd653102898",
+   "execution_count": 9,
+   "id": "035e1253",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hi Polly! The output of the magic_function for the input of 3 is 5.\n",
+      "Hi Polly! The output of the magic_function for the input 3 is 5.\n",
       "---\n",
       "Yes, your name is Polly!\n",
       "---\n",
-      "The output of the magic_function for the input of 3 was 5.\n"
+      "The output of the magic_function for the input 3 was 5.\n"
      ]
     }
    ],
    "source": [
+    "from langchain_core.messages import SystemMessage\n",
     "from langgraph.checkpoint import MemorySaver  # an in-memory checkpointer\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
@@ -502,7 +483,7 @@
     "\n",
     "memory = MemorySaver()\n",
     "app = create_react_agent(\n",
-    "    model, tools, state_modifier=system_message, checkpointer=memory\n",
+    "    model, tools, messages_modifier=system_message, checkpointer=memory\n",
     ")\n",
     "\n",
     "config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
@@ -544,16 +525,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "e62843c4-1107-41f0-a50b-aea256e28053",
+   "execution_count": 10,
+   "id": "d640feb3",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_1exy0rScfPmo4fy27FbQ5qJ2')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])]}\n",
-      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_1exy0rScfPmo4fy27FbQ5qJ2'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
+      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])]}\n",
+      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
       "{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.')]}\n"
      ]
     }
@@ -604,23 +585,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "076ebc85-f804-4093-a25a-a16334c9898e",
+   "execution_count": 11,
+   "id": "86abbe07",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_my9rzFSKR4T1yYKwCsfbZB8A', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 61, 'total_tokens': 75}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_bc2a86f5f5', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-dd705555-8fae-4fb1-a033-5d99a23e3c22-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_my9rzFSKR4T1yYKwCsfbZB8A', 'type': 'tool_call'}], usage_metadata={'input_tokens': 61, 'output_tokens': 14, 'total_tokens': 75})]}}\n",
-      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', tool_call_id='call_my9rzFSKR4T1yYKwCsfbZB8A')]}}\n",
-      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-698cad05-8cb2-4d08-8c2a-881e354f6cc7-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}}\n"
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yTjXXibj76tyFyPRa1soLo0S', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 70, 'total_tokens': 84}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b275f314-c42e-4e77-9dec-5c23f7dbd53b-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_yTjXXibj76tyFyPRa1soLo0S'}])]}}\n",
+      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', id='41c5f227-528d-4483-a313-b03b23b1d327', tool_call_id='call_yTjXXibj76tyFyPRa1soLo0S')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 93, 'total_tokens': 107}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-0ef12b6e-415d-4758-9b62-5e5e1b350072-0')]}}\n"
      ]
     }
    ],
    "source": [
+    "from langchain_core.messages import AnyMessage\n",
     "from langgraph.prebuilt import create_react_agent\n",
-    "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -630,11 +611,12 @@
     ")\n",
     "\n",
     "\n",
-    "def _modify_state_messages(state: AgentState):\n",
-    "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages()\n",
+    "def _modify_messages(messages: list[AnyMessage]):\n",
+    "    return prompt.invoke({\"messages\": messages}).to_messages()\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
+    "\n",
     "\n",
     "for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
     "    print(step)"
@@ -655,14 +637,14 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "a2f720f3-c121-4be2-b498-92c16bb44b0a",
+   "id": "4eff44bc-a620-4c8a-97b1-268692a842bb",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-a792db4a-278d-4090-82ae-904a30eada93', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_uPZ2D1Bo5mdED3gwgaeWURrf'), 5)]\n"
+      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-837e794f-cfd8-40e0-8abc-4d98ced11b75', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'index': 0}])], tool_call_id='call_ABI4hftfEdnVgKyfF6OzZbca'), 5)]\n"
      ]
     }
    ],
@@ -685,16 +667,16 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "ef23117a-5ccb-42ce-80c3-ea49a9d3a942",
+   "id": "4f4364ea-dffe-4d25-bdce-ef7d0020b880",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='cd7d0f49-a0e0-425a-b2b0-603a716058ed'),\n",
-       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_VfZ9287DuybOSrBsQH5X12xf', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1e965cd-bf61-44f9-aec1-8aaecb80955f-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_VfZ9287DuybOSrBsQH5X12xf', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}),\n",
-       "  ToolMessage(content='5', name='magic_function', id='20d5c2fe-a5d8-47fa-9e04-5282642e2039', tool_call_id='call_VfZ9287DuybOSrBsQH5X12xf'),\n",
-       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 78, 'total_tokens': 92}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-abf9341c-ef41-4157-935d-a3be5dfa2f41-0', usage_metadata={'input_tokens': 78, 'output_tokens': 14, 'total_tokens': 92})]}"
+       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='0f63e437-c4d8-4da9-b6f5-b293ebfe4a64'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_S96v28LlI6hNkQrNnIio0JPh', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ffef7898-14b1-4537-ad90-7c000a8a5d25-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_S96v28LlI6hNkQrNnIio0JPh'}]),\n",
+       "  ToolMessage(content='5', name='magic_function', id='fbd9df4e-1dda-4d3e-9044-b001f7875476', tool_call_id='call_S96v28LlI6hNkQrNnIio0JPh'),\n",
+       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 87, 'total_tokens': 101}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-e5d94c54-d9f4-45cd-be8e-a9101a8d88d6-0')]}"
       ]
      },
      "execution_count": 13,
@@ -726,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "16f189a7-fc78-4cb5-aa16-a94ca06401a6",
    "metadata": {},
    "outputs": [],
@@ -742,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "c96aefd7-6f6e-4670-aca6-1ac3d4e7871f",
    "metadata": {},
    "outputs": [
@@ -757,7 +739,11 @@
       "Invoking: `magic_function` with `{'input': '3'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mParece que hubo un error al intentar calcular el valor de la función mágica. ¿Te gustaría que lo intente de nuevo?\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "Invoking: `magic_function` with `{'input': '3'}`\n",
+      "responded: Parece que hubo un error al intentar obtener el valor de `magic_function(3)`. Permíteme intentarlo de nuevo.\n",
+      "\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mAún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -766,10 +752,10 @@
      "data": {
       "text/plain": [
        "{'input': 'what is the value of magic_function(3)?',\n",
-       " 'output': 'Parece que hubo un error al intentar calcular el valor de la función mágica. ¿Te gustaría que lo intente de nuevo?'}"
+       " 'output': 'Aún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?'}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -811,7 +797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "b974a91f-6ae8-4644-83d9-73666258a6db",
    "metadata": {},
    "outputs": [
@@ -819,12 +805,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='what is the value of magic_function(3)?' id='74e2d5e8-2b59-4820-979c-8d11ecfc14c2'\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_ihtrH6IG95pDXpKluIwAgi3J', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-5a35e465-8a08-43dd-ac8b-4a76dcace305-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_ihtrH6IG95pDXpKluIwAgi3J', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='8c37c19b-3586-46b1-aab9-a045786801a2' tool_call_id='call_ihtrH6IG95pDXpKluIwAgi3J'\n",
-      "content='It seems there was an error in processing the request. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_iF0vYWAd6rfely0cXSqdMOnF', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 88, 'total_tokens': 119}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-eb88ec77-d492-43a5-a5dd-4cefef9a6920-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_iF0vYWAd6rfely0cXSqdMOnF', 'type': 'tool_call'}] usage_metadata={'input_tokens': 88, 'output_tokens': 31, 'total_tokens': 119}\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='c9ff261f-a0f1-4c92-a9f2-cd749f62d911' tool_call_id='call_iF0vYWAd6rfely0cXSqdMOnF'\n",
-      "content='I am currently unable to process the request with the input \"3\" for the `magic_function`. If you have any other questions or need assistance with something else, please let me know!' response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 141, 'total_tokens': 180}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None} id='run-d42508aa-f286-4b57-80fb-f8a76736d470-0' usage_metadata={'input_tokens': 141, 'output_tokens': 39, 'total_tokens': 180}\n"
+      "('human', 'what is the value of magic_function(3)?')\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_pFdKcCu5taDTtOOfX14vEDRp', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-25836468-ba7e-43be-a7cf-76bba06a2a08-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_pFdKcCu5taDTtOOfX14vEDRp'}]\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='1a08b883-9c7b-4969-9e9b-67ce64cdcb5f' tool_call_id='call_pFdKcCu5taDTtOOfX14vEDRp'\n",
+      "content='It seems there was an error when trying to apply the magic function. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 34, 'prompt_tokens': 97, 'total_tokens': 131}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-d571b774-0ea3-4e35-8b7d-f32932c3f3cc-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K'}]\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='0b45787b-c82a-487f-9a5a-de129c30460f' tool_call_id='call_DA0lpDIkBFg2GHy4WsEcZG4K'\n",
+      "content='It appears that there is a consistent issue when trying to apply the magic function to the input \"3.\" This could be due to various reasons, such as the input not being in the correct format or an internal error.\\n\\nIf you have any other questions or if there\\'s something else you\\'d like to try, please let me know!' response_metadata={'token_usage': {'completion_tokens': 66, 'prompt_tokens': 153, 'total_tokens': 219}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None} id='run-50a962e6-21b7-4327-8dea-8e2304062627-0'\n"
      ]
     }
    ],
@@ -861,7 +847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "4b8498fc-a7af-4164-a401-d8714f082306",
    "metadata": {},
    "outputs": [
@@ -888,7 +874,7 @@
        " 'output': 'Agent stopped due to max iterations.'}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -931,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "a2b29113-e6be-4f91-aa4c-5c63dea3e423",
    "metadata": {},
    "outputs": [
@@ -939,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_FKiTkTd0Ffd4rkYSzERprf1M', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b842f7b6-ec10-40f8-8c0e-baa220b77e91-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_FKiTkTd0Ffd4rkYSzERprf1M', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_HaQkeCwD5QskzJzFixCBacZ4', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-596c9200-771f-436d-8576-72fcb81620f1-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_HaQkeCwD5QskzJzFixCBacZ4'}])]}}\n",
       "------\n",
       "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
@@ -970,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "id": "e9eb55f4-a321-4bac-b52d-9e43b411cf92",
    "metadata": {},
    "outputs": [
@@ -978,7 +964,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_WoOB8juagB08xrP38twYlYKR', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73dee47e-30ab-42c9-bb0c-6f227cac96cd-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_WoOB8juagB08xrP38twYlYKR', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1c77db7-405f-43d9-8d57-751f2ca1a58c-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv'}])]}}\n",
       "------\n",
       "Task Cancelled.\n"
      ]
@@ -1019,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "id": "3f6e2cf2",
    "metadata": {},
    "outputs": [
@@ -1081,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "id": "73cabbc4",
    "metadata": {},
    "outputs": [
@@ -1089,10 +1075,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='what is the value of magic_function(3)?' id='4fa7fbe5-758c-47a3-9268-717665d10680'\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_ujE0IQBbIQnxcF9gsZXQfdhF', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-65d689aa-baee-4342-a5d2-048feefab418-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ujE0IQBbIQnxcF9gsZXQfdhF', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
-      "content='Sorry there was an error, please try again.' name='magic_function' id='ef8ddf1d-9ad7-4ac0-b784-b673c4d94bbd' tool_call_id='call_ujE0IQBbIQnxcF9gsZXQfdhF'\n",
-      "content='It seems there was an issue with the previous attempt. Let me try that again.' additional_kwargs={'tool_calls': [{'id': 'call_GcsAfCFUHJ50BN2IOWnwTbQ7', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 32, 'prompt_tokens': 87, 'total_tokens': 119}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-54527c4b-8ff0-4ee8-8abf-224886bd222e-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_GcsAfCFUHJ50BN2IOWnwTbQ7', 'type': 'tool_call'}] usage_metadata={'input_tokens': 87, 'output_tokens': 32, 'total_tokens': 119}\n",
+      "('human', 'what is the value of magic_function(3)?')\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_bTURmOn9C8zslmn0kMFeykIn', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-0844a504-7e6b-4ea6-a069-7017e38121ee-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_bTURmOn9C8zslmn0kMFeykIn'}]\n",
+      "content='Sorry there was an error, please try again.' name='magic_function' id='00d5386f-eb23-4628-9a29-d9ce6a7098cc' tool_call_id='call_bTURmOn9C8zslmn0kMFeykIn'\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_JYqvvvWmXow2u012DuPoDHFV', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 96, 'total_tokens': 110}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-b73b1b1c-c829-4348-98cd-60b315c85448-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_JYqvvvWmXow2u012DuPoDHFV'}]\n",
       "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
     }
@@ -1132,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "id": "b94bb169",
    "metadata": {},
    "outputs": [
@@ -1230,12 +1216,12 @@
    "source": [
     "### In LangGraph\n",
     "\n",
-    "We can use the [`state_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
+    "We can use the [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "b309ba9a",
    "metadata": {},
    "outputs": [
@@ -1260,9 +1246,9 @@
     }
    ],
    "source": [
+    "from langchain_core.messages import AnyMessage\n",
     "from langgraph.errors import GraphRecursionError\n",
     "from langgraph.prebuilt import create_react_agent\n",
-    "from langgraph.prebuilt.chat_agent_executor import AgentState\n",
     "\n",
     "magic_step_num = 1\n",
     "\n",
@@ -1279,12 +1265,12 @@
     "tools = [magic_function]\n",
     "\n",
     "\n",
-    "def _modify_state_messages(state: AgentState):\n",
+    "def _modify_messages(messages: list[AnyMessage]):\n",
     "    # Give the agent amnesia, only keeping the original user query\n",
-    "    return [(\"system\", \"You are a helpful assistant\"), state[\"messages\"][0]]\n",
+    "    return [(\"system\", \"You are a helpful assistant\"), messages[0]]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
     "\n",
     "try:\n",
     "    for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
@@ -1322,7 +1308,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,

docs/docs/how_to/pydantic_compatibility.md

@@ -1,97 +1,27 @@
 # How to use LangChain with different Pydantic versions
 
-- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/).
-- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/).
-- Pydantic 1 End of Life was in June 2024. LangChain will be dropping support for Pydantic 1 in the near future,
-and likely migrating internally to Pydantic 2. The timeline is tentatively September. This change will be accompanied by a minor version bump in the main langchain packages to version 0.3.x.
+- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
+- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/)
+- Pydantic v2 and v1 are under the same package name, so both versions cannot be installed at the same time
 
-As of `langchain>=0.0.267`, LangChain allows users to install either Pydantic V1 or V2.
+## LangChain Pydantic migration plan
 
-Internally, LangChain continues to use the [Pydantic V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features) via
-the v1 namespace of Pydantic 2.
+As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2. 
+   * Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
+   * During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
+   migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
 
-Because Pydantic does not support mixing .v1 and .v2 objects, users should be aware of a number of issues
-when using LangChain with Pydantic.
-
-## 1. Passing Pydantic objects to LangChain APIs
-
-Most LangChain APIs that accept Pydantic objects have been updated to accept both Pydantic v1 and v2 objects.
-
-* Pydantic v1 objects correspond to subclasses of `pydantic.BaseModel` if `pydantic 1` is installed or subclasses of `pydantic.v1.BaseModel` if `pydantic 2` is installed.
-* Pydantic v2 objects correspond to subclasses of `pydantic.BaseModel` if `pydantic 2` is installed.
-
-
-| API                                    | Pydantic 1 | Pydantic 2                                                     |
-|----------------------------------------|------------|----------------------------------------------------------------|
-| `BaseChatModel.bind_tools`             | Yes        | langchain-core>=0.2.23, appropriate version of partner package |
-| `BaseChatModel.with_structured_output` | Yes        | langchain-core>=0.2.23, appropriate version of partner package |
-| `Tool.from_function`                   | Yes        | langchain-core>=0.2.23                                         |
-| `StructuredTool.from_function`         | Yes        | langchain-core>=0.2.23                                         |
-
-
-Partner packages that accept pydantic v2 objects via `bind_tools` or `with_structured_output` APIs:
-
-| Package Name        | pydantic v1 | pydantic v2 |
-|---------------------|-------------|-------------|
-| langchain-mistralai | Yes         | >=0.1.11    |
-| langchain-anthropic | Yes         | >=0.1.21    |
-| langchain-robocorp  | Yes         | >=0.0.10    |
-| langchain-openai    | Yes         | >=0.1.19    |
-| langchain-fireworks | Yes         | >=0.1.5     |
-
-Additional partner packages will be updated to accept Pydantic v2 objects in the future.
-
-If you are still seeing issues with these APIs or other APIs that accept Pydantic objects, please open an issue, and we'll
-address it.
-
-Example:
-
-Prior to `langchain-core<0.2.23`, use Pydantic v1 objects when passing to LangChain APIs.
-
-
-```python
-from langchain_openai import ChatOpenAI
-from pydantic.v1 import BaseModel # <-- Note v1 namespace
-
-class Person(BaseModel):
-    """Personal information"""
-    name: str
-    
-model = ChatOpenAI()
-model = model.with_structured_output(Person)
-
-model.invoke('Bob is a person.')
-```
-
-After `langchain-core>=0.2.23`, use either Pydantic v1 or v2 objects when passing to LangChain APIs.
-
-```python
-from langchain_openai import ChatOpenAI
-from pydantic import BaseModel
-
-class Person(BaseModel):
-    """Personal information"""
-    name: str
-    
-    
-model = ChatOpenAI()
-model = model.with_structured_output(Person)
-
-model.invoke('Bob is a person.')
-```
-
-## 2. Sub-classing LangChain models
-
-Because LangChain internally uses Pydantic v1, if you are sub-classing LangChain models, you should use Pydantic v1
-primitives.
+User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
 
+Below are two examples of showing how to avoid mixing pydantic v1 and v2 code in
+the case of inheritance and in the case of passing objects to LangChain.
 
 **Example 1: Extending via inheritance**
 
 **YES** 
 
 ```python
-from pydantic.v1 import validator
+from pydantic.v1 import root_validator, validator
 from langchain_core.tools import BaseTool
 
 class CustomTool(BaseTool): # BaseTool is v1 code
@@ -140,33 +70,38 @@ CustomTool(
 )
 ```
 
+**Example 2: Passing objects to LangChain**
 
-## 3. Disable run-time validation for LangChain objects used inside Pydantic v2 models
-
-e.g.,
+**YES**
 
 ```python
-from typing import Annotated
+from langchain_core.tools import Tool
+from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
 
-from langchain_openai import ChatOpenAI # <-- ChatOpenAI uses pydantic v1
-from pydantic import BaseModel, SkipValidation
+class CalculatorInput(BaseModel):
+    question: str = Field()
 
-
-class Foo(BaseModel): # <-- BaseModel is from Pydantic v2
-    model: Annotated[ChatOpenAI, SkipValidation()]
-
-Foo(model=ChatOpenAI(api_key="hello"))
+Tool.from_function( # <-- tool uses v1 namespace
+    func=lambda question: 'hello',
+    name="Calculator",
+    description="useful for when you need to answer questions about math",
+    args_schema=CalculatorInput
+)
 ```
 
-## 4: LangServe cannot generate OpenAPI docs if running Pydantic 2
+**NO**
 
-If you are using Pydantic 2, you will not be able to generate OpenAPI docs using LangServe.
+```python
+from langchain_core.tools import Tool
+from pydantic import BaseModel, Field # <-- Uses v2 namespace
 
-If you need OpenAPI docs, your options are to either install Pydantic 1:
+class CalculatorInput(BaseModel):
+    question: str = Field()
 
-`pip install pydantic==1.10.17`
-
-or else to use the `APIHandler` object in LangChain to manually create the
-routes for your API.
-
-See: https://python.langchain.com/v0.2/docs/langserve/#pydantic
+Tool.from_function( # <-- tool uses v1 namespace
+    func=lambda question: 'hello',
+    name="Calculator",
+    description="useful for when you need to answer questions about math",
+    args_schema=CalculatorInput
+)
+```

docs/docs/how_to/tool_calling.ipynb

@@ -22,36 +22,57 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "\n",
     "- [Chat models](/docs/concepts/#chat-models)\n",
     "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [Tool calling](/docs/concepts/#functiontool-calling)\n",
     "- [Output parsers](/docs/concepts/#output-parsers)\n",
     "\n",
     ":::\n",
     "\n",
-    "[Tool calling](/docs/concepts/#functiontool-calling) allows a chat model to respond to a given prompt by \"calling a tool\".\n",
+    ":::info Tool calling vs function calling\n",
     "\n",
-    "Remember, while the name \"tool calling\" implies that the model is directly performing some action, this is actually not the case! The model only generates the arguments to a tool, and actually running the tool (or not) is up to the user.\n",
-    "\n",
-    "Tool calling is a general technique that generates structured output from a model, and you can use it even when you don't intend to invoke any tools. An example use-case of that is [extraction from unstructured text](/docs/tutorials/extraction/).\n",
-    "\n",
-    "![Diagram of calling a tool](/img/tool_call.png)\n",
-    "\n",
-    "If you want to see how to use the model-generated tool call to actually run a tool function [check out this guide](/docs/how_to/tool_results_pass_to_model/).\n",
-    "\n",
-    ":::note Supported models\n",
-    "\n",
-    "Tool calling is not universal, but is supported by many popular LLM providers, including [Anthropic](/docs/integrations/chat/anthropic/), \n",
-    "[Cohere](/docs/integrations/chat/cohere/), [Google](/docs/integrations/chat/google_vertex_ai_palm/), \n",
-    "[Mistral](/docs/integrations/chat/mistralai/), [OpenAI](/docs/integrations/chat/openai/), and even for locally-running models via [Ollama](/docs/integrations/chat/ollama/).\n",
-    "\n",
-    "You can find a [list of all models that support tool calling here](/docs/integrations/chat/).\n",
+    "We use the term tool calling interchangeably with function calling. Although\n",
+    "function calling is sometimes meant to refer to invocations of a single function,\n",
+    "we treat all models as though they can return multiple tool or function calls in \n",
+    "each message.\n",
     "\n",
     ":::\n",
     "\n",
-    "LangChain implements standard interfaces for defining tools, passing them to LLMs, and representing tool calls.\n",
-    "This guide will cover how to bind tools to an LLM, then invoke the LLM to generate these arguments."
+    ":::info Supported models\n",
+    "\n",
+    "You can find a [list of all models that support tool calling](/docs/integrations/chat/).\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "Tool calling allows a chat model to respond to a given prompt by \"calling a tool\".\n",
+    "While the name implies that the model is performing \n",
+    "some action, this is actually not the case! The model generates the \n",
+    "arguments to a tool, and actually running the tool (or not) is up to the user.\n",
+    "For example, if you want to [extract output matching some schema](/docs/how_to/structured_output/) \n",
+    "from unstructured text, you could give the model an \"extraction\" tool that takes \n",
+    "parameters matching the desired schema, then treat the generated output as your final \n",
+    "result.\n",
+    "\n",
+    ":::note\n",
+    "\n",
+    "If you only need formatted values, try the [.with_structured_output()](/docs/how_to/structured_output/#the-with_structured_output-method) chat model method as a simpler entrypoint.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "However, tool calling goes beyond [structured output](/docs/how_to/structured_output/)\n",
+    "since you can pass responses from called tools back to the model to create longer interactions.\n",
+    "For instance, given a search engine tool, an LLM might handle a \n",
+    "query by first issuing a call to the search engine with arguments. The system calling the LLM can \n",
+    "receive the tool call, execute it, and return the output to the LLM to inform its \n",
+    "response. LangChain includes a suite of [built-in tools](/docs/integrations/tools/) \n",
+    "and supports several methods for defining your own [custom tools](/docs/how_to/custom_tools). \n",
+    "\n",
+    "Tool calling is not universal, but many popular LLM providers, including [Anthropic](https://www.anthropic.com/), \n",
+    "[Cohere](https://cohere.com/), [Google](https://cloud.google.com/vertex-ai), \n",
+    "[Mistral](https://mistral.ai/), [OpenAI](https://openai.com/), and others, \n",
+    "support variants of a tool calling feature.\n",
+    "\n",
+    "LangChain implements standard interfaces for defining tools, passing them to LLMs, \n",
+    "and representing tool calls. This guide and the other How-to pages in the Tool section will show you how to use tools with LangChain."
    ]
   },
   {
@@ -70,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,14 +112,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "LangChain also implements a `@tool` decorator that allows for further control of the tool schema, such as tool names and argument descriptions. See the how-to guide [here](/docs/how_to/custom_tools/#creating-tools-from-functions) for details.\n",
+    "LangChain also implements a `@tool` decorator that allows for further control of the tool schema, such as tool names and argument descriptions. See the how-to guide [here](/docs/how_to/custom_tools/#creating-tools-from-functions) for detail.\n",
     "\n",
-    "We can also define the schemas without the accompanying functions using [Pydantic](https://docs.pydantic.dev):"
+    "We can also define the schema using [Pydantic](https://docs.pydantic.dev):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,8 +149,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To actually bind those schemas to a chat model, we'll use the `.bind_tools()` method. This handles converting\n",
-    "the `Add` and `Multiply` schemas to the proper format for the model. The tool schema will then be passed it in each time the model is invoked.\n",
+    "We can bind them to chat models as follows:\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -138,7 +158,11 @@
     "  customVarName=\"llm\"\n",
     "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
     "/>\n",
-    "```"
+    "```\n",
+    "\n",
+    "We'll use the `.bind_tools()` method to handle converting\n",
+    "`Multiply` to the proper format for the model, then and bind it (i.e.,\n",
+    "passing it in each time the model is invoked)."
    ]
   },
   {
@@ -159,7 +183,7 @@
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -170,7 +194,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_wLTBasMppAwpdiA5CD92l9x7', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 89, 'total_tokens': 107}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_0f03d4f0ee', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d3f36cca-f225-416f-ac16-0217046f0b38-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_wLTBasMppAwpdiA5CD92l9x7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 89, 'output_tokens': 18, 'total_tokens': 107})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_g4RuAijtDcSeM96jXyCuiLSN', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 95, 'total_tokens': 113}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5157d15a-7e0e-4ab1-af48-3d98010cd152-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_g4RuAijtDcSeM96jXyCuiLSN'}], usage_metadata={'input_tokens': 95, 'output_tokens': 18, 'total_tokens': 113})"
       ]
      },
      "execution_count": 4,
@@ -190,7 +214,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see our LLM generated arguments to a tool! You can look at the docs for [bind_tools()](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools, as well as [this guide on how to force the LLM to call a tool](/docs/how_to/tool_choice/) rather than letting it decide."
+    "As we can see, even though the prompt didn't really suggest a tool call, our LLM made one since it was forced to do so. You can look at the docs for [bind_tools()](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools."
    ]
   },
   {
@@ -222,12 +246,10 @@
       "text/plain": [
        "[{'name': 'Multiply',\n",
        "  'args': {'a': 3, 'b': 12},\n",
-       "  'id': 'call_uqJsNrDJ8ZZnFa1BHHYAllEv',\n",
-       "  'type': 'tool_call'},\n",
+       "  'id': 'call_TnadLbWJu9HwDULRb51RNSMw'},\n",
        " {'name': 'Add',\n",
        "  'args': {'a': 11, 'b': 49},\n",
-       "  'id': 'call_ud1uHAaYsdpWuxugwoJ63BDs',\n",
-       "  'type': 'tool_call'}]"
+       "  'id': 'call_Q9vt1up05sOQScXvUYWzSpCg'}]"
       ]
      },
      "execution_count": 5,
@@ -286,17 +308,17 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Now you've learned how to bind tool schemas to a chat model and have the model call the tool.\n",
-    "\n",
-    "Next, check out this guide on actually using the tool by invoking the function and passing the results back to the model:\n",
+    "Now you've learned how to bind tool schemas to a chat model and to call those tools. Next, you can learn more about how to use tools:\n",
     "\n",
+    "- Few shot promting [with tools](/docs/how_to/tools_few_shot/)\n",
+    "- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
+    "- Bind [model-specific tools](/docs/how_to/tools_model_specific/)\n",
+    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
     "- Pass [tool results back to model](/docs/how_to/tool_results_pass_to_model)\n",
     "\n",
     "You can also check out some more specific uses of tool calling:\n",
     "\n",
-    "- Few shot prompting [with tools](/docs/how_to/tools_few_shot/)\n",
-    "- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
-    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
+    "- Building [tool-using chains and agents](/docs/how_to#tools)\n",
     "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
    ]
   }
@@ -317,7 +339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -9,34 +9,12 @@
     ":::info Prerequisites\n",
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "- [LangChain Tools](/docs/concepts/#tools)\n",
+    "- [Tools](/docs/concepts/#tools)\n",
     "- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
-    "- [Using chat models to call tools](/docs/how_to/tool_calling)\n",
-    "- [Defining custom tools](/docs/how_to/custom_tools/)\n",
     "\n",
     ":::\n",
     "\n",
-    "Some models are capable of [**tool calling**](/docs/concepts/#functiontool-calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
-    "\n",
-    "![Diagram of a tool call invocation](/img/tool_invocation.png)\n",
-    "\n",
-    "![Diagram of a tool call result](/img/tool_results.png)\n",
-    "\n",
-    "First, let's define our tools and our model:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{=mdx}\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs\n",
-    "  customVarName=\"llm\"\n",
-    "  fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
-    "/>\n",
-    "```"
+    "If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s and `ToolCall`s. First, let's define our tools and our model."
    ]
   },
   {
@@ -44,25 +22,6 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "from langchain_core.tools import tool\n",
     "\n",
@@ -79,8 +38,23 @@
     "    return a * b\n",
     "\n",
     "\n",
-    "tools = [add, multiply]\n",
+    "tools = [add, multiply]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
     "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"
    ]
   },
@@ -88,88 +62,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, let's get the model to call a tool. We'll add it to a list of messages that we'll treat as conversation history:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_GPGPE943GORirhIAYnWv00rK', 'type': 'tool_call'}, {'name': 'add', 'args': {'a': 11, 'b': 49}, 'id': 'call_dm8o64ZrY3WFZHAvCh1bEJ6i', 'type': 'tool_call'}]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
+    "The nice thing about Tools is that if we invoke them with a ToolCall, we'll automatically get back a ToolMessage that can be fed back to the model: \n",
     "\n",
-    "query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
+    ":::info Requires ``langchain-core >= 0.2.19``\n",
     "\n",
-    "messages = [HumanMessage(query)]\n",
-    "\n",
-    "ai_msg = llm_with_tools.invoke(messages)\n",
-    "\n",
-    "print(ai_msg.tool_calls)\n",
-    "\n",
-    "messages.append(ai_msg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next let's invoke the tool functions using the args the model populated!\n",
-    "\n",
-    "Conveniently, if we invoke a LangChain `Tool` with a `ToolCall`, we'll automatically get back a `ToolMessage` that can be fed back to the model:\n",
-    "\n",
-    ":::caution Compatibility\n",
-    "\n",
-    "This functionality was added in `langchain-core == 0.2.19`. Please make sure your package is up to date.\n",
-    "\n",
-    "If you are on earlier versions of `langchain-core`, you will need to extract the `args` field from the tool and construct a `ToolMessage` manually.\n",
+    "This functionality was added in ``langchain-core == 0.2.19``. Please make sure your package is up to date.\n",
     "\n",
     ":::"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
-       " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_loT2pliJwJe3p7nkgXYF48A1', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'multiply'}, 'type': 'function'}, {'id': 'call_bG9tYZCXOeYDZf3W46TceoV4', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 87, 'total_tokens': 137}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_661538dc1f', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-e3db3c46-bf9e-478e-abc1-dc9a264f4afe-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_loT2pliJwJe3p7nkgXYF48A1', 'type': 'tool_call'}, {'name': 'add', 'args': {'a': 11, 'b': 49}, 'id': 'call_bG9tYZCXOeYDZf3W46TceoV4', 'type': 'tool_call'}], usage_metadata={'input_tokens': 87, 'output_tokens': 50, 'total_tokens': 137}),\n",
-       " ToolMessage(content='36', name='multiply', tool_call_id='call_loT2pliJwJe3p7nkgXYF48A1'),\n",
-       " ToolMessage(content='60', name='add', tool_call_id='call_bG9tYZCXOeYDZf3W46TceoV4')]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for tool_call in ai_msg.tool_calls:\n",
-    "    selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
-    "    tool_msg = selected_tool.invoke(tool_call)\n",
-    "    messages.append(tool_msg)\n",
-    "\n",
-    "messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And finally, we'll invoke the model with the tool results. The model will use this information to generate a final answer to our original query:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -178,7 +79,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='The result of \\\\(3 \\\\times 12\\\\) is 36, and the result of \\\\(11 + 49\\\\) is 60.', response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 153, 'total_tokens': 184}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_661538dc1f', 'finish_reason': 'stop', 'logprobs': None}, id='run-87d1ef0a-1223-4bb3-9310-7b591789323d-0', usage_metadata={'input_tokens': 153, 'output_tokens': 31, 'total_tokens': 184})"
+       "[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
+       " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Smg3NHJNxrKfAmd4f9GkaYn3', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'multiply'}, 'type': 'function'}, {'id': 'call_55K1C0DmH6U5qh810gW34xZ0', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 49, 'prompt_tokens': 88, 'total_tokens': 137}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-56657feb-96dd-456c-ab8e-1857eab2ade0-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_Smg3NHJNxrKfAmd4f9GkaYn3', 'type': 'tool_call'}, {'name': 'add', 'args': {'a': 11, 'b': 49}, 'id': 'call_55K1C0DmH6U5qh810gW34xZ0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 88, 'output_tokens': 49, 'total_tokens': 137}),\n",
+       " ToolMessage(content='36', name='multiply', tool_call_id='call_Smg3NHJNxrKfAmd4f9GkaYn3'),\n",
+       " ToolMessage(content='60', name='add', tool_call_id='call_55K1C0DmH6U5qh810gW34xZ0')]"
       ]
      },
      "execution_count": 5,
@@ -186,6 +90,37 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "from langchain_core.messages import HumanMessage, ToolMessage\n",
+    "\n",
+    "query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
+    "\n",
+    "messages = [HumanMessage(query)]\n",
+    "ai_msg = llm_with_tools.invoke(messages)\n",
+    "messages.append(ai_msg)\n",
+    "for tool_call in ai_msg.tool_calls:\n",
+    "    selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
+    "    tool_msg = selected_tool.invoke(tool_call)\n",
+    "    messages.append(tool_msg)\n",
+    "messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 153, 'total_tokens': 171}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ba5032f0-f773-406d-a408-8314e66511d0-0', usage_metadata={'input_tokens': 153, 'output_tokens': 18, 'total_tokens': 171})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "llm_with_tools.invoke(messages)"
    ]
@@ -194,25 +129,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that each `ToolMessage` must include a `tool_call_id` that matches an `id` in the original tool calls that the model generates. This helps the model match tool responses with tool calls.\n",
-    "\n",
-    "Tool calling agents, like those in [LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/), use this basic flow to answer queries and solve tasks.\n",
-    "\n",
-    "## Related\n",
-    "\n",
-    "- [LangGraph quickstart](https://langchain-ai.github.io/langgraph/tutorials/introduction/)\n",
-    "- Few shot prompting [with tools](/docs/how_to/tools_few_shot/)\n",
-    "- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
-    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
-    "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
+    "Note that we pass back the same `id` in the `ToolMessage` as the what we receive from the model in order to help the model match tool responses with tool calls."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -224,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/groq.ipynb

@@ -2,259 +2,298 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
    "source": [
     "---\n",
     "sidebar_label: Groq\n",
+    "keywords: [chatgroq]\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
    "metadata": {},
    "source": [
-    "# ChatGroq\n",
+    "# Groq\n",
     "\n",
-    "This will help you getting started with Groq [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models).\n",
+    "LangChain supports integration with [Groq](https://groq.com/) chat models. Groq specializes in fast AI inference.\n",
     "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGroq](https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://api.python.langchain.com/en/latest/groq_api_reference.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/keys) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
+    "To get started, you'll first need to install the langchain-groq package:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\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": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "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;49m24.1.2\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;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
    "source": [
     "%pip install -qU langchain-groq"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
    "metadata": {},
    "source": [
-    "## Instantiation\n",
+    "Request an [API key](https://wow.groq.com) and set it as an environment variable:\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_groq import ChatGroq\n",
+    "```bash\n",
+    "export GROQ_API_KEY=<YOUR API KEY>\n",
+    "```\n",
     "\n",
-    "llm = ChatGroq(\n",
-    "    model=\"mixtral-8x7b-32768\",\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='I enjoy programming. (The French translation is: \"J\\'aime programmer.\")\\n\\nNote: I chose to translate \"I love programming\" as \"J\\'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.', response_metadata={'token_usage': {'completion_tokens': 73, 'prompt_tokens': 31, 'total_tokens': 104, 'completion_time': 0.1140625, 'prompt_time': 0.003352463, 'queue_time': None, 'total_time': 0.117414963}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-64433c19-eadf-42fc-801e-3071e3c40160-0', usage_metadata={'input_tokens': 31, 'output_tokens': 73, 'total_tokens': 104})"
-      ]
-     },
-     "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": [
-      "I enjoy programming. (The French translation is: \"J'aime programmer.\")\n",
-      "\n",
-      "Note: I chose to translate \"I love programming\" as \"J'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
+    "Alternatively, you may configure the API key when you initialize ChatGroq.\n",
     "\n",
-    "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
+    "Here's an example of it in action:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='That\\'s great! I can help you translate English phrases related to programming into German.\\n\\n\"I love programming\" can be translated as \"Ich liebe Programmieren\" in German.\\n\\nHere are some more programming-related phrases translated into German:\\n\\n* \"Programming language\" = \"Programmiersprache\"\\n* \"Code\" = \"Code\"\\n* \"Variable\" = \"Variable\"\\n* \"Function\" = \"Funktion\"\\n* \"Array\" = \"Array\"\\n* \"Object-oriented programming\" = \"Objektorientierte Programmierung\"\\n* \"Algorithm\" = \"Algorithmus\"\\n* \"Data structure\" = \"Datenstruktur\"\\n* \"Debugging\" = \"Fehlersuche\"\\n* \"Compile\" = \"Kompilieren\"\\n* \"Link\" = \"Verknüpfen\"\\n* \"Run\" = \"Ausführen\"\\n* \"Test\" = \"Testen\"\\n* \"Deploy\" = \"Bereitstellen\"\\n* \"Version control\" = \"Versionskontrolle\"\\n* \"Open source\" = \"Open Source\"\\n* \"Software development\" = \"Softwareentwicklung\"\\n* \"Agile methodology\" = \"Agile Methodik\"\\n* \"DevOps\" = \"DevOps\"\\n* \"Cloud computing\" = \"Cloud Computing\"\\n\\nI hope this helps! Let me know if you have any other questions or if you need further translations.', response_metadata={'token_usage': {'completion_tokens': 331, 'prompt_tokens': 25, 'total_tokens': 356, 'completion_time': 0.520006542, 'prompt_time': 0.00250165, 'queue_time': None, 'total_time': 0.522508192}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-74207fb7-85d3-417d-b2b9-621116b75d41-0', usage_metadata={'input_tokens': 25, 'output_tokens': 331, 'total_tokens': 356})"
+       "AIMessage(content=\"Low latency is crucial for Large Language Models (LLMs) because it directly impacts the user experience, model performance, and overall efficiency. Here are some reasons why low latency is essential for LLMs:\\n\\n1. **Real-time Interaction**: LLMs are often used in applications that require real-time interaction, such as chatbots, virtual assistants, and language translation. Low latency ensures that the model responds quickly to user input, providing a seamless and engaging experience.\\n2. **Conversational Flow**: In conversational AI, latency can disrupt the natural flow of conversation. Low latency helps maintain a smooth conversation, allowing users to respond quickly and naturally, without feeling like they're waiting for the model to catch up.\\n3. **Model Performance**: High latency can lead to increased error rates, as the model may struggle to keep up with the input pace. Low latency enables the model to process information more efficiently, resulting in better accuracy and performance.\\n4. **Scalability**: As the number of users and requests increases, low latency becomes even more critical. It allows the model to handle a higher volume of requests without sacrificing performance, making it more scalable and efficient.\\n5. **Resource Utilization**: Low latency can reduce the computational resources required to process requests. By minimizing latency, you can optimize resource allocation, reduce costs, and improve overall system efficiency.\\n6. **User Experience**: High latency can lead to frustration, abandonment, and a poor user experience. Low latency ensures that users receive timely responses, which is essential for building trust and satisfaction.\\n7. **Competitive Advantage**: In applications like customer service or language translation, low latency can be a key differentiator. It can provide a competitive advantage by offering a faster and more responsive experience, setting your application apart from others.\\n8. **Edge Computing**: With the increasing adoption of edge computing, low latency is critical for processing data closer to the user. This reduces latency even further, enabling real-time processing and analysis of data.\\n9. **Real-time Analytics**: Low latency enables real-time analytics and insights, which are essential for applications like sentiment analysis, trend detection, and anomaly detection.\\n10. **Future-Proofing**: As LLMs continue to evolve and become more complex, low latency will become even more critical. By prioritizing low latency now, you'll be better prepared to handle the demands of future LLM applications.\\n\\nIn summary, low latency is vital for LLMs because it ensures a seamless user experience, improves model performance, and enables efficient resource utilization. By prioritizing low latency, you can build more effective, scalable, and efficient LLM applications that meet the demands of real-time interaction and processing.\", response_metadata={'token_usage': {'completion_tokens': 541, 'prompt_tokens': 33, 'total_tokens': 574, 'completion_time': 1.499777658, 'prompt_time': 0.008344704, 'queue_time': None, 'total_time': 1.508122362}, 'model_name': 'llama3-70b-8192', 'system_fingerprint': 'fp_87cbfbbc4d', 'finish_reason': 'stop', 'logprobs': None}, id='run-49dad960-ace8-4cd7-90b3-2db99ecbfa44-0')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_groq import ChatGroq\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",
+    "chat = ChatGroq(\n",
+    "    temperature=0,\n",
+    "    model=\"llama3-70b-8192\",\n",
+    "    # api_key=\"\" # Optional if not set as an environment variable\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",
-    ")"
+    "system = \"You are a helpful assistant.\"\n",
+    "human = \"{text}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke({\"text\": \"Explain the importance of low latency for LLMs.\"})"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
    "metadata": {},
    "source": [
-    "## API reference\n",
+    "You can view the available models [here](https://console.groq.com/docs/models).\n",
     "\n",
-    "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html"
+    "## Tool calling\n",
+    "\n",
+    "Groq chat models support [tool calling](/docs/how_to/tool_calling) to generate output matching a specific schema. The model may choose to call multiple tools or the same tool multiple times if appropriate.\n",
+    "\n",
+    "Here's an example:"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'get_current_weather',\n",
+       "  'args': {'location': 'San Francisco', 'unit': 'Celsius'},\n",
+       "  'id': 'call_pydj'},\n",
+       " {'name': 'get_current_weather',\n",
+       "  'args': {'location': 'Tokyo', 'unit': 'Celsius'},\n",
+       "  'id': 'call_jgq3'}]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Optional\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_current_weather(location: str, unit: Optional[str]):\n",
+    "    \"\"\"Get the current weather in a given location\"\"\"\n",
+    "    return \"Cloudy with a chance of rain.\"\n",
+    "\n",
+    "\n",
+    "tool_model = chat.bind_tools([get_current_weather], tool_choice=\"auto\")\n",
+    "\n",
+    "res = tool_model.invoke(\"What is the weather like in San Francisco and Tokyo?\")\n",
+    "\n",
+    "res.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `.with_structured_output()`\n",
+    "\n",
+    "You can also use the convenience [`.with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method) method to coerce `ChatGroq` into returning a structured output.\n",
+    "Here is an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Joke(setup='Why did the cat join a band?', punchline='Because it wanted to be the purr-cussionist!', rating=None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class Joke(BaseModel):\n",
+    "    \"\"\"Joke to tell user.\"\"\"\n",
+    "\n",
+    "    setup: str = Field(description=\"The setup of the joke\")\n",
+    "    punchline: str = Field(description=\"The punchline to the joke\")\n",
+    "    rating: Optional[int] = Field(description=\"How funny the joke is, from 1 to 10\")\n",
+    "\n",
+    "\n",
+    "structured_llm = chat.with_structured_output(Joke)\n",
+    "\n",
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Behind the scenes, this takes advantage of the above tool calling functionality.\n",
+    "\n",
+    "## Async"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Here is a limerick about the sun:\\n\\nThere once was a sun in the sky,\\nWhose warmth and light caught the eye,\\nIt shone bright and bold,\\nWith a fiery gold,\\nAnd brought life to all, as it flew by.', response_metadata={'token_usage': {'completion_tokens': 51, 'prompt_tokens': 18, 'total_tokens': 69, 'completion_time': 0.144614022, 'prompt_time': 0.00585394, 'queue_time': None, 'total_time': 0.150467962}, 'model_name': 'llama3-70b-8192', 'system_fingerprint': 'fp_2f30b0b571', 'finish_reason': 'stop', 'logprobs': None}, id='run-e42340ba-f0ad-4b54-af61-8308d8ec8256-0')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"human\", \"Write a Limerick about {topic}\")])\n",
+    "chain = prompt | chat\n",
+    "await chain.ainvoke({\"topic\": \"The Sun\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Silvery glow bright\n",
+      "Luna's gentle light shines down\n",
+      "Midnight's gentle queen"
+     ]
+    }
+   ],
+   "source": [
+    "chat = ChatGroq(temperature=0, model=\"llama3-70b-8192\")\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"human\", \"Write a haiku about {topic}\")])\n",
+    "chain = prompt | chat\n",
+    "for chunk in chain.stream({\"topic\": \"The Moon\"}):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Passing custom parameters\n",
+    "\n",
+    "You can pass other Groq-specific parameters using the `model_kwargs` argument on initialization. Here's an example of enabling JSON mode:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='{ \"response\": \"That\\'s a tough question! There are eight species of bears found in the world, and each one is unique and amazing in its own way. However, if I had to pick one, I\\'d say the giant panda is a popular favorite among many people. Who can resist those adorable black and white markings?\", \"followup_question\": \"Would you like to know more about the giant panda\\'s habitat and diet?\" }', response_metadata={'token_usage': {'completion_tokens': 89, 'prompt_tokens': 50, 'total_tokens': 139, 'completion_time': 0.249032839, 'prompt_time': 0.011134497, 'queue_time': None, 'total_time': 0.260167336}, 'model_name': 'llama3-70b-8192', 'system_fingerprint': 'fp_2f30b0b571', 'finish_reason': 'stop', 'logprobs': None}, id='run-558ce67e-8c63-43fe-a48f-6ecf181bc922-0')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = ChatGroq(\n",
+    "    model=\"llama3-70b-8192\", model_kwargs={\"response_format\": {\"type\": \"json_object\"}}\n",
+    ")\n",
+    "\n",
+    "system = \"\"\"\n",
+    "You are a helpful assistant.\n",
+    "Always respond with a JSON object with two string keys: \"response\" and \"followup_question\".\n",
+    "\"\"\"\n",
+    "human = \"{question}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "\n",
+    "chain.invoke({\"question\": \"what bear is best?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -268,9 +307,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 5
+ "nbformat_minor": 2
 }

docs/docs/integrations/chat/oci_generative_ai.ipynb

@@ -33,7 +33,7 @@
     "### 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\n",
     "\n",

docs/docs/integrations/chat/ollama_functions.ipynb

@@ -284,9 +284,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "For more on binding tools and tool call outputs, head to the [tool calling](../../how_to/function_calling.ipynb) docs."
-   ]
+   "source": "For more on binding tools and tool call outputs, head to the [tool calling](docs/how_to/function_calling) docs."
   },
   {
    "cell_type": "markdown",

docs/docs/integrations/chat/together.ipynb

@@ -1,263 +1,103 @@
 {
  "cells": [
   {
-   "cell_type": "raw",
-   "id": "afaf8039",
+   "cell_type": "markdown",
+   "id": "2970dd75-8ebf-4b51-8282-9b454b8f356d",
    "metadata": {},
    "source": [
-    "---\n",
-    "sidebar_label: Together\n",
-    "---"
+    "# Together AI\n",
+    "\n",
+    "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/inference-models) in a couple lines of code.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with Together AI models."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
+   "id": "1c47fc36",
    "metadata": {},
    "source": [
-    "# ChatTogether\n",
-    "\n",
-    "\n",
-    "This page will help you get started with Together AI [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/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/v0.2/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatTogether](https://api.python.langchain.com/en/latest/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://api.python.langchain.com/en/latest/together_api_reference.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:"
+    "## Installation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "execution_count": null,
+   "id": "1ecdb29d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
+    "%pip install --upgrade langchain-together"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "id": "89883202",
    "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:"
+    "## Environment\n",
+    "\n",
+    "To use Together AI, you'll need an API key which you can find here:\n",
+    "https://api.together.ai/settings/api-keys. This can be passed in as an init param\n",
+    "``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8304b4d9",
+   "metadata": {},
+   "source": [
+    "## Example"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "execution_count": null,
+   "id": "637bb53f",
    "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",
+    "# Querying chat models with Together AI\n",
     "\n",
-    "The LangChain Together integration lives in the `langchain-together` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "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;49m24.1.2\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;49mpip 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": "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": 5,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "from langchain_together import ChatTogether\n",
     "\n",
-    "llm = ChatTogether(\n",
+    "# choose from our 50+ models here: https://docs.together.ai/docs/inference-models\n",
+    "chat = ChatTogether(\n",
+    "    # together_api_key=\"YOUR_API_KEY\",\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": 6,
-   "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-79efa49b-dbaf-4ef8-9dce-958533823ef6-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
-      ]
-     },
-     "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": "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": 8,
-   "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-80bba5fa-1723-4242-8d5a-c09b76b8350b-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
-      ]
-     },
-     "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",
-    ")"
+    "# stream the response back from the model\n",
+    "for m in chat.stream(\"Tell me fun things to do in NYC\"):\n",
+    "    print(m.content, end=\"\", flush=True)\n",
+    "\n",
+    "# if you don't want to do streaming, you can use the invoke method\n",
+    "# chat.invoke(\"Tell me fun things to do in NYC\")"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b7170d-d7c5-4890-9714-a37238343805",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## API reference\n",
+    "# Querying code and language models with Together AI\n",
     "\n",
-    "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_together.chat_models.ChatTogether.html"
+    "from langchain_together import Together\n",
+    "\n",
+    "llm = Together(\n",
+    "    model=\"codellama/CodeLlama-70b-Python-hf\",\n",
+    "    # together_api_key=\"...\"\n",
+    ")\n",
+    "\n",
+    "print(llm.invoke(\"def bubble_sort(): \"))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -271,7 +111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/yi.ipynb

@@ -1,228 +0,0 @@
-{
- "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://api.python.langchain.com/en/latest/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://api.python.langchain.com/en/latest/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.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",
-    "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": [
-    {
-     "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://api.python.langchain.com/en/latest/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
-}

docs/docs/integrations/llms/yi.ipynb

@@ -1,133 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Yi\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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Installing the langchain packages needed to use the integration\n",
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisite\n",
-    "An API key is required to access Yi LLM API. Visit https://www.lingyiwanwu.com/ to get your API key. When applying for the API key, you need to specify whether it's for domestic (China) or international use."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use Yi LLM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"YI_API_KEY\"] = \"YOUR_API_KEY\"\n",
-    "\n",
-    "from langchain_community.llms import YiLLM\n",
-    "\n",
-    "# Load the model\n",
-    "llm = YiLLM(model=\"yi-large\")\n",
-    "\n",
-    "# You can specify the region if needed (default is \"auto\")\n",
-    "# llm = YiLLM(model=\"yi-large\", region=\"domestic\")  # or \"international\"\n",
-    "\n",
-    "# Basic usage\n",
-    "res = llm.invoke(\"What's your name?\")\n",
-    "print(res)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate method\n",
-    "res = llm.generate(\n",
-    "    prompts=[\n",
-    "        \"Explain the concept of large language models.\",\n",
-    "        \"What are the potential applications of AI in healthcare?\",\n",
-    "    ]\n",
-    ")\n",
-    "print(res)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Streaming\n",
-    "for chunk in llm.stream(\"Describe the key features of the Yi language model series.\"):\n",
-    "    print(chunk, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Asynchronous streaming\n",
-    "import asyncio\n",
-    "\n",
-    "\n",
-    "async def run_aio_stream():\n",
-    "    async for chunk in llm.astream(\n",
-    "        \"Write a brief on the future of AI according to Dr. Kai-Fu Lee's vision.\"\n",
-    "    ):\n",
-    "        print(chunk, end=\"\", flush=True)\n",
-    "\n",
-    "\n",
-    "asyncio.run(run_aio_stream())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Adjusting parameters\n",
-    "llm_with_params = YiLLM(\n",
-    "    model=\"yi-large\",\n",
-    "    temperature=0.7,\n",
-    "    top_p=0.9,\n",
-    ")\n",
-    "\n",
-    "res = llm_with_params(\n",
-    "    \"Propose an innovative AI application that could benefit society.\"\n",
-    ")\n",
-    "print(res)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/memory/tidb_chat_message_history.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# TiDB\n",
     "\n",
-    "> [TiDB Cloud](https://www.pingcap.com/tidb-serverless/), is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature at https://pingcap.com/ai.\n",
+    "> [TiDB Cloud](https://pingcap.com/ai), is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature today!\n",
     "\n",
     "This notebook introduces how to use TiDB to store chat message history. "
    ]

docs/docs/integrations/platforms/aws.mdx

@@ -197,24 +197,6 @@ See a [usage example](/docs/integrations/vectorstores/documentdb).
 ```python
 from langchain.vectorstores import DocumentDBVectorSearch
 ```
-### Amazon MemoryDB 
-[Amazon MemoryDB](https://aws.amazon.com/memorydb/) is a durable, in-memory database service that delivers ultra-fast performance. MemoryDB is compatible with Redis OSS, a popular open source data store, 
-enabling you to quickly build applications using the same flexible and friendly Redis OSS APIs, and commands that they already use today. 
-
-InMemoryVectorStore class provides a vectorstore to connect with Amazon MemoryDB.
-
-```python
-from langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore
-
-vds = InMemoryVectorStore.from_documents(
-            chunks,
-            embeddings,
-            redis_url="rediss://cluster_endpoint:6379/ssl=True ssl_cert_reqs=none",
-            vector_schema=vector_schema,
-            index_name=INDEX_NAME,
-        )
-```
-See a [usage example](/docs/integrations/vectorstores/memorydb).
 
 ## Retrievers
 

docs/docs/integrations/platforms/google.mdx

@@ -140,18 +140,6 @@ See a [usage example](/docs/integrations/text_embedding/google_vertex_ai_palm).
 from langchain_google_vertexai import VertexAIEmbeddings
 ```
 
-### Palm Embedding
-
-We need to install `langchain-community` python package.
-
-```bash
-pip install langchain-community
-```
-
-```python
-from langchain_community.embeddings.google_palm import GooglePalmEmbeddings
-```
-
 ## Document Loaders
 
 ### AlloyDB for PostgreSQL

docs/docs/integrations/platforms/index.mdx

@@ -0,0 +1,55 @@
+---
+sidebar_position: 0
+sidebar_class_name: hidden
+---
+
+# Providers
+
+:::info
+
+If you'd like to write your own integration, see [Extending LangChain](/docs/how_to/#custom).
+If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
+
+:::
+
+LangChain integrates with many providers.
+
+## Partner Packages
+
+These providers have standalone `langchain-{provider}` packages for improved versioning, dependency management and testing.
+
+- [AI21](/docs/integrations/providers/ai21)
+- [Airbyte](/docs/integrations/providers/airbyte)
+- [Amazon Web Services](/docs/integrations/platforms/aws)
+- [Anthropic](/docs/integrations/platforms/anthropic)
+- [Astra DB](/docs/integrations/providers/astradb)
+- [Cohere](/docs/integrations/providers/cohere)
+- [Couchbase](/docs/integrations/providers/couchbase)
+- [Elasticsearch](/docs/integrations/providers/elasticsearch)
+- [Exa Search](/docs/integrations/providers/exa_search)
+- [Fireworks](/docs/integrations/providers/fireworks)
+- [Google](/docs/integrations/platforms/google)
+- [Groq](/docs/integrations/providers/groq)
+- [IBM](/docs/integrations/providers/ibm)
+- [MistralAI](/docs/integrations/providers/mistralai)
+- [MongoDB](/docs/integrations/providers/mongodb_atlas)
+- [Nomic](/docs/integrations/providers/nomic)
+- [Nvidia](/docs/integrations/providers/nvidia)
+- [OpenAI](/docs/integrations/platforms/openai)
+- [Pinecone](/docs/integrations/providers/pinecone)
+- [Qdrant](/docs/integrations/providers/qdrant)
+- [Robocorp](/docs/integrations/providers/robocorp)
+- [Together AI](/docs/integrations/providers/together)
+- [Unstructured](/docs/integrations/providers/unstructured)
+- [Upstage](/docs/integrations/providers/upstage)
+- [Voyage AI](/docs/integrations/providers/voyageai)
+
+
+## Featured Community Providers
+
+- [Hugging Face](/docs/integrations/platforms/huggingface)
+- [Microsoft](/docs/integrations/platforms/microsoft)
+
+## All Providers
+
+Click [here](/docs/integrations/providers/) to see all providers.

docs/docs/integrations/platforms/microsoft.mdx

@@ -156,20 +156,6 @@ See a [usage example](/docs/integrations/document_loaders/microsoft_onedrive).
 from langchain_community.document_loaders import OneDriveLoader
 ```
 
-### Microsoft OneDrive File
-
->[Microsoft OneDrive](https://en.wikipedia.org/wiki/OneDrive) (formerly `SkyDrive`) is a file-hosting service operated by Microsoft.
-
-First, you need to install a python package.
-
-```bash
-pip install o365
-```
-
-```python
-from langchain_community.document_loaders import OneDriveFileLoader
-```
-
 
 ### Microsoft Word
 
@@ -352,7 +338,7 @@ Follow the documentation [here](/docs/integrations/tools/bing_search) to get a d
 
 The environment variable `BING_SUBSCRIPTION_KEY` and `BING_SEARCH_URL` are required from Bing Search resource.
 
-```python
+```bash
 from langchain_community.tools.bing_search import BingSearchResults
 from langchain_community.utilities import BingSearchAPIWrapper
 

docs/docs/integrations/providers/dspy.ipynb

@@ -355,7 +355,7 @@
    "id": "859daaee-ac5d-47f8-8704-827f5578bf1b",
    "metadata": {},
    "source": [
-    "## Define a metric\n",
+    "## Define a metic\n",
     "\n",
     "We now need to define a metric. This will be used to determine which runs were successful and we can learn from. Here we will use DSPy's metrics, though you can write your own."
    ]

docs/docs/integrations/providers/robocorp.mdx

@@ -11,8 +11,7 @@ You need to install `langchain-robocorp` python package:
 pip install langchain-robocorp
 ```
 
-You will need a running instance of `Action Server` to communicate with from your agent application. 
-See the [Robocorp Quickstart](https://github.com/robocorp/robocorp#quickstart) on how to setup Action Server and create your Actions.
+You will need a running instance of Action Server  to communicate with from your agent application. See the [Robocorp Quickstart](https://github.com/robocorp/robocorp#quickstart) on how to setup Action Server and create your Actions.
 
 You can bootstrap a new project using Action Server `new` command.
 
@@ -22,12 +21,6 @@ cd ./your-project-name
 action-server start
 ```
 
-## Tool
-
-```python
-from langchain_robocorp.toolkits import ActionServerRequestTool
-```
-
 ## Toolkit
 
 See a [usage example](/docs/integrations/toolkits/robocorp).

docs/docs/integrations/providers/sap.mdx

@@ -1,25 +0,0 @@
-# SAP
-
->[SAP SE(Wikipedia)](https://www.sap.com/about/company.html) is a German multinational 
-> software company. It develops enterprise software to manage business operation and 
-> customer relations. The company is the world's leading 
-> `enterprise resource planning (ERP)` software vendor.
-
-## Installation and Setup
-
-We need to install the `hdbcli` python package.
-
-```bash
-pip install hdbcli
-```
-
-## Vectorstore
-
->[SAP HANA Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is 
-> a vector store fully integrated into the `SAP HANA Cloud` database.
-
-See a [usage example](/docs/integrations/vectorstores/sap_hanavector).
-
-```python
-from langchain_community.vectorstores.hanavector import HanaDB
-```

docs/docs/integrations/providers/tidb.mdx

@@ -1,10 +1,10 @@
 # TiDB
 
-> [TiDB Cloud](https://www.pingcap.com/tidb-serverless), is a comprehensive Database-as-a-Service (DBaaS) solution,
+> [TiDB Cloud](https://pingcap.com/ai), is a comprehensive Database-as-a-Service (DBaaS) solution,
 > that provides dedicated and serverless options. `TiDB Serverless` is now integrating 
 > a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly 
 > develop AI applications using `TiDB Serverless` without the need for a new database or additional 
-> technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature at https://pingcap.com/ai.
+> technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature today.
 
 
 ## Installation and Setup

docs/docs/integrations/providers/yi.mdx

@@ -1,23 +0,0 @@
-# 01.AI
-
->[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.
-
-## Installation and Setup
-
-Register and get an API key from either the China site [here](https://platform.lingyiwanwu.com/apikeys) or the global site [here](https://platform.01.ai/apikeys).
-
-## LLMs
-
-See a [usage example](/docs/integrations/llms/yi).
-
-```python
-from langchain_community.llms import YiLLM
-```
-
-## Chat models
-
-See a [usage example](/docs/integrations/chat/yi).
-
-```python
-from langchain_community.chat_models import ChatYi
-```

docs/docs/integrations/retrievers/self_query/hanavector_self_query.ipynb

@@ -1,246 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SAP HANA Cloud Vector Engine\n",
-    "\n",
-    "For more information on how to setup the SAP HANA vetor store, take a look at the [documentation](/docs/integrations/vectorstores/sap_hanavector.ipynb).\n",
-    "\n",
-    "We use the same setup here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "# Use OPENAI_API_KEY env variable\n",
-    "# os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\"\n",
-    "from hdbcli import dbapi\n",
-    "\n",
-    "# Use connection settings from the environment\n",
-    "connection = dbapi.connect(\n",
-    "    address=os.environ.get(\"HANA_DB_ADDRESS\"),\n",
-    "    port=os.environ.get(\"HANA_DB_PORT\"),\n",
-    "    user=os.environ.get(\"HANA_DB_USER\"),\n",
-    "    password=os.environ.get(\"HANA_DB_PASSWORD\"),\n",
-    "    autocommit=True,\n",
-    "    sslValidateCertificate=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To be able to self query with good performance we create additional metadata fields\n",
-    "for our vectorstore table in HANA:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create custom table with attribute\n",
-    "cur = connection.cursor()\n",
-    "cur.execute(\"DROP TABLE LANGCHAIN_DEMO_SELF_QUERY\", ignoreErrors=True)\n",
-    "cur.execute(\n",
-    "    (\n",
-    "        \"\"\"CREATE TABLE \"LANGCHAIN_DEMO_SELF_QUERY\"  (\n",
-    "        \"name\" NVARCHAR(100), \"is_active\" BOOLEAN, \"id\" INTEGER, \"height\" DOUBLE,\n",
-    "        \"VEC_TEXT\" NCLOB, \n",
-    "        \"VEC_META\" NCLOB, \n",
-    "        \"VEC_VECTOR\" REAL_VECTOR\n",
-    "        )\"\"\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's add some documents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.vectorstores.hanavector import HanaDB\n",
-    "from langchain_core.documents import Document\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "\n",
-    "# Prepare some test documents\n",
-    "docs = [\n",
-    "    Document(\n",
-    "        page_content=\"First\",\n",
-    "        metadata={\"name\": \"adam\", \"is_active\": True, \"id\": 1, \"height\": 10.0},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Second\",\n",
-    "        metadata={\"name\": \"bob\", \"is_active\": False, \"id\": 2, \"height\": 5.7},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Third\",\n",
-    "        metadata={\"name\": \"jane\", \"is_active\": True, \"id\": 3, \"height\": 2.4},\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "db = HanaDB(\n",
-    "    connection=connection,\n",
-    "    embedding=embeddings,\n",
-    "    table_name=\"LANGCHAIN_DEMO_SELF_QUERY\",\n",
-    "    specific_metadata_columns=[\"name\", \"is_active\", \"id\", \"height\"],\n",
-    ")\n",
-    "\n",
-    "# Delete already existing documents from the table\n",
-    "db.delete(filter={})\n",
-    "db.add_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Self querying\n",
-    "\n",
-    "Now for the main act: here is how to construct a SelfQueryRetriever for HANA vectorstore:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.query_constructor.base import AttributeInfo\n",
-    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain_community.query_constructors.hanavector import HanaTranslator\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
-    "\n",
-    "metadata_field_info = [\n",
-    "    AttributeInfo(\n",
-    "        name=\"name\",\n",
-    "        description=\"The name of the person\",\n",
-    "        type=\"string\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"is_active\",\n",
-    "        description=\"Whether the person is active\",\n",
-    "        type=\"boolean\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"id\",\n",
-    "        description=\"The ID of the person\",\n",
-    "        type=\"integer\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"height\",\n",
-    "        description=\"The height of the person\",\n",
-    "        type=\"float\",\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "document_content_description = \"A collection of persons\"\n",
-    "\n",
-    "hana_translator = HanaTranslator()\n",
-    "\n",
-    "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm,\n",
-    "    db,\n",
-    "    document_content_description,\n",
-    "    metadata_field_info,\n",
-    "    structured_query_translator=hana_translator,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use this retriever to prepare a (self) query for a person:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_prompt = \"Which person is not active?\"\n",
-    "\n",
-    "docs = retriever.invoke(input=query_prompt)\n",
-    "for doc in docs:\n",
-    "    print(\"-\" * 80)\n",
-    "    print(doc.page_content, \" \", doc.metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also take a look at how the query is being constructed:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.query_constructor.base import (\n",
-    "    StructuredQueryOutputParser,\n",
-    "    get_query_constructor_prompt,\n",
-    ")\n",
-    "\n",
-    "prompt = get_query_constructor_prompt(\n",
-    "    document_content_description,\n",
-    "    metadata_field_info,\n",
-    ")\n",
-    "output_parser = StructuredQueryOutputParser.from_components()\n",
-    "query_constructor = prompt | llm | output_parser\n",
-    "\n",
-    "sq = query_constructor.invoke(input=query_prompt)\n",
-    "\n",
-    "print(\"Structured query: \", sq)\n",
-    "\n",
-    "print(\"Translated for hana vector store: \", hana_translator.visit_structured_query(sq))"
-   ]
-  }
- ],
- "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": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/stores/in_memory.ipynb

@@ -2,14 +2,12 @@
  "cells": [
   {
    "cell_type": "raw",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
+   "metadata": {},
    "source": [
     "---\n",
-    "sidebar_label: InMemoryByteStore\n",
+    "sidebar_label: In Memory\n",
+    "sidebar_position: 2\n",
+    "keywords: [InMemoryStore]\n",
     "---"
    ]
   },
@@ -19,26 +17,29 @@
    "source": [
     "# InMemoryByteStore\n",
     "\n",
-    "This guide will help you get started with in-memory [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `InMemoryByteStore` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.InMemoryByteStore.html).\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "The `InMemoryByteStore` is a non-persistent implementation of a `ByteStore` that stores everything in a Python dictionary. It's intended for demos and cases where you don't need persistence past the lifetime of the Python process.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/in_memory/) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [InMemoryByteStore](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.InMemoryByteStore.html) | [langchain_core](https://api.python.langchain.com/en/latest/core_api_reference.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_core?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_core?style=flat-square&label=%20) |"
+    "The `InMemoryByteStore` is a non-persistent implementation of `ByteStore` that stores everything in a Python dictionary."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[b'v1', b'v2']\n"
+     ]
+    }
+   ],
    "source": [
-    "### Installation\n",
+    "from langchain.storage import InMemoryByteStore\n",
     "\n",
-    "The LangChain `InMemoryByteStore` integration lives in the `langchain_core` package:"
+    "store = InMemoryByteStore()\n",
+    "\n",
+    "store.mset([(\"k1\", b\"v1\"), (\"k2\", b\"v2\")])\n",
+    "print(store.mget([\"k1\", \"k2\"]))"
    ]
   },
   {
@@ -46,123 +47,12 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -qU langchain_core"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now you can instantiate your byte store:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.stores import InMemoryByteStore\n",
-    "\n",
-    "kv_store = InMemoryByteStore()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "You can set data under keys like this using the `mset` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[b'value1', b'value2']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kv_store.mset(\n",
-    "    [\n",
-    "        [\"key1\", b\"value1\"],\n",
-    "        [\"key2\", b\"value2\"],\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can delete data using the `mdelete` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, None]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kv_store.mdelete(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `InMemoryByteStore` features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/langchain_core.stores.InMemoryByteStore.html"
-   ]
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -176,7 +66,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/stores/index.mdx

@@ -3,10 +3,27 @@ sidebar_position: 1
 sidebar_class_name: hidden
 ---
 
-# Key-value stores
+# Stores
 
-[Key-value stores](/docs/concepts/#key-value-stores) are used by other LangChain components to store and retrieve data.
+In many different applications, having some sort of key-value storage is helpful. 
+In this section, we will look at a few different ways to store key-value pairs
+using implementations of the `ByteStore` interface.
 
-import DocCardList from "@theme/DocCardList";
+## Features (natively supported)
 
-<DocCardList />
+All `ByteStore`s support the following functions, which are used for modifying
+**m**ultiple key-value pairs at once:
+
+- `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
+- `mset(key_value_pairs: Sequence[Tuple[str, bytes]]) -> None`: set the contents of multiple keys
+- `mdelete(key: Sequence[str]) -> None`: delete multiple keys
+- `yield_keys(prefix: Optional[str] = None) -> Iterator[str]`: yield all keys in the store, optionally filtering by a prefix
+
+## How to pick one
+
+`ByteStore`s are designed to be interchangeable. By default, most dependent integrations
+use the `InMemoryByteStore`, which is a simple in-memory key-value store.
+
+However, if you start having other requirements, like massive scalability or persistence,
+you can swap out the `ByteStore` implementation with one of the other ones documented
+in this section.

docs/docs/integrations/text_embedding/fastembed.ipynb

@@ -73,25 +73,16 @@
     "- `max_length: int` (default: 512)\n",
     "    > The maximum number of tokens. Unknown behavior for values > 512.\n",
     "\n",
-    "- `cache_dir: Optional[str]` (default: None)\n",
+    "- `cache_dir: Optional[str]`\n",
     "    > The path to the cache directory. Defaults to `local_cache` in the parent directory.\n",
     "\n",
-    "- `threads: Optional[int]` (default: None)\n",
-    "    > The number of threads a single onnxruntime session can use.\n",
+    "- `threads: Optional[int]`\n",
+    "    > The number of threads a single onnxruntime session can use. Defaults to None.\n",
     "\n",
     "- `doc_embed_type: Literal[\"default\", \"passage\"]` (default: \"default\")\n",
     "    > \"default\": Uses FastEmbed's default embedding method.\n",
     "    \n",
-    "    > \"passage\": Prefixes the text with \"passage\" before embedding.\n",
-    "\n",
-    "- `batch_size: int` (default: 256)\n",
-    "    > Batch size for encoding. Higher values will use more memory, but be faster.\n",
-    "\n",
-    "- `parallel: Optional[int]` (default: None)\n",
-    "\n",
-    "    > If `>1`, data-parallel encoding will be used, recommended for offline encoding of large datasets.\n",
-    "    > If `0`, use all available cores.\n",
-    "    > If `None`, don't use data-parallel processing, use default onnxruntime threading instead."
+    "    > \"passage\": Prefixes the text with \"passage\" before embedding."
    ]
   },
   {

docs/docs/integrations/toolkits/index.mdx

@@ -1,21 +0,0 @@
----
-sidebar_position: 0
-sidebar_class_name: hidden
----
-
-# Toolkits
-
-**Toolkits** are collections of tools that are designed to be used together for specific tasks. They include conveniences for loading tools
-that share common authentication, services, or other objects. They can be implemented by subclassing the
-[BaseToolkit](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseToolkit.html#langchain_core.tools.BaseToolkit) class.
-
-This table lists common toolkits.
-
-
-| Namespace 🔻 | Class |
-|------------|---------|
-| langchain_community.agent_toolkits.github | [GitHubToolkit](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html) |
-| langchain_community.agent_toolkits.gmail | [GmailToolkit](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.gmail.toolkit.GmailToolkit.html) |
-| langchain_community.agent_toolkits.openapi | [RequestsToolkit](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html) |
-| langchain_community.agent_toolkits.slack | [SlackToolkit](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html) |
-| langchain_community.agent_toolkits.sql | [SQLDatabaseToolkit](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html) |

docs/docs/integrations/toolkits/robocorp.ipynb

@@ -44,7 +44,7 @@
     "\n",
     "Let's add a dummy function to `action.py`.\n",
     "\n",
-    "```python\n",
+    "```\n",
     "@action\n",
     "def get_weather_forecast(city: str, days: int, scale: str = \"celsius\") -> str:\n",
     "    \"\"\"\n",
@@ -63,7 +63,7 @@
     "\n",
     "We then start the server:\n",
     "\n",
-    "```bash\n",
+    "```\n",
     "action-server start\n",
     "```\n",
     "\n",
@@ -193,7 +193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/chatgpt_plugins.ipynb

@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "93b35dd0",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_class_name: hidden\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3f34700b",
@@ -17,14 +7,6 @@
    "source": [
     "# ChatGPT Plugins\n",
     "\n",
-    "```{=mdx}\n",
-    ":::warning Deprecated\n",
-    "\n",
-    "OpenAI has [deprecated plugins](https://openai.com/index/chatgpt-plugins/).\n",
-    "\n",
-    ":::\n",
-    "```\n",
-    "\n",
     "This example shows how to use ChatGPT Plugins within LangChain abstractions.\n",
     "\n",
     "Note 1: This currently only works for plugins with no auth.\n",

docs/docs/integrations/vectorstores/memorydb.ipynb

@@ -1,537 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Amazon MemoryDB\n",
-    "\n",
-    ">[Vector Search](https://docs.aws.amazon.com/memorydb/latest/devguide/vector-search.html/) introduction and langchain integration guide.\n",
-    "\n",
-    "## What is Amazon MemoryDB?\n",
-    "\n",
-    "MemoryDB is compatible with Redis OSS, a popular open source data store, enabling you to quickly build applications using the same flexible and friendly Redis OSS data structures, APIs, and commands that they already use today. With MemoryDB, all of your data is stored in memory, which enables you to achieve microsecond read and single-digit millisecond write latency and high throughput. MemoryDB also stores data durably across multiple Availability Zones (AZs) using a Multi-AZ transactional log to enable fast failover, database recovery, and node restarts.\n",
-    "\n",
-    "\n",
-    "## Vector search for MemoryDB \n",
-    "\n",
-    "Vector search for MemoryDB extends the functionality of MemoryDB. Vector search can be used in conjunction with existing MemoryDB functionality. Applications that do not use vector search are unaffected by its presence. Vector search is available in all Regions that MemoryDB is available. You can use your existing MemoryDB data or Redis OSS API to build machine learning and generative AI use cases, such as retrieval-augmented generation, anomaly detection, document retrieval, and real-time recommendations.\n",
-    "\n",
-    "* Indexing of multiple fields in Redis hashes and `JSON`\n",
-    "* Vector similarity search (with `HNSW` (ANN) or `FLAT` (KNN))\n",
-    "* Vector Range Search (e.g. find all vectors within a radius of a query vector)\n",
-    "* Incremental indexing without performance loss\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up\n",
-    "\n",
-    "\n",
-    "### Install Redis Python client\n",
-    "\n",
-    "`Redis-py` is a python  client that can be used to connect to MemoryDB"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  redis langchain-aws"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_aws.embeddings import BedrockEmbeddings\n",
-    "\n",
-    "embeddings = BedrockEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MemoryDB Connection\n",
-    "\n",
-    "Valid Redis Url schemas are:\n",
-    "1. `redis://`  - Connection to Redis cluster, unencrypted\n",
-    "2. `rediss://` - Connection to Redis cluster, with TLS encryption\n",
-    "\n",
-    "More information about additional connection parameters can be found in the [redis-py documentation](https://redis-py.readthedocs.io/en/stable/connections.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sample data\n",
-    "\n",
-    "First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata = [\n",
-    "    {\n",
-    "        \"user\": \"john\",\n",
-    "        \"age\": 18,\n",
-    "        \"job\": \"engineer\",\n",
-    "        \"credit_score\": \"high\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"user\": \"derrick\",\n",
-    "        \"age\": 45,\n",
-    "        \"job\": \"doctor\",\n",
-    "        \"credit_score\": \"low\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"user\": \"nancy\",\n",
-    "        \"age\": 94,\n",
-    "        \"job\": \"doctor\",\n",
-    "        \"credit_score\": \"high\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"user\": \"tyler\",\n",
-    "        \"age\": 100,\n",
-    "        \"job\": \"engineer\",\n",
-    "        \"credit_score\": \"high\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"user\": \"joe\",\n",
-    "        \"age\": 35,\n",
-    "        \"job\": \"dentist\",\n",
-    "        \"credit_score\": \"medium\",\n",
-    "    },\n",
-    "]\n",
-    "texts = [\"foo\", \"foo\", \"foo\", \"bar\", \"bar\"]\n",
-    "index_name = \"users\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create MemoryDB vector store\n",
-    "\n",
-    "The InMemoryVectorStore instance can be initialized using the below methods \n",
-    "- ``InMemoryVectorStore.__init__`` - Initialize directly\n",
-    "- ``InMemoryVectorStore.from_documents`` - Initialize from a list of ``Langchain.docstore.Document`` objects\n",
-    "- ``InMemoryVectorStore.from_texts`` - Initialize from a list of texts (optionally with metadata)\n",
-    "- ``InMemoryVectorStore.from_existing_index`` - Initialize from an existing MemoryDB index\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore\n",
-    "\n",
-    "vds = InMemoryVectorStore.from_texts(\n",
-    "    embeddings,\n",
-    "    redis_url=\"rediss://cluster_endpoint:6379/ssl=True ssl_cert_reqs=none\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'users'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vds.index_name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Querying\n",
-    "\n",
-    "There are multiple ways to query the ``InMemoryVectorStore``  implementation based on what use case you have:\n",
-    "\n",
-    "- ``similarity_search``: Find the most similar vectors to a given vector.\n",
-    "- ``similarity_search_with_score``: Find the most similar vectors to a given vector and return the vector distance\n",
-    "- ``similarity_search_limit_score``: Find the most similar vectors to a given vector and limit the number of results to the ``score_threshold``\n",
-    "- ``similarity_search_with_relevance_scores``: Find the most similar vectors to a given vector and return the vector similarities\n",
-    "- ``max_marginal_relevance_search``: Find the most similar vectors to a given vector while also optimizing for diversity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "foo\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = vds.similarity_search(\"foo\")\n",
-    "print(results[0].page_content)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Content: foo --- Score: 0.0\n",
-      "Content: foo --- Score: 0.0\n",
-      "Content: foo --- Score: 0.0\n",
-      "Content: bar --- Score: 0.1566\n",
-      "Content: bar --- Score: 0.1566\n"
-     ]
-    }
-   ],
-   "source": [
-    "# with scores (distances)\n",
-    "results = vds.similarity_search_with_score(\"foo\", k=5)\n",
-    "for result in results:\n",
-    "    print(f\"Content: {result[0].page_content} --- Score: {result[1]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Content: foo --- Score: 0.0\n",
-      "Content: foo --- Score: 0.0\n",
-      "Content: foo --- Score: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# limit the vector distance that can be returned\n",
-    "results = vds.similarity_search_with_score(\"foo\", k=5, distance_threshold=0.1)\n",
-    "for result in results:\n",
-    "    print(f\"Content: {result[0].page_content} --- Score: {result[1]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Content: foo --- Similiarity: 1.0\n",
-      "Content: foo --- Similiarity: 1.0\n",
-      "Content: foo --- Similiarity: 1.0\n",
-      "Content: bar --- Similiarity: 0.8434\n",
-      "Content: bar --- Similiarity: 0.8434\n"
-     ]
-    }
-   ],
-   "source": [
-    "# with scores\n",
-    "results = vds.similarity_search_with_relevance_scores(\"foo\", k=5)\n",
-    "for result in results:\n",
-    "    print(f\"Content: {result[0].page_content} --- Similiarity: {result[1]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['doc:users:b9c71d62a0a34241a37950b448dafd38']"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can also add new documents as follows\n",
-    "new_document = [\"baz\"]\n",
-    "new_metadata = [{\"user\": \"sam\", \"age\": 50, \"job\": \"janitor\", \"credit_score\": \"high\"}]\n",
-    "# both the document and metadata must be lists\n",
-    "vds.add_texts(new_document, new_metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## MemoryDB as Retriever\n",
-    "\n",
-    "Here we go over different options for using the vector store as a retriever.\n",
-    "\n",
-    "There are three different search methods we can use to do retrieval. By default, it will use semantic similarity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Content: foo  --- Score:  0.0\n",
-      "Content: foo  --- Score:  0.0\n",
-      "Content: foo  --- Score:  0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"foo\"\n",
-    "results = vds.similarity_search_with_score(query, k=3, return_metadata=True)\n",
-    "\n",
-    "for result in results:\n",
-    "    print(\"Content:\", result[0].page_content, \" --- Score: \", result[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vds.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 4})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='foo', metadata={'id': 'doc:users_modified:988ecca7574048e396756efc0e79aeca', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'}),\n",
-       " Document(page_content='bar', metadata={'id': 'doc:users_modified:01ef6caac12b42c28ad870aefe574253', 'user': 'tyler', 'job': 'engineer', 'credit_score': 'high', 'age': '100'})]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = retriever.invoke(query)\n",
-    "docs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is also the `similarity_distance_threshold` retriever which allows the user to specify the vector distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vds.as_retriever(\n",
-    "    search_type=\"similarity_distance_threshold\",\n",
-    "    search_kwargs={\"k\": 4, \"distance_threshold\": 0.1},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='foo', metadata={'id': 'doc:users_modified:988ecca7574048e396756efc0e79aeca', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'})]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = retriever.invoke(query)\n",
-    "docs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lastly, the ``similarity_score_threshold`` allows the user to define the minimum score for similar documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vds.as_retriever(\n",
-    "    search_type=\"similarity_score_threshold\",\n",
-    "    search_kwargs={\"score_threshold\": 0.9, \"k\": 10},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='foo', metadata={'id': 'doc:users_modified:988ecca7574048e396756efc0e79aeca', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users_modified:009b1afeb4084cc6bdef858c7a99b48e', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users_modified:7087cee9be5b4eca93c30fbdd09a2731', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'})]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever.invoke(\"foo\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='foo', metadata={'id': 'doc:users:8f6b673b390647809d510112cde01a27', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}),\n",
-       " Document(page_content='bar', metadata={'id': 'doc:users:93521560735d42328b48c9c6f6418d6a', 'user': 'tyler', 'job': 'engineer', 'credit_score': 'high', 'age': '100'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users:125ecd39d07845eabf1a699d44134a5b', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'}),\n",
-       " Document(page_content='foo', metadata={'id': 'doc:users:d6200ab3764c466082fde3eaab972a2a', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'})]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever.invoke(\"foo\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete  index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To delete your entries you have to address them by their keys."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# delete the indices too\n",
-    "InMemoryVectorStore.drop_index(\n",
-    "    index_name=\"users\", delete_documents=True, redis_url=\"redis://localhost:6379\"\n",
-    ")\n",
-    "InMemoryVectorStore.drop_index(\n",
-    "    index_name=\"users_modified\",\n",
-    "    delete_documents=True,\n",
-    "    redis_url=\"redis://localhost:6379\",\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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/vectorstores/qdrant.ipynb

@@ -317,7 +317,7 @@
     "To search with only dense vectors,\n",
     "\n",
     "- The `retrieval_mode` parameter should be set to `RetrievalMode.DENSE`(default).\n",
-    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided to the `embedding` parameter."
+    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided for the `embedding` parameter."
    ]
   },
   {
@@ -407,7 +407,7 @@
     "To perform a hybrid search using dense and sparse vectors with score fusion,\n",
     "\n",
     "- The `retrieval_mode` parameter should be set to `RetrievalMode.HYBRID`.\n",
-    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided to the `embedding` parameter.\n",
+    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided for the `embedding` parameter.\n",
     "- An implementation of the [`SparseEmbeddings`](https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py) interface using any sparse embeddings provider has to be provided as value to the `sparse_embedding` parameter.\n",
     "\n",
     "Note that if you've added documents with the `HYBRID` mode, you can switch to any retrieval mode when searching. Since both the dense and sparse vectors are available in the collection."

docs/docs/integrations/vectorstores/tidb_vector.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# TiDB Vector\n",
     "\n",
-    "> [TiDB Cloud](https://www.pingcap.com/tidb-serverless), is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Create a free TiDB Serverless cluster and start using the vector search feature at https://pingcap.com/ai.\n",
+    "> [TiDB Cloud](https://tidbcloud.com/), is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Be among the first to experience it by joining the waitlist for the private beta at https://tidb.cloud/ai.\n",
     "\n",
     "This notebook provides a detailed guide on utilizing the TiDB Vector functionality, showcasing its features and practical applications."
    ]

docs/docs/integrations/vectorstores/vdms.ipynb

@@ -12,8 +12,7 @@
     "VDMS supports:\n",
     "* K nearest neighbor search\n",
     "* Euclidean distance (L2) and inner product (IP)\n",
-    "* Libraries for indexing and computing distances: TileDBDense, TileDBSparse, FaissFlat (Default), FaissIVFFlat, Flinng\n",
-    "* Embeddings for text, images, and video\n",
+    "* Libraries for indexing and computing distances: TileDBDense, TileDBSparse, FaissFlat (Default), FaissIVFFlat\n",
     "* Vector and metadata searches\n",
     "\n",
     "VDMS has server and client components. To setup the server, see the [installation instructions](https://github.com/IntelLabs/vdms/blob/master/INSTALL.md) or use the [docker image](https://hub.docker.com/r/intellabs/vdms).\n",
@@ -41,7 +40,7 @@
    ],
    "source": [
     "# Pip install necessary package\n",
-    "%pip install --upgrade --quiet pip vdms sentence-transformers langchain-huggingface > /dev/null"
+    "%pip install --upgrade --quiet pip sentence-transformers vdms \"unstructured-inference==0.6.6\";"
    ]
   },
   {
@@ -63,7 +62,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "b26917ffac236673ef1d035ab9c91fe999e29c9eb24aa6c7103d7baa6bf2f72d\n"
+      "e6061b270eef87de5319a6c5af709b36badcad8118069a8f6b577d2e01ad5e2d\n"
      ]
     }
    ],
@@ -93,9 +92,6 @@
    "outputs": [],
    "source": [
     "import time\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "from langchain_community.document_loaders.text import TextLoader\n",
     "from langchain_community.vectorstores import VDMS\n",
@@ -294,7 +290,7 @@
    "source": [
     "# add data\n",
     "collection_name = \"my_collection_faiss_L2\"\n",
-    "db_FaissFlat = VDMS.from_documents(\n",
+    "db = VDMS.from_documents(\n",
     "    docs,\n",
     "    client=vdms_client,\n",
     "    ids=ids,\n",
@@ -305,7 +301,7 @@
     "# Query (No metadata filtering)\n",
     "k = 3\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "returned_docs = db_FaissFlat.similarity_search(query, k=k, filter=None)\n",
+    "returned_docs = db.similarity_search(query, k=k, filter=None)\n",
     "print_results(returned_docs, score=False)"
    ]
   },
@@ -396,226 +392,10 @@
     "k = 3\n",
     "constraints = {\"page_number\": [\">\", 30], \"president_included\": [\"==\", True]}\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "returned_docs = db_FaissFlat.similarity_search(query, k=k, filter=constraints)\n",
+    "returned_docs = db.similarity_search(query, k=k, filter=constraints)\n",
     "print_results(returned_docs, score=False)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "92ab3370",
-   "metadata": {},
-   "source": [
-    "### Similarity Search using Faiss IVFFlat and Inner Product (IP) Distance\n",
-    "\n",
-    "In this section, we add the documents to VDMS using Faiss IndexIVFFlat indexing and IP as the distance metric for similarity search. We search for three documents (`k=3`) related to the query `What did the president say about Ketanji Brown Jackson` and also return the score along with the document.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "78f502cf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------\n",
-      "\n",
-      "Score:\t1.2032090425\n",
-      "\n",
-      "Content:\n",
-      "\tTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "\n",
-      "Metadata:\n",
-      "\tid:\t32\n",
-      "\tpage_number:\t32\n",
-      "\tpresident_included:\tTrue\n",
-      "\tsource:\t../../how_to/state_of_the_union.txt\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Score:\t1.4952471256\n",
-      "\n",
-      "Content:\n",
-      "\tAs Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
-      "\n",
-      "It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
-      "\n",
-      "And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
-      "\n",
-      "Third, support our veterans. \n",
-      "\n",
-      "Veterans are the best of us. \n",
-      "\n",
-      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
-      "\n",
-      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
-      "\n",
-      "Our troops in Iraq and Afghanistan faced many dangers.\n",
-      "\n",
-      "Metadata:\n",
-      "\tid:\t37\n",
-      "\tpage_number:\t37\n",
-      "\tpresident_included:\tFalse\n",
-      "\tsource:\t../../how_to/state_of_the_union.txt\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Score:\t1.5008399487\n",
-      "\n",
-      "Content:\n",
-      "\tA former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
-      "\n",
-      "Metadata:\n",
-      "\tid:\t33\n",
-      "\tpage_number:\t33\n",
-      "\tpresident_included:\tFalse\n",
-      "\tsource:\t../../how_to/state_of_the_union.txt\n",
-      "--------------------------------------------------\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "db_FaissIVFFlat = VDMS.from_documents(\n",
-    "    docs,\n",
-    "    client=vdms_client,\n",
-    "    ids=ids,\n",
-    "    collection_name=\"my_collection_FaissIVFFlat_IP\",\n",
-    "    embedding=embedding,\n",
-    "    engine=\"FaissIVFFlat\",\n",
-    "    distance_strategy=\"IP\",\n",
-    ")\n",
-    "# Query\n",
-    "k = 3\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs_with_score = db_FaissIVFFlat.similarity_search_with_score(query, k=k, filter=None)\n",
-    "print_results(docs_with_score)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e66d9125",
-   "metadata": {},
-   "source": [
-    "### Similarity Search using FLINNG and IP Distance\n",
-    "\n",
-    "In this section, we add the documents to VDMS using Filters to Identify Near-Neighbor Groups (FLINNG) indexing and IP as the distance metric for similarity search. We search for three documents (`k=3`) related to the query `What did the president say about Ketanji Brown Jackson` and also return the score along with the document."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "add81beb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------\n",
-      "\n",
-      "Score:\t1.2032090425\n",
-      "\n",
-      "Content:\n",
-      "\tTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "\n",
-      "Metadata:\n",
-      "\tid:\t32\n",
-      "\tpage_number:\t32\n",
-      "\tpresident_included:\tTrue\n",
-      "\tsource:\t../../how_to/state_of_the_union.txt\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Score:\t1.4952471256\n",
-      "\n",
-      "Content:\n",
-      "\tAs Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
-      "\n",
-      "It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
-      "\n",
-      "And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
-      "\n",
-      "Third, support our veterans. \n",
-      "\n",
-      "Veterans are the best of us. \n",
-      "\n",
-      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
-      "\n",
-      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
-      "\n",
-      "Our troops in Iraq and Afghanistan faced many dangers.\n",
-      "\n",
-      "Metadata:\n",
-      "\tid:\t37\n",
-      "\tpage_number:\t37\n",
-      "\tpresident_included:\tFalse\n",
-      "\tsource:\t../../how_to/state_of_the_union.txt\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "Score:\t1.5008399487\n",
-      "\n",
-      "Content:\n",
-      "\tA former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
-      "\n",
-      "Metadata:\n",
-      "\tid:\t33\n",
-      "\tpage_number:\t33\n",
-      "\tpresident_included:\tFalse\n",
-      "\tsource:\t../../how_to/state_of_the_union.txt\n",
-      "--------------------------------------------------\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "db_Flinng = VDMS.from_documents(\n",
-    "    docs,\n",
-    "    client=vdms_client,\n",
-    "    ids=ids,\n",
-    "    collection_name=\"my_collection_Flinng_IP\",\n",
-    "    embedding=embedding,\n",
-    "    engine=\"Flinng\",\n",
-    "    distance_strategy=\"IP\",\n",
-    ")\n",
-    "# Query\n",
-    "k = 3\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs_with_score = db_Flinng.similarity_search_with_score(query, k=k, filter=None)\n",
-    "print_results(docs_with_score)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "a5984766",
@@ -629,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "3001ba6e",
    "metadata": {},
    "outputs": [
@@ -639,7 +419,7 @@
      "text": [
       "--------------------------------------------------\n",
       "\n",
-      "Score:\t1.2032090425\n",
+      "Score:\t1.2032090425491333\n",
       "\n",
       "Content:\n",
       "\tTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
@@ -657,7 +437,7 @@
       "\tsource:\t../../how_to/state_of_the_union.txt\n",
       "--------------------------------------------------\n",
       "\n",
-      "Score:\t1.4952471256\n",
+      "Score:\t1.495247483253479\n",
       "\n",
       "Content:\n",
       "\tAs Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
@@ -683,7 +463,7 @@
       "\tsource:\t../../how_to/state_of_the_union.txt\n",
       "--------------------------------------------------\n",
       "\n",
-      "Score:\t1.5008399487\n",
+      "Score:\t1.5008409023284912\n",
       "\n",
       "Content:\n",
       "\tA former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
@@ -725,6 +505,114 @@
     "print_results(docs_with_score)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "92ab3370",
+   "metadata": {},
+   "source": [
+    "### Similarity Search using Faiss IVFFlat and Euclidean Distance\n",
+    "\n",
+    "In this section, we add the documents to VDMS using Faiss IndexIVFFlat indexing and L2 as the distance metric for similarity search. We search for three documents (`k=3`) related to the query `What did the president say about Ketanji Brown Jackson` and also return the score along with the document.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78f502cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "\n",
+      "Score:\t1.2032090425491333\n",
+      "\n",
+      "Content:\n",
+      "\tTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "\n",
+      "Metadata:\n",
+      "\tid:\t32\n",
+      "\tpage_number:\t32\n",
+      "\tpresident_included:\tTrue\n",
+      "\tsource:\t../../how_to/state_of_the_union.txt\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Score:\t1.495247483253479\n",
+      "\n",
+      "Content:\n",
+      "\tAs Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. \n",
+      "\n",
+      "It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. \n",
+      "\n",
+      "And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. \n",
+      "\n",
+      "Third, support our veterans. \n",
+      "\n",
+      "Veterans are the best of us. \n",
+      "\n",
+      "I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. \n",
+      "\n",
+      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
+      "\n",
+      "Our troops in Iraq and Afghanistan faced many dangers.\n",
+      "\n",
+      "Metadata:\n",
+      "\tid:\t37\n",
+      "\tpage_number:\t37\n",
+      "\tpresident_included:\tFalse\n",
+      "\tsource:\t../../how_to/state_of_the_union.txt\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Score:\t1.5008409023284912\n",
+      "\n",
+      "Content:\n",
+      "\tA former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "\n",
+      "Metadata:\n",
+      "\tid:\t33\n",
+      "\tpage_number:\t33\n",
+      "\tpresident_included:\tFalse\n",
+      "\tsource:\t../../how_to/state_of_the_union.txt\n",
+      "--------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "db_FaissIVFFlat = VDMS.from_documents(\n",
+    "    docs,\n",
+    "    client=vdms_client,\n",
+    "    ids=ids,\n",
+    "    collection_name=\"my_collection_FaissIVFFlat_L2\",\n",
+    "    embedding=embedding,\n",
+    "    engine=\"FaissIVFFlat\",\n",
+    "    distance_strategy=\"L2\",\n",
+    ")\n",
+    "# Query\n",
+    "k = 3\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs_with_score = db_FaissIVFFlat.similarity_search_with_score(query, k=k, filter=None)\n",
+    "print_results(docs_with_score)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9ed3ec50",
@@ -734,12 +622,12 @@
     "\n",
     "While building toward a real application, you want to go beyond adding data, and also update and delete data.\n",
     "\n",
-    "Here is a basic example showing how to do so.  First, we will update the metadata for the document most relevant to the query by adding a date. "
+    "Here is a basic example showing how to do so.  First, we will update the metadata for the document most relevant to the query."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "81a02810",
    "metadata": {},
    "outputs": [
@@ -750,7 +638,7 @@
       "Original metadata: \n",
       "\t{'id': '32', 'page_number': 32, 'president_included': True, 'source': '../../how_to/state_of_the_union.txt'}\n",
       "new metadata: \n",
-      "\t{'id': '32', 'page_number': 32, 'president_included': True, 'source': '../../how_to/state_of_the_union.txt', 'last_date_read': {'_date': '2024-05-01T14:30:00'}}\n",
+      "\t{'id': '32', 'page_number': 32, 'president_included': True, 'source': '../../how_to/state_of_the_union.txt', 'new_value': 'hello world'}\n",
       "--------------------------------------------------\n",
       "\n",
       "UPDATED ENTRY (id=32):\n",
@@ -767,8 +655,8 @@
       "id:\n",
       "\t32\n",
       "\n",
-      "last_date_read:\n",
-      "\t2024-05-01T14:30:00+00:00\n",
+      "new_value:\n",
+      "\thello world\n",
       "\n",
       "page_number:\n",
       "\t32\n",
@@ -784,26 +672,19 @@
     }
    ],
    "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "doc = db_FaissFlat.similarity_search(query)[0]\n",
+    "doc = db.similarity_search(query)[0]\n",
     "print(f\"Original metadata: \\n\\t{doc.metadata}\")\n",
     "\n",
-    "# Update the metadata for a document by adding last datetime document read\n",
-    "datetime_str = datetime(2024, 5, 1, 14, 30, 0).isoformat()\n",
-    "doc.metadata[\"last_date_read\"] = {\"_date\": datetime_str}\n",
+    "# update the metadata for a document\n",
+    "doc.metadata[\"new_value\"] = \"hello world\"\n",
     "print(f\"new metadata: \\n\\t{doc.metadata}\")\n",
     "print(f\"{DELIMITER}\\n\")\n",
     "\n",
     "# Update document in VDMS\n",
     "id_to_update = doc.metadata[\"id\"]\n",
-    "db_FaissFlat.update_document(collection_name, id_to_update, doc)\n",
-    "response, response_array = db_FaissFlat.get(\n",
-    "    collection_name,\n",
-    "    constraints={\n",
-    "        \"id\": [\"==\", id_to_update],\n",
-    "        \"last_date_read\": [\">=\", {\"_date\": \"2024-05-01T00:00:00\"}],\n",
-    "    },\n",
+    "db.update_document(collection_name, id_to_update, doc)\n",
+    "response, response_array = db.get(\n",
+    "    collection_name, constraints={\"id\": [\"==\", id_to_update]}\n",
     ")\n",
     "\n",
     "# Display Results\n",
@@ -821,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "95537fe8",
    "metadata": {},
    "outputs": [
@@ -835,13 +716,11 @@
     }
    ],
    "source": [
-    "print(\"Documents before deletion: \", db_FaissFlat.count(collection_name))\n",
+    "print(\"Documents before deletion: \", db.count(collection_name))\n",
     "\n",
     "id_to_remove = ids[-1]\n",
-    "db_FaissFlat.delete(collection_name=collection_name, ids=[id_to_remove])\n",
-    "print(\n",
-    "    f\"Documents after deletion (id={id_to_remove}): {db_FaissFlat.count(collection_name)}\"\n",
-    ")"
+    "db.delete(collection_name=collection_name, ids=[id_to_remove])\n",
+    "print(f\"Documents after deletion (id={id_to_remove}): {db.count(collection_name)}\")"
    ]
   },
   {
@@ -860,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "1db4d6ed",
    "metadata": {},
    "outputs": [
@@ -879,7 +758,7 @@
       "\n",
       "Metadata:\n",
       "\tid:\t32\n",
-      "\tlast_date_read:\t2024-05-01T14:30:00+00:00\n",
+      "\tnew_value:\thello world\n",
       "\tpage_number:\t32\n",
       "\tpresident_included:\tTrue\n",
       "\tsource:\t../../how_to/state_of_the_union.txt\n"
@@ -888,7 +767,7 @@
    ],
    "source": [
     "embedding_vector = embedding.embed_query(query)\n",
-    "returned_docs = db_FaissFlat.similarity_search_by_vector(embedding_vector)\n",
+    "returned_docs = db.similarity_search_by_vector(embedding_vector)\n",
     "\n",
     "# Print Results\n",
     "print_document_details(returned_docs[0])"
@@ -908,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "2bc0313b",
    "metadata": {},
    "outputs": [
@@ -916,7 +795,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Deleted entry:\n",
+      "Returned entry:\n",
       "\n",
       "blob:\n",
       "\tTrue\n",
@@ -959,18 +838,18 @@
     }
    ],
    "source": [
-    "response, response_array = db_FaissFlat.get(\n",
+    "response, response_array = db.get(\n",
     "    collection_name,\n",
     "    limit=1,\n",
     "    include=[\"metadata\", \"embeddings\"],\n",
     "    constraints={\"id\": [\"==\", \"2\"]},\n",
     ")\n",
     "\n",
-    "# Delete id=2\n",
-    "db_FaissFlat.delete(collection_name=collection_name, ids=[\"2\"])\n",
+    "print(\"Returned entry:\")\n",
+    "print_response([response[0][\"FindDescriptor\"][\"entities\"][0]])\n",
     "\n",
-    "print(\"Deleted entry:\")\n",
-    "print_response([response[0][\"FindDescriptor\"][\"entities\"][0]])"
+    "# Delete id=2\n",
+    "db.delete(collection_name=collection_name, ids=[\"2\"])"
    ]
   },
   {
@@ -990,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "120f55eb",
    "metadata": {},
    "outputs": [
@@ -1009,7 +888,7 @@
       "\n",
       "Metadata:\n",
       "\tid:\t32\n",
-      "\tlast_date_read:\t2024-05-01T14:30:00+00:00\n",
+      "\tnew_value:\thello world\n",
       "\tpage_number:\t32\n",
       "\tpresident_included:\tTrue\n",
       "\tsource:\t../../how_to/state_of_the_union.txt\n"
@@ -1017,7 +896,7 @@
     }
    ],
    "source": [
-    "retriever = db_FaissFlat.as_retriever()\n",
+    "retriever = db.as_retriever()\n",
     "relevant_docs = retriever.invoke(query)[0]\n",
     "\n",
     "print_document_details(relevant_docs)"
@@ -1035,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "f00be6d0",
    "metadata": {},
    "outputs": [
@@ -1054,7 +933,7 @@
       "\n",
       "Metadata:\n",
       "\tid:\t32\n",
-      "\tlast_date_read:\t2024-05-01T14:30:00+00:00\n",
+      "\tnew_value:\thello world\n",
       "\tpage_number:\t32\n",
       "\tpresident_included:\tTrue\n",
       "\tsource:\t../../how_to/state_of_the_union.txt\n"
@@ -1062,7 +941,7 @@
     }
    ],
    "source": [
-    "retriever = db_FaissFlat.as_retriever(search_type=\"mmr\")\n",
+    "retriever = db.as_retriever(search_type=\"mmr\")\n",
     "relevant_docs = retriever.invoke(query)[0]\n",
     "\n",
     "print_document_details(relevant_docs)"
@@ -1078,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "ab911470",
    "metadata": {},
    "outputs": [
@@ -1088,7 +967,7 @@
      "text": [
       "--------------------------------------------------\n",
       "\n",
-      "Score:\t1.2032091618\n",
+      "Score:\t1.2032092809677124\n",
       "\n",
       "Content:\n",
       "\tTonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
@@ -1101,13 +980,13 @@
       "\n",
       "Metadata:\n",
       "\tid:\t32\n",
-      "\tlast_date_read:\t2024-05-01T14:30:00+00:00\n",
+      "\tnew_value:\thello world\n",
       "\tpage_number:\t32\n",
       "\tpresident_included:\tTrue\n",
       "\tsource:\t../../how_to/state_of_the_union.txt\n",
       "--------------------------------------------------\n",
       "\n",
-      "Score:\t1.50705266\n",
+      "Score:\t1.507053256034851\n",
       "\n",
       "Content:\n",
       "\tBut cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. \n",
@@ -1143,7 +1022,7 @@
     }
    ],
    "source": [
-    "mmr_resp = db_FaissFlat.max_marginal_relevance_search_with_score(query, k=2, fetch_k=10)\n",
+    "mmr_resp = db.max_marginal_relevance_search_with_score(query, k=2, fetch_k=10)\n",
     "print_results(mmr_resp)"
    ]
   },
@@ -1158,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "874e7af9",
    "metadata": {},
    "outputs": [
@@ -1172,11 +1051,11 @@
     }
    ],
    "source": [
-    "print(\"Documents before deletion: \", db_FaissFlat.count(collection_name))\n",
+    "print(\"Documents before deletion: \", db.count(collection_name))\n",
     "\n",
-    "db_FaissFlat.delete(collection_name=collection_name)\n",
+    "db.delete(collection_name=collection_name)\n",
     "\n",
-    "print(\"Documents after deletion: \", db_FaissFlat.count(collection_name))"
+    "print(\"Documents after deletion: \", db.count(collection_name))"
    ]
   },
   {
@@ -1189,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "08931796",
    "metadata": {},
    "outputs": [
@@ -1218,7 +1097,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a60725a6",
+   "id": "0386ea81",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1240,7 +1119,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/versions/migrating_chains/conversation_chain.ipynb

@@ -1,262 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "030d95bc-2f9d-492b-8245-b791b866936b",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "title: Migrating from ConversationalChain\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d20aeaad-b3ca-4a7d-b02d-3267503965af",
-   "metadata": {},
-   "source": [
-    "[`ConversationChain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html) incorporates a memory of previous messages to sustain a stateful conversation.\n",
-    "\n",
-    "Some advantages of switching to the LCEL implementation are:\n",
-    "\n",
-    "- Innate support for threads/separate sessions. To make this work with `ConversationChain`, you'd need to instantiate a separate memory class outside the chain.\n",
-    "- More explicit parameters. `ConversationChain` contains a hidden default prompt, which can cause confusion.\n",
-    "- Streaming support. `ConversationChain` only supports streaming via callbacks.\n",
-    "\n",
-    "`RunnableWithMessageHistory` implements sessions via configuration parameters. It should be instantiated with a callable that returns a [chat message history](https://api.python.langchain.com/en/latest/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html). By default, it expects this function to take a single argument `session_id`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b99b47ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "717c8673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00df631d-5121-4918-94aa-b88acce9b769",
-   "metadata": {},
-   "source": [
-    "import { ColumnContainer, Column } from \"@theme/Columns\";\n",
-    "\n",
-    "<ColumnContainer>\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4f2cc6dc-d70a-4c13-9258-452f14290da6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'how are you?',\n",
-       " 'history': '',\n",
-       " 'response': \"Arr matey, I be doin' well on the high seas, plunderin' and pillagin' as usual. How be ye?\"}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import ConversationChain\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "template = \"\"\"\n",
-    "You are a pirate. Answer the following questions as best you can.\n",
-    "Chat history: {history}\n",
-    "Question: {input}\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "memory = ConversationBufferMemory()\n",
-    "\n",
-    "chain = ConversationChain(\n",
-    "    llm=ChatOpenAI(),\n",
-    "    memory=memory,\n",
-    "    prompt=prompt,\n",
-    ")\n",
-    "\n",
-    "chain({\"input\": \"how are you?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8e36b0e-c7dc-4130-a51b-189d4b756c7f",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "666c92a0-b555-4418-a465-6490c1b92570",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Arr, me matey! I be doin' well, sailin' the high seas and searchin' for treasure. How be ye?\""
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.chat_history import InMemoryChatMessageHistory\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a pirate. Answer the following questions as best you can.\"),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "history = InMemoryChatMessageHistory()\n",
-    "\n",
-    "\n",
-    "def get_history():\n",
-    "    return history\n",
-    "\n",
-    "\n",
-    "chain = prompt | ChatOpenAI() | StrOutputParser()\n",
-    "\n",
-    "wrapped_chain = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    get_history,\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "wrapped_chain.invoke({\"input\": \"how are you?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b386ce6-895e-442c-88f3-7bec0ab9f401",
-   "metadata": {},
-   "source": [
-    "\n",
-    "</Column>\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "The above example uses the same `history` for all sessions. The example below shows how to use a different chat history for each session."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "96152263-98d7-4e06-8c73-d0c0abf3e8e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Ahoy there, me hearty! What can this old pirate do for ye today?'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "\n",
-    "store = {}\n",
-    "\n",
-    "\n",
-    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
-    "    if session_id not in store:\n",
-    "        store[session_id] = InMemoryChatMessageHistory()\n",
-    "    return store[session_id]\n",
-    "\n",
-    "\n",
-    "chain = prompt | ChatOpenAI() | StrOutputParser()\n",
-    "\n",
-    "wrapped_chain = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    get_session_history,\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "wrapped_chain.invoke(\n",
-    "    {\"input\": \"Hello!\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2717810",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "See [this tutorial](/docs/tutorials/chatbot) for a more end-to-end guide on building with [`RunnableWithMessageHistory`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html).\n",
-    "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
-   ]
-  }
- ],
- "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
-}

docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb

@@ -1,289 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9e279999-6bf0-4a48-9e06-539b916dc705",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "title: Migrating from ConversationalRetrievalChain\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "292a3c83-44a9-4426-bbec-f1a778d00d93",
-   "metadata": {},
-   "source": [
-    "The [`ConversationalRetrievalChain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html) was an all-in one way that combined retrieval-augmented generation with chat history, allowing you to \"chat with\" your documents.\n",
-    "\n",
-    "Advantages of switching to the LCEL implementation are similar to the `RetrievalQA` section above:\n",
-    "\n",
-    "- Clearer internals. The `ConversationalRetrievalChain` chain hides an entire question rephrasing step which dereferences the initial query against the chat history.\n",
-    "  - This means the class contains two sets of configurable prompts, LLMs, etc.\n",
-    "- More easily return source documents.\n",
-    "- Support for runnable methods like streaming and async operations.\n",
-    "\n",
-    "Here are side-by-side implementations with custom prompts. We'll reuse the loaded documents and vector store from the previous section:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b99b47ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain-community langchain langchain-openai faiss-cpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "717c8673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "44119498-5a98-4077-9e2f-c75500e7eace",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load docs\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai.chat_models import ChatOpenAI\n",
-    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
-    "\n",
-    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
-    "data = loader.load()\n",
-    "\n",
-    "# Split\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
-    "all_splits = text_splitter.split_documents(data)\n",
-    "\n",
-    "# Store splits\n",
-    "vectorstore = FAISS.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())\n",
-    "\n",
-    "# LLM\n",
-    "llm = ChatOpenAI()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bc06416",
-   "metadata": {},
-   "source": [
-    "import { ColumnContainer, Column } from \"@theme/Columns\";\n",
-    "\n",
-    "<ColumnContainer>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8b471e7d-3ccb-4ab3-bc09-304c4b14a908",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'question': 'What are autonomous agents?',\n",
-       " 'chat_history': '',\n",
-       " 'answer': 'Autonomous agents are entities empowered with capabilities like planning, task decomposition, and memory to perform complex tasks independently. These agents can leverage tools like browsing the internet, reading documentation, executing code, and calling APIs to achieve their objectives. They are designed to handle tasks like scientific discovery and experimentation autonomously.'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "condense_question_template = \"\"\"\n",
-    "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n",
-    "\n",
-    "Chat History:\n",
-    "{chat_history}\n",
-    "Follow Up Input: {question}\n",
-    "Standalone question:\"\"\"\n",
-    "\n",
-    "condense_question_prompt = ChatPromptTemplate.from_template(condense_question_template)\n",
-    "\n",
-    "qa_template = \"\"\"\n",
-    "You are an assistant for question-answering tasks.\n",
-    "Use the following pieces of retrieved context to answer\n",
-    "the question. If you don't know the answer, say that you\n",
-    "don't know. Use three sentences maximum and keep the\n",
-    "answer concise.\n",
-    "\n",
-    "Chat History:\n",
-    "{chat_history}\n",
-    "\n",
-    "Other context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "\n",
-    "qa_prompt = ChatPromptTemplate.from_template(qa_template)\n",
-    "\n",
-    "convo_qa_chain = ConversationalRetrievalChain.from_llm(\n",
-    "    llm,\n",
-    "    vectorstore.as_retriever(),\n",
-    "    condense_question_prompt=condense_question_prompt,\n",
-    "    combine_docs_chain_kwargs={\n",
-    "        \"prompt\": qa_prompt,\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "convo_qa_chain(\n",
-    "    {\n",
-    "        \"question\": \"What are autonomous agents?\",\n",
-    "        \"chat_history\": \"\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43a8a23c",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "35657a13-ad67-4af1-b1f9-f58606ae43b4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'What are autonomous agents?',\n",
-       " 'chat_history': [],\n",
-       " 'context': [Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs and leverage other LLMs.\\nFor example, when requested to \"develop a novel anticancer drug\", the model came up with the following reasoning steps:'),\n",
-       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.'),\n",
-       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#'),\n",
-       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content=\"LLM Powered Autonomous Agents | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\nemojisearch.app\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n      LLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\")],\n",
-       " 'answer': 'Autonomous agents are entities that can act independently to achieve specific goals or tasks without direct human intervention. These agents have the ability to perceive their environment, make decisions, and take actions based on their programming or learning. They can perform tasks such as planning, execution, and problem-solving autonomously.'}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
-    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "\n",
-    "condense_question_system_template = (\n",
-    "    \"Given a chat history and the latest user question \"\n",
-    "    \"which might reference context in the chat history, \"\n",
-    "    \"formulate a standalone question which can be understood \"\n",
-    "    \"without the chat history. Do NOT answer the question, \"\n",
-    "    \"just reformulate it if needed and otherwise return it as is.\"\n",
-    ")\n",
-    "\n",
-    "condense_question_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", condense_question_system_template),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "history_aware_retriever = create_history_aware_retriever(\n",
-    "    llm, vectorstore.as_retriever(), condense_question_prompt\n",
-    ")\n",
-    "\n",
-    "system_prompt = (\n",
-    "    \"You are an assistant for question-answering tasks. \"\n",
-    "    \"Use the following pieces of retrieved context to answer \"\n",
-    "    \"the question. If you don't know the answer, say that you \"\n",
-    "    \"don't know. Use three sentences maximum and keep the \"\n",
-    "    \"answer concise.\"\n",
-    "    \"\\n\\n\"\n",
-    "    \"{context}\"\n",
-    ")\n",
-    "\n",
-    "qa_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", system_prompt),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "qa_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
-    "\n",
-    "convo_qa_chain = create_retrieval_chain(history_aware_retriever, qa_chain)\n",
-    "\n",
-    "convo_qa_chain.invoke(\n",
-    "    {\n",
-    "        \"input\": \"What are autonomous agents?\",\n",
-    "        \"chat_history\": [],\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2717810",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "## Next steps\n",
-    "\n",
-    "You've now seen how to migrate existing usage of some legacy chains to LCEL.\n",
-    "\n",
-    "Next, check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
-   ]
-  }
- ],
- "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
-}

docs/docs/versions/migrating_chains/index.mdx

@@ -1,34 +0,0 @@
----
-sidebar_position: 1
----
-
-# How to migrate chains to LCEL
-
-:::info Prerequisites
-
-This guide assumes familiarity with the following concepts:
-- [LangChain Expression Language](/docs/concepts#langchain-expression-language-lcel)
-
-:::
-
-LCEL is designed to streamline the process of building useful apps with LLMs and combining related components. It does this by providing:
-
-1. **A unified interface**: Every LCEL object implements the `Runnable` interface, which defines a common set of invocation methods (`invoke`, `batch`, `stream`, `ainvoke`, ...). This makes it possible to also automatically and consistently support useful operations like streaming of intermediate steps and batching, since every chain composed of LCEL objects is itself an LCEL object.
-2. **Composition primitives**: LCEL provides a number of primitives that make it easy to compose chains, parallelize components, add fallbacks, dynamically configure chain internals, and more.
-
-LangChain maintains a number of legacy abstractions. Many of these can be reimplemented via short combinations of LCEL primitives. Doing so confers some general advantages:
-
-- The resulting chains typically implement the full `Runnable` interface, including streaming and asynchronous support where appropriate;
-- The chains may be more easily extended or modified;
-- The parameters of the chain are typically surfaced for easier customization (e.g., prompts) over previous versions, which tended to be subclasses and had opaque parameters and internals.
-
-The LCEL implementations can be slightly more verbose, but there are significant benefits in transparency and customizability.
-
-The below pages assist with migration from various specific chains to LCEL:
-
-- [LLMChain](/docs/versions/migrating_chains/llm_chain)
-- [ConversationChain](/docs/versions/migrating_chains/conversation_chain)
-- [RetrievalQA](/docs/versions/migrating_chains/retrieval_qa)
-- [ConversationalRetrievalChain](/docs/versions/migrating_chains/conversation_retrieval_chain)
-
-Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information.

docs/docs/versions/migrating_chains/llm_chain.ipynb

@@ -1,213 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b57124cc-60a0-4c18-b7ce-3e483d1024a2",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "title: Migrating from LLMChain\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce8457ed-c0b1-4a74-abbd-9d3d2211270f",
-   "metadata": {},
-   "source": [
-    "[`LLMChain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html) combined a prompt template, LLM, and output parser into a class.\n",
-    "\n",
-    "Some advantages of switching to the LCEL implementation are:\n",
-    "\n",
-    "- Clarity around contents and parameters. The legacy `LLMChain` contains a default output parser and other options.\n",
-    "- Easier streaming. `LLMChain` only supports streaming via callbacks.\n",
-    "- Easier access to raw message outputs if desired. `LLMChain` only exposes these via a parameter or via callback."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b99b47ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "717c8673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3621b62-a037-42b8-8faa-59575608bb8b",
-   "metadata": {},
-   "source": [
-    "import { ColumnContainer, Column } from \"@theme/Columns\";\n",
-    "\n",
-    "<ColumnContainer>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f91c9809-8ee7-4e38-881d-0ace4f6ea883",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'adjective': 'funny',\n",
-       " 'text': \"Why couldn't the bicycle stand up by itself?\\n\\nBecause it was two tired!\"}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"user\", \"Tell me a {adjective} joke\")],\n",
-    ")\n",
-    "\n",
-    "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\n",
-    "\n",
-    "chain({\"adjective\": \"funny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cdc3b527-c09e-4c77-9711-c3cc4506cd95",
-   "metadata": {},
-   "source": [
-    "\n",
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f0903025-9aa8-4a53-8336-074341c00e59",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Why was the math book sad?\\n\\nBecause it had too many problems.'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"user\", \"Tell me a {adjective} joke\")],\n",
-    ")\n",
-    "\n",
-    "chain = prompt | ChatOpenAI() | StrOutputParser()\n",
-    "\n",
-    "chain.invoke({\"adjective\": \"funny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c0b0513-77b8-4371-a20e-3e487cec7e7f",
-   "metadata": {},
-   "source": [
-    "\n",
-    "</Column>\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "Note that `LLMChain` by default returns a `dict` containing both the input and the output. If this behavior is desired, we can replicate it using another LCEL primitive, [`RunnablePassthrough`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "20f11321-834a-485a-a8ad-85734d572902",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'adjective': 'funny',\n",
-       " 'text': 'Why did the scarecrow win an award? Because he was outstanding in his field!'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "outer_chain = RunnablePassthrough().assign(text=chain)\n",
-    "\n",
-    "outer_chain.invoke({\"adjective\": \"funny\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2717810",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "See [this tutorial](/docs/tutorials/llm_chain) for more detail on building with prompt templates, LLMs, and output parsers.\n",
-    "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
-   ]
-  }
- ],
- "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
-}

docs/docs/versions/migrating_chains/retrieval_qa.ipynb

@@ -1,261 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "eddcd5c1-cbe9-4a7d-8903-7d1ab29f9094",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "title: Migrating from RetrievalQA\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2d37868-dd01-4814-a76a-256f36cf66f7",
-   "metadata": {},
-   "source": [
-    "The [`RetrievalQA`](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html) chain performed natural-language question answering over a data source using retrieval-augmented generation.\n",
-    "\n",
-    "Some advantages of switching to the LCEL implementation are:\n",
-    "\n",
-    "- Easier customizability. Details such as the prompt and how documents are formatted are only configurable via specific parameters in the `RetrievalQA` chain.\n",
-    "- More easily return source documents.\n",
-    "- Support for runnable methods like streaming and async operations.\n",
-    "\n",
-    "Now let's look at them side-by-side. We'll use the same ingestion code to load a [blog post by Lilian Weng](https://lilianweng.github.io/posts/2023-06-23-agent/) on autonomous agents into a local vector store:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b99b47ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain-community langchain langchain-openai faiss-cpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "717c8673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1efbe16e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load docs\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai.chat_models import ChatOpenAI\n",
-    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
-    "\n",
-    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
-    "data = loader.load()\n",
-    "\n",
-    "# Split\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
-    "all_splits = text_splitter.split_documents(data)\n",
-    "\n",
-    "# Store splits\n",
-    "vectorstore = FAISS.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())\n",
-    "\n",
-    "# LLM\n",
-    "llm = ChatOpenAI()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7e16438",
-   "metadata": {},
-   "source": [
-    "import { ColumnContainer, Column } from \"@theme/Columns\";\n",
-    "\n",
-    "<ColumnContainer>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### Legacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2d0ddc98-75e5-4c1c-a1b5-7ef612516dc9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'query': 'What are autonomous agents?',\n",
-       " 'result': 'Autonomous agents are LLM-empowered agents capable of handling autonomous design, planning, and performance of complex scientific experiments. These agents can browse the Internet, read documentation, execute code, call robotics experimentation APIs, and leverage other LLMs. They can generate reasoning steps, such as developing a novel anticancer drug, based on requested tasks.'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain.chains import RetrievalQA\n",
-    "\n",
-    "# See full prompt at https://smith.langchain.com/hub/rlm/rag-prompt\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "qa_chain = RetrievalQA.from_llm(\n",
-    "    llm, retriever=vectorstore.as_retriever(), prompt=prompt\n",
-    ")\n",
-    "\n",
-    "qa_chain(\"What are autonomous agents?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "081948e5",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "\n",
-    "<Column>\n",
-    "\n",
-    "#### LCEL\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "91ae87cc-7b2f-4d0e-a6ae-a7a4c8c5ba41",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Autonomous agents are agents empowered by large language models (LLMs) that can handle autonomous design, planning, and performance of complex tasks such as scientific experiments. These agents can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs, and leverage other LLMs for their tasks. The model can come up with reasoning steps when given a specific task, such as developing a novel anticancer drug.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "# See full prompt at https://smith.langchain.com/hub/rlm/rag-prompt\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "\n",
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "\n",
-    "qa_chain = (\n",
-    "    {\n",
-    "        \"context\": vectorstore.as_retriever() | format_docs,\n",
-    "        \"question\": RunnablePassthrough(),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "qa_chain.invoke(\"What are autonomous agents?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6f44fe8",
-   "metadata": {},
-   "source": [
-    "</Column>\n",
-    "</ColumnContainer>\n",
-    "\n",
-    "The LCEL implementation exposes the internals of what's happening around retrieving, formatting documents, and passing them through a prompt to the LLM, but it is more verbose. You can customize and wrap this composition logic in a helper function, or use the higher-level [`create_retrieval_chain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html) and [`create_stuff_documents_chain`](https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) helper method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c448a74c-1f0a-445b-b629-51bc151ab620",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'What are autonomous agents?',\n",
-       " 'context': [Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs and leverage other LLMs.\\nFor example, when requested to \"develop a novel anticancer drug\", the model came up with the following reasoning steps:'),\n",
-       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.'),\n",
-       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#'),\n",
-       "  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Or\\n@article{weng2023agent,\\n  title   = \"LLM-powered Autonomous Agents\",\\n  author  = \"Weng, Lilian\",\\n  journal = \"lilianweng.github.io\",\\n  year    = \"2023\",\\n  month   = \"Jun\",\\n  url     = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\\n}\\nReferences#\\n[1] Wei et al. “Chain of thought prompting elicits reasoning in large language models.” NeurIPS 2022\\n[2] Yao et al. “Tree of Thoughts: Dliberate Problem Solving with Large Language Models.” arXiv preprint arXiv:2305.10601 (2023).')],\n",
-       " 'answer': 'Autonomous agents are entities capable of operating independently to perform tasks or make decisions without direct human intervention. In the context provided, autonomous agents empowered by Large Language Models (LLMs) are used for scientific discovery, including tasks like autonomous design, planning, and executing complex scientific experiments.'}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain import hub\n",
-    "from langchain.chains import create_retrieval_chain\n",
-    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "\n",
-    "# See full prompt at https://smith.langchain.com/hub/langchain-ai/retrieval-qa-chat\n",
-    "retrieval_qa_chat_prompt = hub.pull(\"langchain-ai/retrieval-qa-chat\")\n",
-    "\n",
-    "combine_docs_chain = create_stuff_documents_chain(llm, retrieval_qa_chat_prompt)\n",
-    "rag_chain = create_retrieval_chain(vectorstore.as_retriever(), combine_docs_chain)\n",
-    "\n",
-    "rag_chain.invoke({\"input\": \"What are autonomous agents?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2717810",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
-   ]
-  }
- ],
- "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
-}

docs/docs/versions/v0_2/deprecations.mdx

@@ -568,7 +568,7 @@ Removal: 0.3.0
 
 Alternative: [RunnableSequence](/docs/how_to/sequence/), e.g., `prompt | llm`
 
-This [migration guide](/docs/versions/migrating_chains/llm_chain) has a side-by-side comparison.
+This [migration guide](/docs/how_to/migrate_chains/#llmchain) has a side-by-side comparison.
 
 
 #### LLMSingleActionAgent
@@ -756,7 +756,7 @@ Removal: 0.3.0
 
 
 Alternative: [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain-chains-retrieval-create-retrieval-chain)
-This [migration guide](/docs/versions/migrating_chains/retrieval_qa) has a side-by-side comparison.
+This [migration guide](/docs/how_to/migrate_chains/#retrievalqa) has a side-by-side comparison.
 
 
 #### load_agent_from_config
@@ -823,7 +823,7 @@ Removal: 0.3.0
 
 
 Alternative: [create_history_aware_retriever](https://api.python.langchain.com/en/latest/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) together with [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain-chains-retrieval-create-retrieval-chain) (see example in docstring)
-This [migration guide](/docs/versions/migrating_chains/conversation_retrieval_chain) has a side-by-side comparison.
+This [migration guide](/docs/how_to/migrate_chains/#conversationalretrievalchain) has a side-by-side comparison.
 
 
 #### create_extraction_chain_pydantic

docs/docs/versions/v0_2/index.mdx

@@ -11,7 +11,7 @@ LangChain v0.2 was released in May 2024. This release includes a number of [brea
 :::note Reference
 
 - [Breaking Changes & Deprecations](/docs/versions/v0_2/deprecations)
-- [Migrating legacy chains to LCEL](/docs/versions/migrating_chains)
+- [Migrating legacy chains to LCEL](/docs/how_to/migrate_chains/)
 - [Migrating to Astream Events v2](/docs/versions/v0_2/migrating_astream_events)
 
 :::

docs/docs/versions/v0_2/migrating_astream_events.mdx

@@ -5,7 +5,13 @@ sidebar_label: astream_events v2
 
 # Migrating to Astream Events v2
 
-We've added a `v2` of the astream_events API with the release of `0.2.x`. You can see this [PR](https://github.com/langchain-ai/langchain/pull/21638) for more details.
+:::danger
+
+This migration guide is a work in progress and is not complete. Please wait to migrate astream_events.
+
+:::
+
+We've added a `v2` of the astream_events API with the release of `0.2.0`. You can see this [PR](https://github.com/langchain-ai/langchain/pull/21638) for more details.
 
 The `v2` version is a re-write of the `v1` version, and should be more efficient, with more consistent output for the events. The `v1` version of the API will be deprecated in favor of the `v2` version and will be removed in  `0.4.0`.
 

docs/package.json

@@ -26,11 +26,7 @@
     "@docusaurus/preset-classic": "2.4.3",
     "@docusaurus/remark-plugin-npm2yarn": "^2.4.3",
     "@docusaurus/theme-mermaid": "2.4.3",
-    "@emotion/react": "^11.11.0",
-    "@emotion/styled": "^11.11.0",
     "@mdx-js/react": "^1.6.22",
-    "@mui/icons-material": "^5.11.16",
-    "@mui/joy": "^5.0.0-alpha.81",
     "@supabase/supabase-js": "^2.39.7",
     "clsx": "^1.2.1",
     "cookie": "^0.6.0",

docs/scripts/document_loader_feat_table.py

@@ -15,14 +15,6 @@ hide_table_of_contents: true
 
 # Document loaders
 
-:::info
-
-If you'd like to write your own document loader, see [this how-to](/docs/how_to/document_loader_custom/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
-
-:::
-
-
 ## Features
 
 The following table shows the feature support for all document loaders.

docs/scripts/model_feat_table.py

@@ -87,14 +87,6 @@ CHAT_MODEL_FEAT_TABLE = {
         "package": "langchain-huggingface",
         "link": "/docs/integrations/chat/huggingface/",
     },
-    "ChatNVIDIA": {
-        "tool_calling": True,
-        "json_mode": False,
-        "local": True,
-        "multimodal": False,
-        "package": "langchain-nvidia-ai-endpoints",
-        "link": "/docs/integrations/chat/nvidia_ai_endpoints/",
-    },
     "ChatOllama": {
         "tool_calling": True,
         "local": True,
@@ -107,27 +99,17 @@ CHAT_MODEL_FEAT_TABLE = {
         "package": "langchain-openai",
         "link": "/docs/integrations/chat/vllm/",
     },
+    "ChatEdenAI": {
+        "tool_calling": True,
+        "package": "langchain-community",
+        "link": "/docs/integrations/chat/edenai/",
+    },
     "ChatLlamaCpp": {
         "tool_calling": True,
         "local": True,
         "package": "langchain-community",
         "link": "/docs/integrations/chat/llamacpp",
     },
-    "ChatAI21": {
-        "tool_calling": True,
-        "package": "langchain-ai21",
-        "link": "/docs/integrations/chat/ai21",
-    },
-    "ChatWatsonx": {
-        "tool_calling": True,
-        "package": "langchain-ibm",
-        "link": "/docs/integrations/chat/ibm_watsonx",
-    },
-    "ChatUpstage": {
-        "tool_calling": True,
-        "package": "langchain-upstage",
-        "link": "/docs/integrations/chat/upstage",
-    },
 }
 
 for feats in CHAT_MODEL_FEAT_TABLE.values():
@@ -144,13 +126,6 @@ custom_edit_url:
 
 # LLMs
 
-:::info
-
-If you'd like to write your own LLM, see [this how-to](/docs/how_to/custom_llm/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
-
-:::
-
 ## Features (natively supported)
 All LLMs implement the Runnable interface, which comes with default implementations of all methods, ie. `ainvoke`, `batch`, `abatch`, `stream`, `astream`. This gives all LLMs basic support for async, streaming and batch, which by default is implemented as below:
 - *Async* support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the LLM is being executed, by moving this call to a background thread.
@@ -176,13 +151,6 @@ hide_table_of_contents: true
 
 ## Advanced features
 
-:::info
-
-If you'd like to write your own chat model, see [this how-to](/docs/how_to/custom_chat_model/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
-
-:::
-
 The following table shows all the chat model classes that support one or more advanced features.
 
 :::info
@@ -279,11 +247,7 @@ def get_chat_model_table() -> str:
         for h in header[1:]:
             value = feats.get(h)
             if h == "package":
-                value = value or "langchain-community"
-                name = value[len("langchain-") :]
-                link = f"https://api.python.langchain.com/en/latest/{name}_api_reference.html"
-                value = f"[{value}]({link})"
-                row.append(value)
+                row.append(value or "langchain-community")
             else:
                 if value == "partial":
                     row.append("🟡")

docs/scripts/partner_pkg_table.py

@@ -1,146 +0,0 @@
-import glob
-import sys
-from pathlib import Path
-
-PARTNER_DIR = Path(__file__).parents[2] / "libs" / "partners"
-DOCS_DIR = Path(__file__).parents[1]
-
-PLATFORMS = {
-    path.split("/")[-1][:-4]
-    for path in glob.glob(
-        str(DOCS_DIR) + "/docs/integrations/platforms/*.mdx", recursive=True
-    )
-}
-EXTERNAL_PACKAGES = {
-    "astradb",
-    "aws",
-    "cohere",
-    "elasticsearch",
-    "google-community",
-    "google-genai",
-    "google-vertexai",
-    "nvidia-ai-endpoints",
-    "postgres",
-    "redis",
-    "weaviate",
-    "upstage",
-}
-
-JS_PACKAGES = {
-    "google-gauth",
-    "openai",
-    "anthropic",
-    "google-genai",
-    "pinecone",
-    "aws",
-    "google-vertexai",
-    "qdrant",
-    "azure-dynamic-sessions",
-    "google-vertexai-web",
-    "redis",
-    "azure-openai",
-    "google-webauth",
-    "baidu-qianfan",
-    "groq",
-    "standard-tests",
-    "cloudflare",
-    "mistralai",
-    "textsplitters",
-    "cohere",
-    "mixedbread-ai",
-    "weaviate",
-    "mongodb",
-    "yandex",
-    "exa",
-    "nomic",
-    "google-common",
-    "ollama",
-}
-
-
-IN_REPO_PACKAGES = {
-    path.split("/")[-2]
-    for path in glob.glob(str(PARTNER_DIR) + "/**/pyproject.toml", recursive=True)
-}
-ALL_PACKAGES = IN_REPO_PACKAGES.union(EXTERNAL_PACKAGES)
-
-CUSTOM_NAME = {
-    "google-genai": "Google Generative AI",
-    "aws": "AWS",
-    "airbyte": "Airbyte",
-}
-CUSTOM_PROVIDER_PAGES = {
-    "azure-dynamic-sessions": "/docs/integrations/platforms/microsoft/",
-    "google-community": "/docs/integrations/platforms/google/",
-    "google-genai": "/docs/integrations/platforms/google/",
-    "google-vertexai": "/docs/integrations/platforms/google/",
-    "nvidia-ai-endpoints": "/docs/integrations/providers/nvidia/",
-    "exa": "/docs/integrations/providers/exa_search/",
-    "mongodb": "/docs/integrations/providers/mongodb_atlas/",
-}
-PLATFORM_PAGES = {name: f"/docs/integrations/platforms/{name}/" for name in PLATFORMS}
-PROVIDER_PAGES = {
-    name: f"/docs/integrations/providers/{name}/"
-    for name in ALL_PACKAGES
-    if glob.glob(str(DOCS_DIR / f"docs/integrations/providers/{name}.*"))
-}
-PROVIDER_PAGES = {
-    **PROVIDER_PAGES,
-    **PLATFORM_PAGES,
-    **CUSTOM_PROVIDER_PAGES,
-}
-print(PROVIDER_PAGES)
-
-
-def package_row(name: str) -> str:
-    js = "✅" if name in JS_PACKAGES else "❌"
-    link = PROVIDER_PAGES.get(name)
-    title = CUSTOM_NAME.get(name) or name.title().replace("-", " ").replace(
-        "db", "DB"
-    ).replace("Db", "DB").replace("ai", "AI").replace("Ai", "AI")
-    provider = f"[{title}]({link})" if link else title
-    return f"| {provider} | [langchain-{name}](https://api.python.langchain.com/en/latest/{name.replace('-', '_')}_api_reference.html) | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-{name}?style=flat-square&label=%20&color=blue) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-{name}?style=flat-square&label=%20&color=orange) | {js} |"
-
-
-def table() -> str:
-    header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/v0.2/docs/integrations/platforms/) |
-| :--- | :---: | :---: | :---: | :---: |
-"""
-    return header + "\n".join(package_row(name) for name in sorted(ALL_PACKAGES))
-
-
-def doc() -> str:
-    return f"""\
----
-sidebar_position: 0
-sidebar_class_name: hidden
----
-
-# Providers
-
-:::info
-
-If you'd like to write your own integration, see [Extending LangChain](/docs/how_to/#custom).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
-
-:::
-
-LangChain integrates with many providers.
-
-## Integration Packages
-
-These providers have standalone `langchain-{{provider}}` packages for improved versioning, dependency management and testing.
-
-{table()}
-
-## All Providers
-
-Click [here](/docs/integrations/providers/) to see all providers.
-
-"""
-
-
-if __name__ == "__main__":
-    output_dir = Path(sys.argv[1]) / "integrations" / "platforms"
-    with open(output_dir / "index.mdx", "w") as f:
-        f.write(doc())

docs/scripts/tool_feat_table.py

@@ -101,13 +101,6 @@ hide_table_of_contents: true
 
 # Tools
 
-:::info
-
-If you'd like to write your own tool, see [this how-to](/docs/how_to/custom_tools/).
-If you'd like to contribute an integration, see [Contributing integrations](/docs/contributing/integrations/).
-
-:::
-
 ## Search Tools
 
 The following table shows tools that execute online searches in some shape or form:

docs/sidebars.js

@@ -75,11 +75,6 @@ module.exports = {
         "versions/overview",
         "versions/release_policy",
         "versions/packages",
-        {
-          type: 'doc',
-          id: "how_to/pydantic_compatibility",
-          label: "Pydantic",
-        },
         {
           type: "category",
           label: "v0.2",
@@ -92,18 +87,6 @@ module.exports = {
             className: 'hidden',
           }],
         },
-        {
-          type: "category",
-          label: "Migrating to LCEL",
-          link: {type: 'doc', id: 'versions/migrating_chains/index'},
-          collapsible: false,
-          collapsed: false,
-          items: [{
-            type: 'autogenerated',
-            dirName: 'versions/migrating_chains',
-            className: 'hidden',
-          }],
-        },
       ],
     },
     "security"
@@ -281,20 +264,19 @@ module.exports = {
         },
         {
           type: "category",
-          label: "Key-value stores",
+          label: "Memory",
           collapsed: true,
           items: [
             {
               type: "autogenerated",
-              dirName: "integrations/stores",
+              dirName: "integrations/memory",
             },
           ],
           link: {
-            type: "doc",
-            id: "integrations/stores/index",
+            type: "generated-index",
+            slug: "integrations/memory",
           },
         },
-        "integrations/llm_caching",
         {
           type: "category",
           label: "Graphs",
@@ -310,21 +292,6 @@ module.exports = {
             slug: "integrations/graphs",
           },
         },
-        {
-          type: "category",
-          label: "Memory",
-          collapsed: true,
-          items: [
-            {
-              type: "autogenerated",
-              dirName: "integrations/memory",
-            },
-          ],
-          link: {
-            type: "generated-index",
-            slug: "integrations/memory",
-          },
-        },
         {
           type: "category",
           label: "Callbacks",
@@ -370,6 +337,22 @@ module.exports = {
             slug: "integrations/adapters",
           },
         },
+        {
+          type: "category",
+          label: "Stores",
+          collapsed: true,
+          items: [
+            {
+              type: "autogenerated",
+              dirName: "integrations/stores",
+            },
+          ],
+          link: {
+            type: "doc",
+            id: "integrations/stores/index",
+          },
+        },
+        "integrations/llm_caching",
       ],
       link: {
         type: "generated-index",

docs/src/css/custom.css

@@ -12,7 +12,7 @@
  * bundles Infima by default. Infima is a CSS framework designed to
  * work well for content-centric websites.
  */
-
+ 
  @font-face {
   font-family: 'Manrope';
   src: url('/fonts/Manrope-VariableFont_wght.ttf') format('truetype');
@@ -25,49 +25,34 @@
 
 /* You can override the default Infima variables here. */
 :root {
-  --ifm-color-primary: var(--joy-palette-primary-600);
-  --ifm-color-primary-dark: var(--joy-palette-primary-600);
-  --ifm-color-primary-darker: var(--joy-palette-primary-700);
-  --ifm-color-primary-darkest: var(--joy-palette-primary-800);
-  --ifm-color-primary-light: var(--joy-palette-primary-400);
-  --ifm-color-primary-lighter: var(--joy-palette-primary-200);
-  --ifm-color-primary-lightest: var(--joy-palette-primary-100);
+  --ifm-color-primary: #2e8555;
+  --ifm-color-primary-dark: #29784c;
+  --ifm-color-primary-darker: #277148;
+  --ifm-color-primary-darkest: #205d3b;
+  --ifm-color-primary-light: #33925d;
+  --ifm-color-primary-lighter: #359962;
+  --ifm-color-primary-lightest: #3cad6e;
+  --ifm-font-weight-bold: 600;
   --ifm-code-font-size: 95%;
-  --ifm-font-family-base: "Public Sans", sans-serif;
-  --docusaurus-highlighted-code-line-bg: var(--joy-palette-primary-100);
+  --ifm-font-family-base: 'Public Sans';
+  --ifm-menu-link-padding-horizontal: 0.5rem;
+  --ifm-menu-link-padding-vertical: 0.5rem;
+  --doc-sidebar-width: 275px !important;
 }
 
 /* For readability concerns, you should choose a lighter palette in dark mode. */
 [data-theme='dark'] {
-  --ifm-color-primary: var(--joy-palette-primary-400);
-  --ifm-color-primary-dark: var(--joy-palette-primary-500);
-  --ifm-color-primary-darker: var(--joy-palette-primary-600);
-  --ifm-color-primary-darkest: var(--joy-palette-primary-700);
-  --ifm-color-primary-light: var(--joy-palette-primary-300);
-  --ifm-color-primary-lighter: var(--joy-palette-primary-100);
-  --ifm-color-primary-lightest: var(--joy-palette-primary-50);
-  --docusaurus-highlighted-code-line-bg: var(--joy-palette-primary-800);
+  --ifm-color-primary: #25c2a0;
+  --ifm-color-primary-dark: #21af90;
+  --ifm-color-primary-darker: #1fa588;
+  --ifm-color-primary-darkest: #1a8870;
+  --ifm-color-primary-light: #29d5b0;
+  --ifm-color-primary-lighter: #32d8b4;
+  --ifm-color-primary-lightest: #4fddbf;
 }
 
-nav,
-h1,
-h2,
-h3,
-h4 {
-  font-family: "Manrope";
-}
-
-html[data-theme="dark"] {
-  --ifm-background-color: var(--joy-palette-background-body);
-}
-
-.navbar {
-  outline: 1px solid var(--ifm-color-gray-200);
-}
-
-[data-theme="dark"] .navbar {
-  background-color: var(--joy-palette-common-black);
-  outline: 1px solid var(--ifm-color-gray-800);
+nav, h1, h2, h3, h4 {
+  font-family: 'Manrope';
 }
 
 .footer__links {
@@ -117,13 +102,27 @@ html[data-theme="dark"] {
   font-size: 0.85rem;
 }
 
+/* .theme-code-block.language-python::before {
+  content: "";
+  padding: 2px 12px;
+  background-color: var(--ifm-color-primary-light);
+  color: #ffffff;
+  font-weight: bold;
+  border-top-right-radius: 4px;
+  border-top-left-radius: 4px;
+  display: block;
+  margin-bottom: 12px;
+  font-size: 0.6em;
+  width: 100%;
+  box-sizing: border-box;
+  position: relative;
+} */
+
 .theme-code-block.language-python,
 .theme-code-block.language-javascript,
 .theme-code-block.language-js,
 .theme-code-block.language-typescript,
-.theme-code-block.language-ts,
-.theme-code-block.language-shell,
-.theme-code-block.language-bash {
+.theme-code-block.language-ts {
   position: relative; /* Ensure this is set so the ::before pseudo-element is positioned relative to this element */
   padding-left: 4px;
   border: 1px solid var(--ifm-color-primary-darkest);
@@ -133,9 +132,7 @@ html[data-theme="dark"] {
 .theme-code-block.language-javascript::before,
 .theme-code-block.language-js::before,
 .theme-code-block.language-typescript::before,
-.theme-code-block.language-ts::before,
-.theme-code-block.language-shell::before,
-.theme-code-block.language-bash::before {
+.theme-code-block.language-ts::before {
   content: "";
   position: absolute;
   top: 0;
@@ -148,45 +145,10 @@ html[data-theme="dark"] {
   z-index: 1;
 }
 
-.menu__list {
-  display: flex !important;
-  flex-direction: column;
-  gap: 0.125rem;
-
-  position: relative;
-}
-
-.menu__list:not(.theme-doc-sidebar-menu) {
-  padding-left: 1.125rem !important;
-}
-
-.menu__list::before {
-  content: "";
-  width: 1px;
-  left: 0.75rem;
-  top: 0.125rem;
-  bottom: 0.125rem;
-
-  position: absolute;
-
-  background-color: var(--joy-palette-background-level2);
-}
-
-.theme-doc-sidebar-menu.menu__list::before {
-  display: none;
-}
-
-.menu__link {
-  padding-left: 0.5rem;
-}
-
 .theme-doc-sidebar-menu > .theme-doc-sidebar-item-category:not(:first-of-type),
 .theme-doc-sidebar-menu > .theme-doc-sidebar-item-link,
-.theme-doc-sidebar-menu
-  > .theme-doc-sidebar-item-link.theme-doc-sidebar-item-link-level-1:not(
-    :first-of-type
-  ) {
-  margin-top: 0.125rem;
+.theme-doc-sidebar-menu > .theme-doc-sidebar-item-link.theme-doc-sidebar-item-link-level-1:not(:first-of-type) {
+  margin-top: 1rem;
 }
 
 .theme-doc-sidebar-menu .theme-doc-sidebar-item-link,
@@ -205,10 +167,9 @@ html[data-theme="dark"] {
   margin-top: 0;
 }
 
-.theme-doc-sidebar-item-category,
-.theme-doc-sidebar-item-link {
-  font-size: 0.9rem !important;
-  font-weight: 500;
+.theme-doc-sidebar-item-category, .theme-doc-sidebar-menu > .theme-doc-sidebar-item-link {
+  font-size: 1rem;
+  font-weight: 700;
 }
 
 .theme-doc-sidebar-item-category button:before {
@@ -216,9 +177,8 @@ html[data-theme="dark"] {
   width: 1.25rem;
 }
 
-.theme-doc-sidebar-item-link,
-.theme-doc-sidebar-item-category .theme-doc-sidebar-item-category {
-  font-size: 0.9rem;
+.theme-doc-sidebar-item-link, .theme-doc-sidebar-item-category .theme-doc-sidebar-item-category {
+  font-size: .9rem;
   font-weight: 500;
 }
 
@@ -231,30 +191,29 @@ html[data-theme="dark"] {
 .theme-doc-sidebar-item-category > div > button {
   opacity: 0.5;
 }
+
 /* Hack for "More" style caret buttons */
 .theme-doc-sidebar-item-category > div > a::after {
   opacity: 0.5;
 }
 
-.markdown > h1 {
-  margin-top: 1rem;
-  margin-bottom: 1rem !important;
-  font-size: 2.5rem !important;
+.markdown {
+  line-height: 2em;
 }
 
 .markdown > h2 {
-  margin-top: 2.5rem;
+  margin-top: 2rem;
   border-bottom-color: var(--ifm-color-primary);
   border-bottom-width: 2px;
-  font-size: 1.8rem !important;
+  padding-bottom: 1rem;
 }
 
-.markdown > :not(h2) + h3 {
-  margin-top: 2.5rem;
+.markdown > :not(h2) +  h3 {
+  margin-top: 1rem;
 }
 
 .markdown > h4 {
-  margin-bottom: 1rem;
+  margin-bottom: 0.2rem;
   font-weight: 600;
 }
 
@@ -271,25 +230,6 @@ html[data-theme="dark"] {
   display: none !important;
 }
 
-/* Config search */
-
-.card {
-  padding: 1.5rem !important;
-  box-shadow: none !important;
-}
-
-.tabs-container > .code-tabs + div {
-  margin-top: 0 !important;
-  box-shadow: none !important;
-}
-
-.tabs-container > .code-tabs {
-  border: 1px solid var(--joy-palette-divider);
-  border-bottom: none;
-  border-top-left-radius: var(--ifm-code-border-radius);
-  border-top-right-radius: var(--ifm-code-border-radius);
-}
-
 .header-github-link:hover {
   opacity: 0.6;
 }
@@ -308,71 +248,6 @@ html[data-theme="dark"] {
     no-repeat;
 }
 
-/* media dark mode */
-@media (prefers-color-scheme: dark) {
-  .tabs-container > .code-tabs {
-    background: #1e1e1e;
-  }
-  
-  .tabs-container .code-caption {
-    background: #1e1e1e;
-  }
-}
-
-.tabs-container > .code-tabs > [role="tab"] {
-  padding: 0.5rem 1rem !important;
-  border-radius: 0;
-}
-
-.tabs-container > .code-tabs + div .theme-code-block {
-  box-shadow: none !important;
-  border-top-left-radius: 0px !important;
-  border-top-right-radius: 0px !important;
-}
-
-.tabs-container > .code-tabs + div .code-caption {
-  border: 1px solid var(--joy-palette-divider);
-  border-bottom: none;
-  padding: 1rem;
-}
-
-.tabs-container > .code-tabs + div .code-caption p:last-child,
-.tabs-container > .code-tabs + div .code-footnote p:last-child {
-  margin-bottom: 0;
-}
-
-.tabs-container:has(> .code-tabs) {
-  box-shadow: var(--ifm-global-shadow-lw);
-  border-radius: var(--ifm-code-border-radius);
-}
-
-.menu__list-item > .menu__list {
-  transition: all 0.2s ease-in-out !important;
-}
-
-.menu__list-item:not(.menu__list-item--collapsed) > .menu__list {
-  opacity: 1;
-  transform: scale(1);
-}
-
-.menu__list-item.menu__list-item--collapsed > .menu__list {
-  opacity: 0;
-  transform: scale(0.95);
-}
-
-/* prefers reduced motion */
-@media (prefers-reduced-motion: reduce) {
-  .menu__list-item > .menu__list {
-    transition: none !important;
-    opacity: 1 !important;
-  }
-}
-
-hr {
-  background-color: var(--joy-palette-background-level2) !important;
-}
-
-
 div[class^=announcementBar_] {
   height:40px !important;
   font-size: 20px !important;

docs/src/hooks/useColorScheme.js

@@ -1,14 +0,0 @@
-import { useColorScheme as useColorSchemeMui } from "@mui/joy/styles";
-
-// Same theme logic as in smith-frontend
-export function useColorScheme() {
-  const { systemMode, setMode } = useColorSchemeMui();
-  const isDarkMode = systemMode === "dark";
-
-  return {
-    mode: systemMode,
-    isDarkMode,
-    isLightMode: !isDarkMode,
-    setMode,
-  };
-}

docs/src/theme/ChatModelTabs.js

@@ -14,7 +14,6 @@ import CodeBlock from "@theme-original/CodeBlock";
  * @property {string} [mistralParams] - Parameters for Mistral chat model. Defaults to `model="mistral-large-latest"`
  * @property {string} [googleParams] - Parameters for Google chat model. Defaults to `model="gemini-pro"`
  * @property {string} [togetherParams] - Parameters for Together chat model. Defaults to `model="mistralai/Mixtral-8x7B-Instruct-v0.1"`
- * @property {string} [nvidiaParams] - Parameters for Nvidia NIM model. Defaults to `model="meta/llama3-70b-instruct"`
  * @property {boolean} [hideOpenai] - Whether or not to hide OpenAI chat model.
  * @property {boolean} [hideAnthropic] - Whether or not to hide Anthropic chat model.
  * @property {boolean} [hideCohere] - Whether or not to hide Cohere chat model.
@@ -24,7 +23,6 @@ import CodeBlock from "@theme-original/CodeBlock";
  * @property {boolean} [hideGoogle] - Whether or not to hide Google VertexAI chat model.
  * @property {boolean} [hideTogether] - Whether or not to hide Together chat model.
  * @property {boolean} [hideAzure] - Whether or not to hide Microsoft Azure OpenAI chat model.
- * @property {boolean} [hideNvidia] - Whether or not to hide NVIDIA NIM model.
  * @property {string} [customVarName] - Custom variable name for the model. Defaults to `model`.
  */
 
@@ -42,7 +40,6 @@ export default function ChatModelTabs(props) {
     googleParams,
     togetherParams,
     azureParams,
-    nvidiaParams,
     hideOpenai,
     hideAnthropic,
     hideCohere,
@@ -52,7 +49,6 @@ export default function ChatModelTabs(props) {
     hideGoogle,
     hideTogether,
     hideAzure,
-    hideNvidia,
     customVarName,
   } = props;
 
@@ -73,7 +69,6 @@ export default function ChatModelTabs(props) {
   const azureParamsOrDefault =
     azureParams ??
     `\n    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],\n    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],\n    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],\n`;
-  const nvidiaParamsOrDefault = nvidiaParams ?? `model="meta/llama3-70b-instruct"`
 
   const llmVarName = customVarName ?? "model";
 
@@ -123,15 +118,6 @@ export default function ChatModelTabs(props) {
       default: false,
       shouldHide: hideCohere,
     },
-    {
-      value: "NVIDIA",
-      label: "NVIDIA",
-      text: `from langchain import ChatNVIDIA\n\n${llmVarName} = ChatNVIDIA(${nvidiaParamsOrDefault})`,
-      apiKeyName: "NVIDIA_API_KEY",
-      packageName: "langchain-nvidia-ai-endpoints",
-      default: false,
-      shouldHide: hideNvidia,
-    },
     {
       value: "FireworksAI",
       label: "FireworksAI",

docs/src/theme/ColorModeToggle/index.js

@@ -1,18 +0,0 @@
-import React, { useEffect } from "react";
-import ColorModeToggle from "@theme-original/ColorModeToggle";
-import { useColorScheme } from "../../hooks/useColorScheme";
-
-export default function ColorModeToggleWrapper(props) {
-  const { setMode } = useColorScheme();
-
-  // "value" holds the color theme. Either "light" or "dark"
-  const { value } = props;
-
-  // change mode based on "value" prop
-  // "dark" or "light" are also used for MUI
-  useEffect(() => {
-    setMode(value);
-  }, [value]);
-
-  return <ColorModeToggle {...props} />;
-}

docs/src/theme/Root.js

@@ -1,18 +0,0 @@
-import React from "react";
-import { CssVarsProvider, getInitColorSchemeScript } from "@mui/joy/styles";
-import CssBaseline from "@mui/joy/CssBaseline";
-
-export default function Root({ children }) {
-  return (
-    <>
-      {getInitColorSchemeScript()}
-      <CssBaseline />
-      <CssVarsProvider
-        defaultMode="system"
-        modeStorageKey="langsmith-docs-joy-mode"
-      >
-        {children}
-      </CssVarsProvider>
-    </>
-  );
-}

docs/vercel.json

@@ -65,10 +65,6 @@
     {
       "source": "/docs/integrations/toolkits/document_comparison_toolkit(/?)",
       "destination": "/docs/tutorials/rag/"
-    },
-    {
-      "source": "/docs/how_to/migrate_chains(/?)",
-      "destination": "/docs/versions/migrating_chains"
     }
   ]
 }

libs/cli/langchain_cli/integration_template/docs/chat.ipynb

@@ -62,8 +62,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
+    "os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
    ]
   },
   {
@@ -81,8 +80,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
    ]
   },
   {
@@ -197,7 +196,7 @@
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
-    "prompt = ChatPromptTemplate(\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
     "        (\n",
     "            \"system\",\n",

libs/cli/langchain_cli/integration_template/docs/document_loaders.ipynb

@@ -201,24 +201,10 @@
   }
  ],
  "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"
+   "name": "python"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }

libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb

@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__ByteStore\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__ByteStore\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This will help you get started with __ModuleName__ [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all __ModuleName__ByteStore features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/stores/langchain_core.stores.__module_name__ByteStore.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/v0.2/docs/integrations/stores/in_memory/ for an example.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "- TODO: (Optional) A short introduciton to the underlying technology/API.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/_package_name_) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [__ModuleName__ByteStore](https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "To create a __ModuleName__ byte store, you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info, or omit if the service does not require any credentials.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our byte store:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__ByteStore\n",
-    "\n",
-    "kv_store = __ModuleName__ByteStore(\n",
-    "    # params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "- TODO: Run cells so output can be seen.\n",
-    "\n",
-    "You can set data under keys like this using the `mset` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kv_store.mset(\n",
-    "    [\n",
-    "        [\"key1\", b\"value1\"],\n",
-    "        [\"key2\", b\"value2\"],\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And you can delete data using the `mdelete` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kv_store.mdelete(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "kv_store.mget(\n",
-    "    [\n",
-    "        \"key1\",\n",
-    "        \"key2\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this key-value store provider\n",
-    "\n",
-    "E.g. extra initialization. Delete if not relevant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__ByteStore features and configurations, head to the API reference: https://api.python.langchain.com/en/latest/stores/__module_name__.stores.__ModuleName__ByteStore.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

libs/cli/langchain_cli/integration_template/docs/llms.ipynb

@@ -17,75 +17,9 @@
    "source": [
     "# __ModuleName__LLM\n",
     "\n",
-    "- [ ] TODO: Make sure API reference link is correct\n",
+    "This example goes over how to use LangChain to interact with `__ModuleName__` models.\n",
     "\n",
-    "This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- [ ] TODO: Update with relevant info.\n",
-    "\n",
-    "To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc51e756",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b6e1ca6",
-   "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": "196c2b41",
-   "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": "809c6577",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
+    "## Installation"
    ]
   },
   {
@@ -95,38 +29,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a760037",
-   "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": null,
-   "id": "a0562a13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__LLM\n",
-    "\n",
-    "llm = __ModuleName__LLM(\n",
-    "    model=\"model-name\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
+    "# install package\n",
+    "!pip install -U __package_name__"
    ]
   },
   {
@@ -134,9 +38,13 @@
    "id": "0ee90032",
    "metadata": {},
    "source": [
-    "## Invocation\n",
+    "## Environment Setup\n",
     "\n",
-    "- [ ] TODO: Run cells so output can be seen."
+    "Make sure to set the following environment variables:\n",
+    "\n",
+    "- TODO: fill out relevant environment variables or secrets\n",
+    "\n",
+    "## Usage"
    ]
   },
   {
@@ -147,65 +55,21 @@
     "tags": []
    },
    "outputs": [],
-   "source": [
-    "input_text = \"__ModuleName__ is an AI company that \"\n",
-    "\n",
-    "completion = llm.invoke(input_text)\n",
-    "completion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "add38532",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
-    "\n",
-    "- TODO: Run cells so output can be seen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "078e9db2",
-   "metadata": {},
-   "outputs": [],
    "source": [
     "from langchain_core.prompts import PromptTemplate\n",
+    "from __module_name__.llms import __ModuleName__LLM\n",
     "\n",
-    "prompt = PromptTemplate(\n",
-    "    \"How to say {input} in {output_language}:\\n\"\n",
-    ")\n",
+    "template = \"\"\"Question: {question}\n",
     "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e99eef30",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this model provider\n",
+    "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "E.g. creating/using finetuned models via this provider. Delete if not relevant"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9bdfcef",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
+    "prompt = PromptTemplate.from_string(template)\n",
     "\n",
-    "For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
+    "model = __ModuleName__LLM()\n",
+    "\n",
+    "chain = prompt | model\n",
+    "\n",
+    "chain.invoke({\"question\": \"What is LangChain?\"})"
    ]
   }
  ],

libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb

@@ -12,217 +12,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a3d6f34",
+   "id": "e49f1e0d",
    "metadata": {},
    "source": [
     "# __ModuleName__Embeddings\n",
     "\n",
-    "- [ ] TODO: Make sure API reference link is correct\n",
+    "This notebook covers how to get started with __ModuleName__ embedding models.\n",
     "\n",
-    "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/text_embedding/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [__ModuleName__Embeddings](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- [ ] TODO: Update with relevant info.\n",
-    "\n",
-    "To access __ModuleName__ embedding models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ 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": 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 __ModuleName__ integration lives in the `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64853226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "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": null,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__Embeddings\n",
-    "\n",
-    "embeddings = __ModuleName__Embeddings(\n",
-    "    model=\"model-name\",\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 under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\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_document = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_document.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_queries([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": [
-    "### Async Usage\n",
-    "\n",
-    "You can also use `aembed_query` and `aembed_documents` for producing embeddings asynchronously:\n"
+    "## Installation"
    ]
   },
   {
@@ -232,22 +29,83 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import asyncio\n",
+    "# install package\n",
+    "!pip install -U __package_name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Environment Setup\n",
     "\n",
-    "async def async_example():\n",
-    "    single_vector = await embeddings.embed_query(text)\n",
-    "    print(str(single_vector)[:100]) # Show the first 100 characters of the vector\n",
+    "Make sure to set the following environment variables:\n",
     "\n",
-    "asyncio.run(async_example())"
+    "- TODO: fill out relevant environment variables or secrets\n",
+    "\n",
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1bd4396",
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from __module_name__.embeddings import __ModuleName__Embeddings\n",
+    "\n",
+    "embeddings = __ModuleName__Embeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12fcfb4b",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "embeddings.embed_query(\"My query to look up\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f2e6104",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings.embed_documents(\n",
+    "    [\"This is a content of the document\", \"This is another document\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46739f68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# async embed query\n",
+    "await embeddings.aembed_query(\"My query to look up\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e48632ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# async embed documents\n",
+    "await embeddings.aembed_documents(\n",
+    "    [\"This is a content of the document\", \"This is another document\"]\n",
+    ")"
+   ]
   }
  ],
  "metadata": {

libs/cli/langchain_cli/integration_template/docs/toolkits.ipynb

@@ -1,201 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__Toolkit\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This will help you getting started with the __ModuleName__ [toolkit](/docs/concepts/#toolkits). For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/__module_name__.agent_toolkits.__ModuleName__.toolkit.__ModuleName__Toolkit.html).\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Update with relevant info."
-   ]
-  },
-  {
-   "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 `__package_name__` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU __package_name__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our toolkit:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __module_name__ import __ModuleName__Toolkit\n",
-    "\n",
-    "toolkit = __ModuleName__Toolkit(\n",
-    "    # ...\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": "d11245ad-3661-4405-8558-1188896347ec",
-   "metadata": {},
-   "source": [
-    "TODO: list API reference pages for individual tools."
-   ]
-  },
-  {
-   "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 = \"...\"\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": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality or considerations specific to this toolkit\n",
-    "\n",
-    "Fill in or delete if not relevant."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/__module_name__.agent_toolkits.__ModuleName__.toolkit.__ModuleName__Toolkit.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
-}

libs/cli/langchain_cli/integration_template/docs/tools.ipynb

@@ -1,278 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "10238e62-3465-4973-9279-606cbb7ccf16",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: __ModuleName__\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6f91f20",
-   "metadata": {},
-   "source": [
-    "# __ModuleName__\n",
-    "\n",
-    "- TODO: Make sure API reference link is correct.\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with __ModuleName__ [tool](/docs/integrations/tools/). For detailed documentation of all __ModuleName__ features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- TODO: Make sure links and features are correct\n",
-    "\n",
-    "| Class | Package | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/tools/__module_name__) |  Package latest |\n",
-    "| :--- | :--- | :---: | :---: | :---: |\n",
-    "| [__ModuleName__](https://api.python.langchain.com/en/latest/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | beta/❌ | ✅/❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Tool features\n",
-    "\n",
-    "- TODO: Add feature table if it makes sense\n",
-    "\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "- TODO: Add any additional deps\n",
-    "\n",
-    "The integration lives in the `langchain-community` package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f85b4089",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --quiet -U langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b15e9266",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
-    "\n",
-    "- TODO: Add any credentials that are needed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "# if not os.environ.get(\"__MODULE_NAME___API_KEY\"):\n",
-    "#     os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"__MODULE_NAME__ API key:\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
-   "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": 3,
-   "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "- TODO: Fill in instantiation params\n",
-    "\n",
-    "Here we show how to instatiate an instance of the __ModuleName__ tool, with "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.tools import __ModuleName__\n",
-    "\n",
-    "\n",
-    "tool = __ModuleName__(\n",
-    "    ...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74147a1a",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "### [Invoke directly with args](/docs/concepts/#invoke-with-just-the-arguments)\n",
-    "\n",
-    "- TODO: Describe what the tool args are, fill them in, run cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool.invoke({...})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6e73897",
-   "metadata": {},
-   "source": [
-    "### [Invoke with ToolCall](/docs/concepts/#invoke-with-toolcall)\n",
-    "\n",
-    "We can also invoke the tool with a model-generated ToolCall, in which case a ToolMessage will be returned:\n",
-    "\n",
-    "- TODO: Fill in tool args and run cell"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f90e33a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
-    "model_generated_tool_call = {\n",
-    "    \"args\": {...},  # TODO: FILL IN\n",
-    "    \"id\": \"1\",\n",
-    "    \"name\": tool.name,\n",
-    "    \"type\": \"tool_call\",\n",
-    "}\n",
-    "tool.invoke(model_generated_tool_call)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "- TODO: Add user question and run cells\n",
-    "\n",
-    "We can use our tool in a chain by first binding it to a [tool-calling model](/docs/how_to/tool_calling/) and then calling it:\n",
-    "\n",
-    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
-    "\n",
-    "<ChatModelTabs customVarName=\"llm\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "af3123ad-7a02-40e5-b58e-7d56e23e5830",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "# !pip install -qU langchain langchain-openai\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "\n",
-    "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fdbf35b5-3aaf-4947-9ec6-48c21533fb95",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnableConfig, chain\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"human\", \"{user_input}\"),\n",
-    "        (\"placeholder\", \"{messages}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# specifying tool_choice will force the model to call this tool.\n",
-    "llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)\n",
-    "\n",
-    "llm_chain = prompt | llm_with_tools\n",
-    "\n",
-    "\n",
-    "@chain\n",
-    "def tool_chain(user_input: str, config: RunnableConfig):\n",
-    "    input_ = {\"user_input\": user_input}\n",
-    "    ai_msg = llm_chain.invoke(input_, config=config)\n",
-    "    tool_msgs = tool.batch(ai_msg.tool_calls, config=config)\n",
-    "    return llm_chain.invoke({**input_, \"messages\": [ai_msg, *tool_msgs]}, config=config)\n",
-    "\n",
-    "\n",
-    "tool_chain.invoke(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ac8146c",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__ features and configurations head to the API reference: https://api.python.langchain.com/en/latest/tools/langchain_community.tools.__module_name__.tool.__ModuleName__.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-311",
-   "language": "python",
-   "name": "poetry-venv-311"
-  },
-  "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
-}

libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb

@@ -17,7 +17,20 @@
    "source": [
     "# __ModuleName__VectorStore\n",
     "\n",
-    "This notebook covers how to get started with the __ModuleName__ vector store."
+    "This notebook covers how to get started with the __ModuleName__ vector store.\n",
+    "\n",
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d97b55c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install package\n",
+    "!pip install -U __package_name__"
    ]
   },
   {
@@ -25,80 +38,14 @@
    "id": "36fdc060",
    "metadata": {},
    "source": [
-    "## Setup\n",
+    "## Environment Setup\n",
     "\n",
-    "- TODO: Update with relevant info.\n",
-    "- TODO: Update minimum version to be correct.\n",
+    "Make sure to set the following environment variables:\n",
     "\n",
-    "To access __ModuleName__ vector stores you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "64e28aa6",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "%pip install -qU \"__package_name__>=MINIMUM_VERSION\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9695dee7",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
+    "- TODO: fill out relevant environment variables or secrets\n",
+    "- Op\n",
     "\n",
-    "- TODO: Update with relevant info.\n",
-    "\n",
-    "Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "894c30e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
-    "    import getpass\n",
-    "    os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ 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",
-   "id": "93df377e",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "- TODO: Fill out with relevant init params"
+    "## Usage"
    ]
   },
   {
@@ -112,224 +59,7 @@
    "source": [
     "from __module_name__.vectorstores import __ModuleName__VectorStore\n",
     "\n",
-    "vector_store = __ModuleName__VectorStore()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac6071d4",
-   "metadata": {},
-   "source": [
-    "## Manage vector store\n",
-    "\n",
-    "### Add items to vector store\n",
-    "\n",
-    "- TODO: Edit and then run code cell to generate output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17f5efc0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.documents import Document\n",
-    "\n",
-    "document_1 = Document(\n",
-    "    page_content=\"foo\",\n",
-    "    metadata={\"source\": \"https://example.com\"}\n",
-    ")\n",
-    "\n",
-    "document_2 = Document(\n",
-    "    page_content=\"bar\",\n",
-    "    metadata={\"source\": \"https://example.com\"}\n",
-    ")\n",
-    "\n",
-    "document_2 = Document(\n",
-    "    page_content=\"baz\",\n",
-    "    metadata={\"source\": \"https://example.com\"}\n",
-    ")\n",
-    "\n",
-    "documents = [document_1, document_2]\n",
-    "\n",
-    "vector_store.add_documents(documents=documents,ids=[\"1\",\"2\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c738c3e0",
-   "metadata": {},
-   "source": [
-    "### Update items in vector store\n",
-    "\n",
-    "- TODO: Edit and then run code cell to generate output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f0aa8b71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "updated_document = Document(\n",
-    "    page_content=\"qux\",\n",
-    "    metadata={\"source\": \"https://another-example.com\"}\n",
-    ")\n",
-    "\n",
-    "vector_store.update_documents(document_id=\"1\",document=updated_document)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcf1b905",
-   "metadata": {},
-   "source": [
-    "### Delete items from vector store\n",
-    "\n",
-    "- TODO: Edit and then run code cell to generate output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ef61e188",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vector_store.delete(ids=[\"3\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3620501",
-   "metadata": {},
-   "source": [
-    "## Query vector store\n",
-    "\n",
-    "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent. \n",
-    "\n",
-    "### Query directly\n",
-    "\n",
-    "Performing a simple similarity search can be done as follows:\n",
-    "\n",
-    "- TODO: Edit and then run code cell to generate output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa0a16fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = vector_store.similarity_search(query=\"thud\",k=1,filter={\"source\":\"https://example.com\"})\n",
-    "for doc in results:\n",
-    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ed9d733",
-   "metadata": {},
-   "source": [
-    "If you want to execute a similarity search and receive the corresponding scores you can run:\n",
-    "\n",
-    "- TODO: Edit and then run code cell to generate output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5efd2eaa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = vector_store.similarity_search_with_score(query=\"thud\",k=1,filter={\"source\":\"https://example.com\"})\n",
-    "for doc, score in results:\n",
-    "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c235cdc",
-   "metadata": {},
-   "source": [
-    "### Query by turning into retriever\n",
-    "\n",
-    "You can also transform the vector store into a retriever for easier usage in your chains. \n",
-    "\n",
-    "- TODO: Edit and then run code cell to generate output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f3460093",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = vector_store.as_retriever()\n",
-    "retriever.invoke(\"thud\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "901c75dc",
-   "metadata": {},
-   "source": [
-    "Using retriever in a simple RAG chain:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "619b5ef6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "from langchain import hub\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
-    "\n",
-    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "\n",
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "rag_chain = (\n",
-    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "rag_chain.invoke(\"thud\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "069f1b5f",
-   "metadata": {},
-   "source": [
-    "## TODO: Any functionality specific to this vector store\n",
-    "\n",
-    "E.g. creating a persisten database to save to your disk, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a27244f",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name__.vectorstores.__ModuleName__VectorStore.html"
+    "# TODO: switch for preferred way to init and use your vector store\n"
    ]
   }
  ],

libs/cli/langchain_cli/integration_template/integration_template/embeddings.py

@@ -4,70 +4,14 @@ from langchain_core.embeddings import Embeddings
 
 
 class __ModuleName__Embeddings(Embeddings):
-    """__ModuleName__ embedding model integration.
+    """__ModuleName__Embeddings embedding model.
 
-    # TODO: Replace with relevant packages, env vars.
-    Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
-
-    # TODO: Populate with relevant params.
-    Key init args — completion params:
-        model: str
-            Name of __ModuleName__ model to use.
-
-    See full list of supported init args and their descriptions in the params section.
-
-    # TODO: Replace with relevant init params.
-    Instantiate:
+    Example:
         .. code-block:: python
 
             from __module_name__ import __ModuleName__Embeddings
 
-            embed = __ModuleName__Embeddings(
-                model="...",
-                # api_key="...",
-                # other params...
-            )
-
-    Embed single text:
-        .. code-block:: python
-
-            input_text = "The meaning of life is 42"
-            embed.embed_query(input_text)
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
-    # TODO: Delete if token-level streaming isn't supported.
-    Embed multiple text:
-        .. code-block:: python
-
-             input_texts = ["Document 1...", "Document 2..."]
-            embed.embed_documents(input_texts)
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
-    # TODO: Delete if native async isn't supported.
-    Async:
-        .. code-block:: python
-
-            await embed.aembed_query(input_text)
-
-            # multiple:
-            # await embed.aembed_documents(input_texts)
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
+            model = __ModuleName__Embeddings()
     """
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:

libs/cli/langchain_cli/integration_template/integration_template/llms.py

@@ -2,111 +2,37 @@
 
 from typing import (
     Any,
+    AsyncIterator,
+    Iterator,
     List,
     Optional,
 )
 
 from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain_core.language_models import BaseLLM
-from langchain_core.outputs import LLMResult
+from langchain_core.outputs import GenerationChunk, LLMResult
 
 
 class __ModuleName__LLM(BaseLLM):
-    """__ModuleName__ completion model integration.
+    """__ModuleName__LLM large language models.
 
-    # TODO: Replace with relevant packages, env vars.
-    Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
-
-    # TODO: Populate with relevant params.
-    Key init args — completion params:
-        model: str
-            Name of __ModuleName__ model to use.
-        temperature: float
-            Sampling temperature.
-        max_tokens: Optional[int]
-            Max number of tokens to generate.
-
-    # TODO: Populate with relevant params.
-    Key init args — client params:
-        timeout: Optional[float]
-            Timeout for requests.
-        max_retries: int
-            Max number of retries.
-        api_key: Optional[str]
-            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.
-
-    See full list of supported init args and their descriptions in the params section.
-
-    # TODO: Replace with relevant init params.
-    Instantiate:
+    Example:
         .. code-block:: python
 
             from __module_name__ import __ModuleName__LLM
 
-            llm = __ModuleName__LLM(
-                model="...",
-                temperature=0,
-                max_tokens=None,
-                timeout=None,
-                max_retries=2,
-                # api_key="...",
-                # other params...
-            )
-
-    Invoke:
-        .. code-block:: python
-
-            input_text = "The meaning of life is "
-            llm.invoke(input_text)
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
-    # TODO: Delete if token-level streaming isn't supported.
-    Stream:
-        .. code-block:: python
-
-            for chunk in llm.stream(input_text):
-                print(chunk)
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
-        .. code-block:: python
-
-            ''.join(llm.stream(input_text))
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
-    # TODO: Delete if native async isn't supported.
-    Async:
-        .. code-block:: python
-
-            await llm.ainvoke(input_text)
-
-            # stream:
-            # async for chunk in (await llm.astream(input_text))
-
-            # batch:
-            # await llm.abatch([input_text])
-
-        .. code-block:: python
-
-            # TODO: Example output.
+            model = __ModuleName__LLM()
+            model.invoke("Come up with 10 names for a song about parrots")
     """
 
+    @property
+    def _llm_type(self) -> str:
+        """Return type of LLM."""
+        return "__package_name_short__-llm"
+
     # TODO: This method must be implemented to generate text completions.
     def _generate(
         self,
@@ -119,37 +45,32 @@ class __ModuleName__LLM(BaseLLM):
 
     # TODO: Implement if __ModuleName__LLM supports async generation. Otherwise
     # delete method.
-    # async def _agenerate(
-    #     self,
-    #     prompts: List[str],
-    #     stop: Optional[List[str]] = None,
-    #     run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-    #     **kwargs: Any,
-    # ) -> LLMResult:
-    #     raise NotImplementedError
+    async def _agenerate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        raise NotImplementedError
 
     # TODO: Implement if __ModuleName__LLM supports streaming. Otherwise delete method.
-    # def _stream(
-    #     self,
-    #     prompt: str,
-    #     stop: Optional[List[str]] = None,
-    #     run_manager: Optional[CallbackManagerForLLMRun] = None,
-    #     **kwargs: Any,
-    # ) -> Iterator[GenerationChunk]:
-    #     raise NotImplementedError
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        raise NotImplementedError
 
     # TODO: Implement if __ModuleName__LLM supports async streaming. Otherwise delete
     # method.
-    # async def _astream(
-    #     self,
-    #     prompt: str,
-    #     stop: Optional[List[str]] = None,
-    #     run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-    #     **kwargs: Any,
-    # ) -> AsyncIterator[GenerationChunk]:
-    #     raise NotImplementedError
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of LLM."""
-        return "__package_name_short__-llm"
+    async def _astream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[GenerationChunk]:
+        raise NotImplementedError

libs/cli/langchain_cli/integration_template/integration_template/toolkits.py

@@ -1,71 +0,0 @@
-"""__ModuleName__ chat models."""
-
-from typing import List
-from langchain_core.tools import BaseTool, BaseToolKit
-
-
-class __ModuleName__Toolkit(BaseToolKit):
-    # TODO: Replace all TODOs in docstring. See example docstring:
-    # https://github.com/langchain-ai/langchain/blob/a6d1fb4275801a4850e62b6209cfbf096a24f93f/libs/community/langchain_community/agent_toolkits/sql/toolkit.py#L20
-    """__ModuleName__ toolkit.
-
-    # TODO: Replace with relevant packages, env vars, etc.
-    Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
-
-    # TODO: Populate with relevant params.
-    Key init args:
-        arg 1: type
-            description
-        arg 2: type
-            description
-
-    # TODO: Replace with relevant init params.
-    Instantiate:
-        .. code-block:: python
-
-            from __package_name__ import __ModuleName__Toolkit
-
-            toolkit = __ModuleName__Toolkit(
-                # ...
-            )
-
-    Tools:
-        .. code-block:: python
-
-            toolkit.get_tools()
-
-        .. code-block:: python
-
-            # TODO: Example output.
-
-    Use within an agent:
-        .. code-block:: python
-
-            from langgraph.prebuilt import create_react_agent
-
-            agent_executor = create_react_agent(llm, tools)
-
-            example_query = "..."
-
-            events = agent_executor.stream(
-                {"messages": [("user", example_query)]},
-                stream_mode="values",
-            )
-            for event in events:
-                event["messages"][-1].pretty_print()
-
-        .. code-block:: python
-
-             # TODO: Example output.
-
-    """  # noqa: E501
-
-    # TODO: This method must be implemented to generate chat responses.
-    def get_tools(self) -> List[BaseTool]:
-        raise NotImplementedError()

libs/cli/langchain_cli/integration_template/integration_template/tools.py

@@ -1,93 +0,0 @@
-"""__ModuleName__ tools."""
-
-from typing import Optional, Type
-
-from langchain_core.callbacks import (
-    AsyncCallbackManagerForToolRun,
-    CallbackManagerForToolRun,
-)
-from langchain_core.pydantic_v1 import BaseModel, Field
-from langchain_core.tools import BaseTool
-
-
-class __ModuleName__Input(BaseModel):
-    """Input schema for __ModuleName__ tool.
-
-    This docstring is **not** part of what is sent to the model when performing tool
-    calling. The Field default values and descriptions **are** part of what is sent to
-    the model when performing tool calling.
-    """
-
-    # TODO: Add input args and descriptions.
-    # a: int = Field(..., description="first number")
-    # b: int = Field(0, description="second number")
-    ...
-
-
-class __ModuleName__Tool(BaseTool):
-    """__ModuleName__ tool.
-
-    Setup:
-        # TODO: Replace with relevant packages, env vars.
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
-
-    Instantiation:
-        .. code-block:: python
-
-            tool = __ModuleName__Tool(
-                # TODO: init params
-            )
-
-    Invocation with args:
-        .. code-block:: python
-
-            # TODO: invoke args
-            tool.invoke({...})
-
-        .. code-block:: python
-
-            # TODO: output of invocation
-
-    Invocation with ToolCall:
-
-        .. code-block:: python
-
-            # TODO: invoke args
-            tool.invoke({"args": {...}, "id": "1", "name": tool.name, "type": "tool_call})
-
-        .. code-block:: python
-
-            # TODO: output of invocation
-    """
-
-    # TODO: Set tool name and description
-    name: str = "TODO: Tool name"
-    """The name that is passed to the model when performing tool calling."""
-    description: str = "TODO: Tool description."
-    """The description that is passed to the model when performing tool calling."""
-    args_schema: Type[BaseModel] = __ModuleName__Input
-    """The schema that is passed to the model when performing tool calling."""
-
-    # TODO: Add any other init params for the tool.
-    # param1: Optional[str]
-    # """param1 determines foobar"""
-
-    # TODO: Replaced *args with real tool arguments.
-    def _run(
-        self, *args, run_manager: Optional[CallbackManagerForToolRun] = None
-    ) -> str:
-        raise NotImplementedError
-
-    # TODO: Implement if tool has native async functionality, otherwise delete.
-
-    # async def _arun(
-    #     self,
-    #     *args,
-    #     run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
-    # ) -> str:
-    #     ...

libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py

@@ -26,109 +26,15 @@ VST = TypeVar("VST", bound=VectorStore)
 
 
 class __ModuleName__VectorStore(VectorStore):
-    # TODO: Replace all TODOs in docstring.
-    """__ModuleName__ vector store integration.
+    """__ModuleName__ vector store.
 
-    # TODO: Replace with relevant packages, env vars.
-    Setup:
-        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U __package_name__
-            export __MODULE_NAME___API_KEY="your-api-key"
-
-    # TODO: Populate with relevant params.
-    Key init args — indexing params:
-        collection_name: str
-            Name of the collection.
-        embedding_function: Embeddings
-            Embedding function to use.
-
-    # TODO: Populate with relevant params.
-    Key init args — client params:
-        client: Optional[Client]
-            Client to use.
-        connection_args: Optional[dict]
-            Connection arguments.
-
-    # TODO: Replace with relevant init params.
-    Instantiate:
+    Example:
         .. code-block:: python
 
             from __module_name__.vectorstores import __ModuleName__VectorStore
 
-            vector_store = __ModuleName__VectorStore(
-                collection_name="foo",
-                embedding_function=OpenAIEmbeddings(),
-                connection_args={"uri": "./foo.db"},
-                # other params...
-            )
-
-    # TODO: Populate with relevant variables.
-    Add Documents:
-        .. code-block:: python
-
-            from langchain_core.documents import Document
-
-            document = Document(page_content="foo", metadata={"baz": "bar"})
-            vector_store.add_documents([document],ids=["1"])
-
-    # TODO: Populate with relevant variables.
-    Update Documents:
-        .. code-block:: python
-
-            updated_document = Document(
-                page_content="qux",
-                metadata={"bar": "baz"}
-            )
-
-            vector_store.update_documents(document_id="1",document=updated_document)
-
-    Delete Documents:
-        .. code-block:: python
-
-            vector_store.delete(ids=["1"])
-
-    # TODO: Fill out with relevant variables and example output.
-    Search:
-        .. code-block:: python
-
-            results = vector_store.similarity_search(query="thud",k=1)
-            for doc in results:
-                print(f"* {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            # TODO: Example output
-
-    # TODO: Fill out with relevant variables and example output.
-    Search with score:
-        .. code-block:: python
-
-            results = vector_store.similarity_search_with_score(query="thud",k=1)
-            for doc, score in results:
-                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
-
-        .. code-block:: python
-
-            # TODO: Example output
-
-    # TODO: Fill out with relevant variables and example output.
-    Use as Retriever:
-        .. code-block:: python
-
-            retriever = vector_store.as_retriever(
-                search_type="mmr",
-                search_kwargs={"k": 1, "fetch_k": 10, "lambda_mult": 0.5},
-            )
-            retriever.invoke("thud")
-
-        .. code-block:: python
-
-            # TODO: Example output
-
-    """  # noqa: E501
+            vectorstore = __ModuleName__VectorStore()
+    """
 
     def add_texts(
         self,

libs/cli/langchain_cli/namespaces/integration.py

@@ -27,7 +27,7 @@ Replacements = TypedDict(
 )
 
 
-def _process_name(name: str, *, community: bool = False):
+def _process_name(name: str):
     preprocessed = name.replace("_", "-").lower()
 
     if preprocessed.startswith("langchain-"):
@@ -42,17 +42,16 @@ def _process_name(name: str, *, community: bool = False):
         raise ValueError("Name should not end with `-`.")
     if preprocessed.find("--") != -1:
         raise ValueError("Name should not contain consecutive hyphens.")
-    replacements = {
-        "__package_name__": f"langchain-{preprocessed}",
-        "__module_name__": "langchain_" + preprocessed.replace("-", "_"),
-        "__ModuleName__": preprocessed.title().replace("-", ""),
-        "__MODULE_NAME__": preprocessed.upper().replace("-", ""),
-        "__package_name_short__": preprocessed,
-        "__package_name_short_snake__": preprocessed.replace("-", "_"),
-    }
-    if community:
-        replacements["__module_name__"] = preprocessed.replace("-", "_")
-    return Replacements(replacements)
+    return Replacements(
+        {
+            "__package_name__": f"langchain-{preprocessed}",
+            "__module_name__": "langchain_" + preprocessed.replace("-", "_"),
+            "__ModuleName__": preprocessed.title().replace("-", ""),
+            "__MODULE_NAME__": preprocessed.upper().replace("-", ""),
+            "__package_name_short__": preprocessed,
+            "__package_name_short_snake__": preprocessed.replace("-", "_"),
+        }
+    )
 
 
 @integration_cli.command()
@@ -156,8 +155,8 @@ def create_doc(
         str,
         typer.Option(
             help=(
-                "The type of component. Currently only 'ChatModel', "
-                "'DocumentLoader', 'VectorStore' supported."
+                "The type of component. Currently only 'ChatModel', 'DocumentLoader' "
+                "supported."
             ),
         ),
     ] = "ChatModel",
@@ -173,7 +172,7 @@ def create_doc(
     Creates a new integration doc.
     """
     try:
-        replacements = _process_name(name, community=component_type=="Tool")
+        replacements = _process_name(name)
     except ValueError as e:
         typer.echo(e)
         raise typer.Exit(code=1)
@@ -201,19 +200,14 @@ def create_doc(
     )
 
     # copy over template from ../integration_template
-    template_dir = Path(__file__).parents[1] / "integration_template" / "docs"
     if component_type == "ChatModel":
-        docs_template = template_dir / "chat.ipynb"
+        docs_template = (
+            Path(__file__).parents[1] / "integration_template/docs/chat.ipynb"
+        )
     elif component_type == "DocumentLoader":
-        docs_template = template_dir / "document_loaders.ipynb"
-    elif component_type == "Tool":
-        docs_template = template_dir / "tools.ipynb"
-    elif component_type == "VectorStore":
-        docs_template = template_dir / "vectorstores.ipynb"
-    else:
-        raise ValueError(
-            f"Unrecognized {component_type=}. Expected one of 'ChatModel', "
-            f"'DocumentLoader', 'Tool'."
+        docs_template = (
+            Path(__file__).parents[1]
+            / "integration_template/docs/document_loaders.ipynb"
         )
     shutil.copy(docs_template, destination_path)
 

libs/cli/poetry.lock

@@ -426,13 +426,13 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.25"
+version = "0.2.23"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.25-py3-none-any.whl", hash = "sha256:03d61b2a7f4b5f98df248c1b1f0ccd95c9d5ef2269e174133724365cd2a7ee1e"},
-    {file = "langchain_core-0.2.25.tar.gz", hash = "sha256:e64106a7d0e37e4d35b767f79e6c62b56e825f08f9e8cc4368bcea9955257a7e"},
+    {file = "langchain_core-0.2.23-py3-none-any.whl", hash = "sha256:ef0b4184b37e356a27182514aedcc8c41ffacbd6348a801bc775c1ce1f608637"},
+    {file = "langchain_core-0.2.23.tar.gz", hash = "sha256:ac8165f283d8f5214576ffc38387106ef0de7eb8d2c52576d06e8dd3285294b0"},
 ]
 
 [package.dependencies]
@@ -473,13 +473,13 @@ server = ["fastapi (>=0.90.1,<1)", "sse-starlette (>=1.3.0,<2.0.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.94"
+version = "0.1.93"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.94-py3-none-any.whl", hash = "sha256:0d01212086d58699f75814117b026784218042f7859877ce08a248a98d84aa8d"},
-    {file = "langsmith-0.1.94.tar.gz", hash = "sha256:e44afcdc9eee6f238f6a87a02bba83111bd5fad376d881ae299834e06d39d712"},
+    {file = "langsmith-0.1.93-py3-none-any.whl", hash = "sha256:811210b9d5f108f36431bd7b997eb9476a9ecf5a2abd7ddbb606c1cdcf0f43ce"},
+    {file = "langsmith-0.1.93.tar.gz", hash = "sha256:285b6ad3a54f50fa8eb97b5f600acc57d0e37e139dd8cf2111a117d0435ba9b4"},
 ]
 
 [package.dependencies]
@@ -1112,40 +1112,40 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.5.5"
+version = "0.5.4"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "ruff-0.5.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:00817603822a3e42b80f7c3298c8269e09f889ee94640cd1fc7f9329788d7bf8"},
-    {file = "ruff-0.5.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:187a60f555e9f865a2ff2c6984b9afeffa7158ba6e1eab56cb830404c942b0f3"},
-    {file = "ruff-0.5.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fe26fc46fa8c6e0ae3f47ddccfbb136253c831c3289bba044befe68f467bfb16"},
-    {file = "ruff-0.5.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4ad25dd9c5faac95c8e9efb13e15803cd8bbf7f4600645a60ffe17c73f60779b"},
-    {file = "ruff-0.5.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f70737c157d7edf749bcb952d13854e8f745cec695a01bdc6e29c29c288fc36e"},
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+    {file = "ruff-0.5.4.tar.gz", hash = "sha256:2795726d5f71c4f4e70653273d1c23a8182f07dd8e48c12de5d867bfb7557eed"},
 ]
 
 [[package]]
 name = "setuptools"
-version = "72.1.0"
+version = "71.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-72.1.0-py3-none-any.whl", hash = "sha256:5a03e1860cf56bb6ef48ce186b0e557fdba433237481a9a625176c2831be15d1"},
-    {file = "setuptools-72.1.0.tar.gz", hash = "sha256:8d243eff56d095e5817f796ede6ae32941278f542e0f941867cc05ae52b162ec"},
+    {file = "setuptools-71.1.0-py3-none-any.whl", hash = "sha256:33874fdc59b3188304b2e7c80d9029097ea31627180896fb549c578ceb8a0855"},
+    {file = "setuptools-71.1.0.tar.gz", hash = "sha256:032d42ee9fb536e33087fb66cac5f840eb9391ed05637b3f2a76a7c8fb477936"},
 ]
 
 [package.extras]

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-cli"
-version = "0.0.27"
+version = "0.0.26"
 description = "CLI for interacting with LangChain"
 authors = ["Erick Friis <erick@langchain.dev>"]
 readme = "README.md"

libs/community/extended_testing_deps.txt

@@ -40,7 +40,6 @@ jinja2>=3,<4
 jq>=1.4.1,<2
 jsonschema>1
 keybert>=0.8.5
-litellm>=1.30,<=1.39.5
 lxml>=4.9.3,<6.0
 markdownify>=0.11.6,<0.12
 motor>=3.3.1,<4
@@ -87,7 +86,7 @@ tree-sitter>=0.20.2,<0.21
 tree-sitter-languages>=1.8.0,<2
 upstash-redis>=1.1.0,<2
 upstash-ratelimit>=1.1.0,<2
-vdms>=0.0.20
+vdms==0.0.20
 xata>=1.0.0a7,<2
 xmltodict>=0.13.0,<0.14
 nanopq==0.2.1

libs/community/langchain_community/agent_toolkits/sql/toolkit.py

@@ -17,63 +17,12 @@ from langchain_community.utilities.sql_database import SQLDatabase
 
 
 class SQLDatabaseToolkit(BaseToolkit):
-    """SQLDatabaseToolkit for interacting with SQL databases.
+    """Toolkit for interacting with SQL databases.
 
-    Setup:
-        Install ``langchain-community``.
-
-        .. code-block:: bash
-
-            pip install -U langchain-community
-
-    Key init args:
-        db: SQLDatabase
-            The SQL database.
-        llm: BaseLanguageModel
-            The language model (for use with QuerySQLCheckerTool)
-
-    Instantiate:
-        .. code-block:: python
-
-            from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
-            from langchain_community.utilities.sql_database import SQLDatabase
-            from langchain_openai import ChatOpenAI
-
-            db = SQLDatabase.from_uri("sqlite:///Chinook.db")
-            llm = ChatOpenAI(temperature=0)
-
-            toolkit = SQLDatabaseToolkit(db=db, llm=llm)
-
-    Tools:
-        .. code-block:: python
-
-            toolkit.get_tools()
-
-    Use within an agent:
-        .. code-block:: python
-
-            from langchain import hub
-            from langgraph.prebuilt import create_react_agent
-
-            # Pull prompt (or define your own)
-            prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
-            system_message = prompt_template.format(dialect="SQLite", top_k=5)
-
-            # Create agent
-            agent_executor = create_react_agent(
-                llm, toolkit.get_tools(), state_modifier=system_message
-            )
-
-            # Query agent
-            example_query = "Which country's customers spent the most?"
-
-            events = agent_executor.stream(
-                {"messages": [("user", example_query)]},
-                stream_mode="values",
-            )
-            for event in events:
-                event["messages"][-1].pretty_print()
-    """  # noqa: E501
+    Parameters:
+        db: SQLDatabase. The SQL database.
+        llm: BaseLanguageModel. The language model.
+    """
 
     db: SQLDatabase = Field(exclude=True)
     llm: BaseLanguageModel = Field(exclude=True)

libs/community/langchain_community/chains/openapi/chain.py

@@ -3,19 +3,29 @@
 from __future__ import annotations
 
 import json
-from typing import Any, Dict, List, NamedTuple, Optional, cast
+from typing import Any, Dict, List, Literal, NamedTuple, Optional, cast
 
+import aiohttp
 from langchain.chains.api.openapi.requests_chain import APIRequesterChain
 from langchain.chains.api.openapi.response_chain import APIResponderChain
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks
-from langchain_core.language_models import BaseLanguageModel
+from langchain_core.language_models import BaseChatModel, BaseLanguageModel
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+)
 from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.runnables import Runnable, chain
+from langchain_core.tools import BaseTool
 from requests import Response
 
+from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit
 from langchain_community.tools.openapi.utils.api_models import APIOperation
-from langchain_community.utilities.requests import Requests
+from langchain_community.utilities.requests import Requests, TextRequestsWrapper
 
 
 class _ParamMapping(NamedTuple):
@@ -228,3 +238,84 @@ class OpenAPIEndpointChain(Chain, BaseModel):
             callbacks=callbacks,
             **kwargs,
         )
+
+
+def _prepend_system_message(query: str, system_message: str) -> List[BaseMessage]:
+    return [SystemMessage(system_message), HumanMessage(query)]
+
+
+def _invoke_llm(messages: List[BaseMessage], llm: Runnable) -> List[BaseMessage]:
+    return messages + [llm.invoke(messages)]
+
+
+def _execute_tools(
+    messages: List[BaseMessage], tool_name_to_tool: Dict[str, BaseTool]
+) -> List[BaseMessage]:
+    """Execute tool and return result as a string."""
+    output_messages = []
+    ai_message = next(
+        message for message in messages[::-1] if isinstance(message, AIMessage)
+    )
+    for tool_call in ai_message.tool_calls:
+        selected_tool = tool_name_to_tool[tool_call["name"]]
+        tool_msg = selected_tool.invoke(tool_call)
+        output_messages.append(tool_msg)
+    return messages + output_messages
+
+
+def create_openapi_endpoint_chain(
+    llm: BaseChatModel,
+    api_spec: str,
+    system_message: Optional[str] = None,
+    allow_dangerous_requests: bool = False,
+    headers: Optional[Dict[str, str]] = None,
+    aiosession: Optional[aiohttp.ClientSession] = None,
+    auth: Optional[Any] = None,
+    response_content_type: Literal["text", "json"] = "text",
+    verify: bool = True,
+    supported_tools: Optional[List[str]] = None,
+) -> Runnable:
+    requests_wrapper = TextRequestsWrapper(
+        headers=headers,
+        aiosession=aiosession,
+        auth=auth,
+        response_content_type=response_content_type,
+        verify=verify,
+    )
+    toolkit = RequestsToolkit(
+        requests_wrapper=requests_wrapper,
+        allow_dangerous_requests=allow_dangerous_requests,
+    )
+    if supported_tools is None:
+        supported_tools = [
+            "requests_get",
+            "requests_post",
+            "requests_patch",
+            "requests_put",
+            "requests_delete",
+        ]
+    tools = [tool for tool in toolkit.get_tools() if tool.name in supported_tools]
+    llm_with_tools = llm.bind_tools(tools)
+    tool_name_to_tool = {tool.name: tool for tool in tools}
+    if system_message is None:
+        system_message = """
+        You have access to an API to help answer user queries.
+
+        Here is documentation on the API:
+        {api_spec}
+        """
+    system_message = system_message.format(api_spec=api_spec)
+
+    @chain
+    def prepend_system_message(query: str) -> List[BaseMessage]:
+        return _prepend_system_message(query, system_message)
+
+    @chain
+    def invoke_llm(messages: List[BaseMessage]) -> List[BaseMessage]:
+        return _invoke_llm(messages, llm_with_tools)
+
+    @chain
+    def execute_tools(messages: List[BaseMessage]) -> List[BaseMessage]:
+        return _execute_tools(messages, tool_name_to_tool)
+
+    return prepend_system_message | invoke_llm | execute_tools | llm

libs/community/langchain_community/chat_models/__init__.py

@@ -165,15 +165,13 @@ if TYPE_CHECKING:
     from langchain_community.chat_models.yandex import (
         ChatYandexGPT,
     )
-    from langchain_community.chat_models.yi import (
-        ChatYi,
-    )
     from langchain_community.chat_models.yuan2 import (
         ChatYuan2,
     )
     from langchain_community.chat_models.zhipuai import (
         ChatZhipuAI,
     )
+
 __all__ = [
     "AzureChatOpenAI",
     "BedrockChat",
@@ -227,7 +225,6 @@ __all__ = [
     "QianfanChatEndpoint",
     "SolarChat",
     "VolcEngineMaasChat",
-    "ChatYi",
 ]
 
 
@@ -284,7 +281,6 @@ _module_lookup = {
     "VolcEngineMaasChat": "langchain_community.chat_models.volcengine_maas",
     "ChatPremAI": "langchain_community.chat_models.premai",
     "ChatLlamaCpp": "langchain_community.chat_models.llamacpp",
-    "ChatYi": "langchain_community.chat_models.yi",
 }
 
 

libs/community/langchain_community/chat_models/baichuan.py

@@ -1,26 +1,13 @@
 import json
 import logging
 from contextlib import asynccontextmanager
-from typing import (
-    Any,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Sequence,
-    Type,
-    Union,
-)
+from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, Type
 
 import requests
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
-from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import (
     BaseChatModel,
     agenerate_from_stream,
@@ -37,27 +24,14 @@ from langchain_core.messages import (
     HumanMessageChunk,
     SystemMessage,
     SystemMessageChunk,
-    ToolMessage,
-)
-from langchain_core.output_parsers.openai_tools import (
-    make_invalid_tool_call,
-    parse_tool_call,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
-from langchain_core.runnables import Runnable
-from langchain_core.tools import BaseTool
+from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     get_pydantic_field_names,
 )
-from langchain_core.utils.function_calling import convert_to_openai_tool
-
-from langchain_community.chat_models.llamacpp import (
-    _lc_invalid_tool_call_to_openai_tool_call,
-    _lc_tool_call_to_openai_tool_call,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -66,33 +40,14 @@ DEFAULT_API_BASE = "https://api.baichuan-ai.com/v1/chat/completions"
 
 def _convert_message_to_dict(message: BaseMessage) -> dict:
     message_dict: Dict[str, Any]
-    content = message.content
     if isinstance(message, ChatMessage):
-        message_dict = {"role": message.role, "content": content}
+        message_dict = {"role": message.role, "content": message.content}
     elif isinstance(message, HumanMessage):
-        message_dict = {"role": "user", "content": content}
+        message_dict = {"role": "user", "content": message.content}
     elif isinstance(message, AIMessage):
-        message_dict = {"role": "assistant", "content": content}
-        if "tool_calls" in message.additional_kwargs:
-            message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
-
-        elif message.tool_calls or message.invalid_tool_calls:
-            message_dict["tool_calls"] = [
-                _lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
-            ] + [
-                _lc_invalid_tool_call_to_openai_tool_call(tc)
-                for tc in message.invalid_tool_calls
-            ]
-    elif isinstance(message, ToolMessage):
-        message_dict = {
-            "role": "tool",
-            "tool_call_id": message.tool_call_id,
-            "content": content,
-            "name": message.name or message.additional_kwargs.get("name"),
-        }
-
+        message_dict = {"role": "assistant", "content": message.content}
     elif isinstance(message, SystemMessage):
-        message_dict = {"role": "system", "content": content}
+        message_dict = {"role": "system", "content": message.content}
     else:
         raise TypeError(f"Got unknown type {message}")
 
@@ -101,43 +56,14 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     role = _dict["role"]
-    content = _dict.get("content", "")
     if role == "user":
-        return HumanMessage(content=content)
+        return HumanMessage(content=_dict["content"])
     elif role == "assistant":
-        tool_calls = []
-        invalid_tool_calls = []
-        additional_kwargs = {}
-
-        if raw_tool_calls := _dict.get("tool_calls"):
-            additional_kwargs["tool_calls"] = raw_tool_calls
-            for raw_tool_call in raw_tool_calls:
-                try:
-                    tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
-                except Exception as e:
-                    invalid_tool_calls.append(
-                        make_invalid_tool_call(raw_tool_call, str(e))
-                    )
-
-        return AIMessage(
-            content=content,
-            additional_kwargs=additional_kwargs,
-            tool_calls=tool_calls,  # type: ignore[arg-type]
-            invalid_tool_calls=invalid_tool_calls,
-        )
-    elif role == "tool":
-        additional_kwargs = {}
-        if "name" in _dict:
-            additional_kwargs["name"] = _dict["name"]
-        return ToolMessage(
-            content=content,
-            tool_call_id=_dict.get("tool_call_id"),  # type: ignore[arg-type]
-            additional_kwargs=additional_kwargs,
-        )
+        return AIMessage(content=_dict.get("content", "") or "")
     elif role == "system":
-        return SystemMessage(content=content)
+        return SystemMessage(content=_dict.get("content", ""))
     else:
-        return ChatMessage(content=content, role=role)
+        return ChatMessage(content=_dict["content"], role=role)
 
 
 def _convert_delta_to_message_chunk(
@@ -300,24 +226,6 @@ class ChatBaichuan(BaseChatModel):
                 },
                 id='run-952509ed-9154-4ff9-b187-e616d7ddfbba-0'
             )
-    Tool calling:
-
-        .. code-block:: python
-            class get_current_weather(BaseModel):
-                '''Get current weather.'''
-
-                location: str = Field('City or province, such as Shanghai')
-
-
-            llm_with_tools = ChatBaichuan(model='Baichuan3-Turbo').bind_tools([get_current_weather])
-            llm_with_tools.invoke('How is the weather today?')
-
-        .. code-block:: python
-
-            [{'name': 'get_current_weather',
-            'args': {'location': 'New York'},
-            'id': '3951017OF8doB0A',
-            'type': 'tool_call'}]
 
     Response metadata
         .. code-block:: python
@@ -578,7 +486,6 @@ class ChatBaichuan(BaseChatModel):
         model = parameters.pop("model")
         with_search_enhance = parameters.pop("with_search_enhance", False)
         stream = parameters.pop("stream", False)
-        tools = parameters.pop("tools", [])
 
         payload = {
             "model": model,
@@ -588,9 +495,7 @@ class ChatBaichuan(BaseChatModel):
             "temperature": temperature,
             "with_search_enhance": with_search_enhance,
             "stream": stream,
-            "tools": tools,
         }
-
         return payload
 
     def _create_headers_parameters(self, **kwargs) -> Dict[str, Any]:  # type: ignore[no-untyped-def]
@@ -621,23 +526,3 @@ class ChatBaichuan(BaseChatModel):
     @property
     def _llm_type(self) -> str:
         return "baichuan-chat"
-
-    def bind_tools(
-        self,
-        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
-        **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
-        """Bind tool-like objects to this chat model.
-
-        Args:
-            tools: A list of tool definitions to bind to this chat model.
-                Can be a dictionary, pydantic model, callable, or BaseTool.
-                Pydantic
-                models, callables, and BaseTools will be automatically converted to
-                their schema dictionary representation.
-            **kwargs: Any additional parameters to pass to the
-                :class:`~langchain.runnable.Runnable` constructor.
-        """
-
-        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
-        return super().bind(tools=formatted_tools, **kwargs)

libs/community/langchain_community/chat_models/deepinfra.py

@@ -207,7 +207,7 @@ class ChatDeepInfra(BaseChatModel):
     request_timeout: Optional[float] = Field(default=None, alias="timeout")
     temperature: Optional[float] = 1
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Run inference with this temperature. Must be in the closed
+    """Run inference with this temperature. Must by in the closed
        interval [0.0, 1.0]."""
     top_p: Optional[float] = None
     """Decode using nucleus sampling: consider the smallest set of tokens whose

libs/community/langchain_community/chat_models/google_palm.py

@@ -236,7 +236,7 @@ class ChatGooglePalm(BaseChatModel, BaseModel):
     """Model name to use."""
     google_api_key: Optional[SecretStr] = None
     temperature: Optional[float] = None
-    """Run inference with this temperature. Must be in the closed
+    """Run inference with this temperature. Must by in the closed
        interval [0.0, 1.0]."""
     top_p: Optional[float] = None
     """Decode using nucleus sampling: consider the smallest set of tokens whose

libs/community/langchain_community/chat_models/litellm.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import json
 import logging
 from typing import (
     Any,
@@ -43,9 +42,6 @@ from langchain_core.messages import (
     HumanMessageChunk,
     SystemMessage,
     SystemMessageChunk,
-    ToolCall,
-    ToolCallChunk,
-    ToolMessage,
 )
 from langchain_core.outputs import (
     ChatGeneration,
@@ -136,30 +132,10 @@ def _convert_delta_to_message_chunk(
     else:
         additional_kwargs = {}
 
-    tool_call_chunks = []
-    if raw_tool_calls := _dict.get("tool_calls"):
-        additional_kwargs["tool_calls"] = raw_tool_calls
-        try:
-            tool_call_chunks = [
-                ToolCallChunk(
-                    name=rtc["function"].get("name"),
-                    args=rtc["function"].get("arguments"),
-                    id=rtc.get("id"),
-                    index=rtc["index"],
-                )
-                for rtc in raw_tool_calls
-            ]
-        except KeyError:
-            pass
-
     if role == "user" or default_class == HumanMessageChunk:
         return HumanMessageChunk(content=content)
     elif role == "assistant" or default_class == AIMessageChunk:
-        return AIMessageChunk(
-            content=content,
-            additional_kwargs=additional_kwargs,
-            tool_call_chunks=tool_call_chunks,
-        )
+        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
     elif role == "system" or default_class == SystemMessageChunk:
         return SystemMessageChunk(content=content)
     elif role == "function" or default_class == FunctionMessageChunk:
@@ -170,41 +146,23 @@ def _convert_delta_to_message_chunk(
         return default_class(content=content)  # type: ignore[call-arg]
 
 
-def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
-    return {
-        "type": "function",
-        "id": tool_call["id"],
-        "function": {
-            "name": tool_call["name"],
-            "arguments": json.dumps(tool_call["args"]),
-        },
-    }
-
-
 def _convert_message_to_dict(message: BaseMessage) -> dict:
-    message_dict: Dict[str, Any] = {"content": message.content}
     if isinstance(message, ChatMessage):
-        message_dict["role"] = message.role
+        message_dict = {"role": message.role, "content": message.content}
     elif isinstance(message, HumanMessage):
-        message_dict["role"] = "user"
+        message_dict = {"role": "user", "content": message.content}
     elif isinstance(message, AIMessage):
-        message_dict["role"] = "assistant"
+        message_dict = {"role": "assistant", "content": message.content}
         if "function_call" in message.additional_kwargs:
             message_dict["function_call"] = message.additional_kwargs["function_call"]
-        if message.tool_calls:
-            message_dict["tool_calls"] = [
-                _lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
-            ]
-        elif "tool_calls" in message.additional_kwargs:
-            message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
     elif isinstance(message, SystemMessage):
-        message_dict["role"] = "system"
+        message_dict = {"role": "system", "content": message.content}
     elif isinstance(message, FunctionMessage):
-        message_dict["role"] = "function"
-        message_dict["name"] = message.name
-    elif isinstance(message, ToolMessage):
-        message_dict["role"] = "tool"
-        message_dict["tool_call_id"] = message.tool_call_id
+        message_dict = {
+            "role": "function",
+            "content": message.content,
+            "name": message.name,
+        }
     else:
         raise ValueError(f"Got unknown type {message}")
     if "name" in message.additional_kwargs:
@@ -232,7 +190,7 @@ class ChatLiteLLM(BaseChatModel):
     request_timeout: Optional[Union[float, Tuple[float, float]]] = None
     temperature: Optional[float] = 1
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Run inference with this temperature. Must be in the closed
+    """Run inference with this temperature. Must by in the closed
        interval [0.0, 1.0]."""
     top_p: Optional[float] = None
     """Decode using nucleus sampling: consider the smallest set of tokens whose
@@ -402,8 +360,6 @@ class ChatLiteLLM(BaseChatModel):
         for chunk in self.completion_with_retry(
             messages=message_dicts, run_manager=run_manager, **params
         ):
-            if not isinstance(chunk, dict):
-                chunk = chunk.model_dump()
             if len(chunk["choices"]) == 0:
                 continue
             delta = chunk["choices"][0]["delta"]
@@ -428,8 +384,6 @@ class ChatLiteLLM(BaseChatModel):
         async for chunk in await acompletion_with_retry(
             self, messages=message_dicts, run_manager=run_manager, **params
         ):
-            if not isinstance(chunk, dict):
-                chunk = chunk.model_dump()
             if len(chunk["choices"]) == 0:
                 continue
             delta = chunk["choices"][0]["delta"]

libs/community/langchain_community/chat_models/minimax.py

@@ -3,25 +3,12 @@
 import json
 import logging
 from contextlib import asynccontextmanager, contextmanager
-from operator import itemgetter
-from typing import (
-    Any,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Sequence,
-    Type,
-    Union,
-)
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Type, Union
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
-from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import (
     BaseChatModel,
     agenerate_from_stream,
@@ -36,19 +23,10 @@ from langchain_core.messages import (
     ChatMessageChunk,
     HumanMessage,
     SystemMessage,
-    ToolMessage,
-)
-from langchain_core.output_parsers.base import OutputParserLike
-from langchain_core.output_parsers.openai_tools import (
-    JsonOutputKeyToolsParser,
-    PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
-from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
-from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
-from langchain_core.utils.function_calling import convert_to_openai_tool
 
 logger = logging.getLogger(__name__)
 
@@ -99,20 +77,9 @@ def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
     if isinstance(message, HumanMessage):
         message_dict = {"role": "user", "content": message.content}
     elif isinstance(message, AIMessage):
-        message_dict = {
-            "role": "assistant",
-            "content": message.content,
-            "tool_calls": message.additional_kwargs.get("tool_calls"),
-        }
+        message_dict = {"role": "assistant", "content": message.content}
     elif isinstance(message, SystemMessage):
         message_dict = {"role": "system", "content": message.content}
-    elif isinstance(message, ToolMessage):
-        message_dict = {
-            "role": "tool",
-            "content": message.content,
-            "tool_call_id": message.tool_call_id,
-            "name": message.name or message.additional_kwargs.get("name"),
-        }
     else:
         raise TypeError(f"Got unknown type '{message.__class__.__name__}'.")
     return message_dict
@@ -263,70 +230,6 @@ class MiniMaxChat(BaseChatModel):
                 id='run-c263b6f1-1736-4ece-a895-055c26b3436f-0'
             )
 
-    Tool calling:
-        .. code-block:: python
-
-            from langchain_core.pydantic_v1 import BaseModel, Field
-
-
-            class GetWeather(BaseModel):
-                '''Get the current weather in a given location'''
-
-                location: str = Field(
-                    ..., description="The city and state, e.g. San Francisco, CA"
-                )
-
-
-            class GetPopulation(BaseModel):
-                '''Get the current population in a given location'''
-
-                location: str = Field(
-                    ..., description="The city and state, e.g. San Francisco, CA"
-                )
-
-            chat_with_tools = chat.bind_tools([GetWeather, GetPopulation])
-            ai_msg = chat_with_tools.invoke(
-                "Which city is hotter today and which is bigger: LA or NY?"
-            )
-            ai_msg.tool_calls
-
-        .. code-block:: python
-
-            [
-                {
-                    'name': 'GetWeather',
-                    'args': {'location': 'LA'},
-                    'id': 'call_function_2140449382',
-                    'type': 'tool_call'
-                }
-            ]
-
-    Structured output:
-        .. code-block:: python
-
-            from typing import Optional
-
-            from langchain_core.pydantic_v1 import BaseModel, Field
-
-
-            class Joke(BaseModel):
-                '''Joke to tell user.'''
-                setup: str = Field(description="The setup of the joke")
-                punchline: str = Field(description="The punchline to the joke")
-                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
-
-
-            structured_chat = chat.with_structured_output(Joke)
-            structured_chat.invoke("Tell me a joke about cats")
-
-        .. code-block:: python
-
-            Joke(
-                setup='Why do cats have nine lives?',
-                punchline='Because they are so cute and cuddly!',
-                rating=None
-            )
-
     Response metadata
         .. code-block:: python
 
@@ -339,7 +242,7 @@ class MiniMaxChat(BaseChatModel):
              'model_name': 'abab6.5-chat',
              'finish_reason': 'stop'}
 
-    """  # noqa: E501
+    """  # noqa: E501conj
 
     @property
     def _identifying_params(self) -> Dict[str, Any]:
@@ -439,26 +342,12 @@ class MiniMaxChat(BaseChatModel):
         message_dicts = [_convert_message_to_dict(m) for m in messages]
         payload = self._default_params
         payload["messages"] = message_dicts
-
-        self._reformat_function_parameters(kwargs.get("tools", {}))
         payload.update(**kwargs)
-
         if is_stream:
             payload["stream"] = True
 
         return payload
 
-    @staticmethod
-    def _reformat_function_parameters(tools_arg: Dict[Any, Any]) -> None:
-        """Reformat the function parameters to strings."""
-        for tool_arg in tools_arg:
-            if tool_arg["type"] == "function" and not isinstance(
-                tool_arg["function"]["parameters"], str
-            ):
-                tool_arg["function"]["parameters"] = json.dumps(
-                    tool_arg["function"]["parameters"]
-                )
-
     def _generate(
         self,
         messages: List[BaseMessage],
@@ -632,154 +521,3 @@ class MiniMaxChat(BaseChatModel):
 
                     if finish_reason is not None:
                         break
-
-    def bind_tools(
-        self,
-        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
-        **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
-        """Bind tool-like objects to this chat model.
-
-        Args:
-            tools: A list of tool definitions to bind to this chat model.
-                Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
-                models, callables, and BaseTools will be automatically converted to
-                their schema dictionary representation.
-            **kwargs: Any additional parameters to pass to the
-                :class: `~langchain.runnable.Runnable` constructor.
-        """
-
-        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
-        return super().bind(tools=formatted_tools, **kwargs)
-
-    def with_structured_output(
-        self,
-        schema: Union[Dict, Type[BaseModel]],
-        *,
-        include_raw: bool = False,
-        **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
-        """Model wrapper that returns outputs formatted to match the given schema.
-
-        Args:
-            schema: The output schema as a dict or a Pydantic class. If a Pydantic class
-                then the model output will be an object of that class. If a dict then
-                the model output will be a dict. With a Pydantic class the returned
-                attributes will be validated, whereas with a dict they will not be. If
-                `method` is "function_calling" and `schema` is a dict, then the dict
-                must match the OpenAI function-calling spec.
-            include_raw: If False then only the parsed structured output is returned. If
-                an error occurs during model output parsing it will be raised. If True
-                then both the raw model response (a BaseMessage) and the parsed model
-                response will be returned. If an error occurs during output parsing it
-                will be caught and returned as well. The final output is always a dict
-                with keys "raw", "parsed", and "parsing_error".
-
-        Returns:
-            A Runnable that takes any ChatModel input and returns as output:
-
-                If include_raw is True then a dict with keys:
-                    raw: BaseMessage
-                    parsed: Optional[_DictOrPydantic]
-                    parsing_error: Optional[BaseException]
-
-                If include_raw is False then just _DictOrPydantic is returned,
-                where _DictOrPydantic depends on the schema:
-
-                If schema is a Pydantic class then _DictOrPydantic is the Pydantic
-                    class.
-
-                If schema is a dict then _DictOrPydantic is a dict.
-
-        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
-            .. code-block:: python
-
-                from langchain_community.chat_models import MiniMaxChat
-                from langchain_core.pydantic_v1 import BaseModel
-
-                class AnswerWithJustification(BaseModel):
-                    '''An answer to the user question along with justification for the answer.'''
-                    answer: str
-                    justification: str
-
-                llm = MiniMaxChat()
-                structured_llm = llm.with_structured_output(AnswerWithJustification)
-
-                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
-
-                # -> AnswerWithJustification(
-                #     answer='A pound of bricks and a pound of feathers weigh the same.',
-                #     justification='The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same.'
-                # )
-
-        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
-            .. code-block:: python
-
-                from langchain_community.chat_models import MiniMaxChat
-                from langchain_core.pydantic_v1 import BaseModel
-
-                class AnswerWithJustification(BaseModel):
-                    '''An answer to the user question along with justification for the answer.'''
-                    answer: str
-                    justification: str
-
-                llm = MiniMaxChat()
-                structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
-
-                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
-
-                # -> {
-                #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_function_8953642285', 'type': 'function', 'function': {'name': 'AnswerWithJustification', 'arguments': '{"answer": "A pound of bricks and a pound of feathers weigh the same.", "justification": "The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same."}'}}]}, response_metadata={'token_usage': {'total_tokens': 257}, 'model_name': 'abab6.5-chat', 'finish_reason': 'tool_calls'}, id='run-d897e037-2796-49f5-847e-f9f69dd390db-0', tool_calls=[{'name': 'AnswerWithJustification', 'args': {'answer': 'A pound of bricks and a pound of feathers weigh the same.', 'justification': 'The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same.'}, 'id': 'call_function_8953642285', 'type': 'tool_call'}]),
-                #     'parsed': AnswerWithJustification(answer='A pound of bricks and a pound of feathers weigh the same.', justification='The weight of the feathers is much less dense than the weight of the bricks, but since both weigh one pound, they weigh the same.'),
-                #     'parsing_error': None
-                # }
-
-        Example: Function-calling, dict schema (method="function_calling", include_raw=False):
-            .. code-block:: python
-
-                from langchain_community.chat_models import MiniMaxChat
-                from langchain_core.pydantic_v1 import BaseModel
-                from langchain_core.utils.function_calling import convert_to_openai_tool
-
-                class AnswerWithJustification(BaseModel):
-                    '''An answer to the user question along with justification for the answer.'''
-                    answer: str
-                    justification: str
-
-                dict_schema = convert_to_openai_tool(AnswerWithJustification)
-                llm = MiniMaxChat()
-                structured_llm = llm.with_structured_output(dict_schema)
-
-                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
-
-                # -> {
-                #     'answer': 'A pound of bricks and a pound of feathers both weigh the same, which is a pound.',
-                #     'justification': 'The difference is that bricks are much denser than feathers, so a pound of bricks will take up much less space than a pound of feathers.'
-                # }
-        """  # noqa: E501
-        if kwargs:
-            raise ValueError(f"Received unsupported arguments {kwargs}")
-        is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
-        llm = self.bind_tools([schema])
-        if is_pydantic_schema:
-            output_parser: OutputParserLike = PydanticToolsParser(
-                tools=[schema],  # type: ignore[list-item]
-                first_tool_only=True,  # type: ignore[list-item]
-            )
-        else:
-            key_name = convert_to_openai_tool(schema)["function"]["name"]
-            output_parser = JsonOutputKeyToolsParser(
-                key_name=key_name, first_tool_only=True
-            )
-
-        if include_raw:
-            parser_assign = RunnablePassthrough.assign(
-                parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
-            )
-            parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
-            parser_with_fallback = parser_assign.with_fallbacks(
-                [parser_none], exception_key="parsing_error"
-            )
-            return RunnableMap(raw=llm) | parser_with_fallback
-        else:
-            return llm | output_parser

libs/community/langchain_community/chat_models/mlflow.py

@@ -176,36 +176,29 @@ class ChatMlflow(BaseChatModel):
         chunk_iter = self._client.predict_stream(endpoint=self.endpoint, inputs=data)
         first_chunk_role = None
         for chunk in chunk_iter:
-            if chunk["choices"]:
-                choice = chunk["choices"][0]
+            choice = chunk["choices"][0]
 
-                chunk_delta = choice["delta"]
-                if first_chunk_role is None:
-                    first_chunk_role = chunk_delta.get("role")
+            chunk_delta = choice["delta"]
+            if first_chunk_role is None:
+                first_chunk_role = chunk_delta.get("role")
+            chunk = ChatMlflow._convert_delta_to_message_chunk(
+                chunk_delta, first_chunk_role
+            )
 
-                chunk_message = ChatMlflow._convert_delta_to_message_chunk(
-                    chunk_delta, first_chunk_role
-                )
+            generation_info = {}
+            if finish_reason := choice.get("finish_reason"):
+                generation_info["finish_reason"] = finish_reason
+            if logprobs := choice.get("logprobs"):
+                generation_info["logprobs"] = logprobs
 
-                generation_info = {}
-                if finish_reason := choice.get("finish_reason"):
-                    generation_info["finish_reason"] = finish_reason
-                if logprobs := choice.get("logprobs"):
-                    generation_info["logprobs"] = logprobs
+            chunk = ChatGenerationChunk(
+                message=chunk, generation_info=generation_info or None
+            )
 
-                chunk = ChatGenerationChunk(
-                    message=chunk_message, generation_info=generation_info or None
-                )
+            if run_manager:
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
 
-                if run_manager:
-                    run_manager.on_llm_new_token(
-                        chunk.text, chunk=chunk, logprobs=logprobs
-                    )
-
-                yield chunk
-            else:
-                # Handle the case where choices are empty if needed
-                continue
+            yield chunk
 
     @property
     def _identifying_params(self) -> Dict[str, Any]:

libs/community/langchain_community/chat_models/oci_generative_ai.py

@@ -1,22 +1,8 @@
 import json
-import re
-import uuid
 from abc import ABC, abstractmethod
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Sequence,
-    Type,
-    Union,
-)
+from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
-from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import (
     BaseChatModel,
     generate_from_stream,
@@ -28,76 +14,15 @@ from langchain_core.messages import (
     ChatMessage,
     HumanMessage,
     SystemMessage,
-    ToolCall,
-    ToolMessage,
-)
-from langchain_core.messages.tool import ToolCallChunk
-from langchain_core.output_parsers.base import OutputParserLike
-from langchain_core.output_parsers.openai_tools import (
-    JsonOutputKeyToolsParser,
-    PydanticToolsParser,
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Extra
-from langchain_core.runnables import Runnable
-from langchain_core.tools import BaseTool
-from langchain_core.utils.function_calling import convert_to_openai_function
+from langchain_core.pydantic_v1 import Extra
 
 from langchain_community.llms.oci_generative_ai import OCIGenAIBase
 from langchain_community.llms.utils import enforce_stop_tokens
 
 CUSTOM_ENDPOINT_PREFIX = "ocid1.generativeaiendpoint"
 
-JSON_TO_PYTHON_TYPES = {
-    "string": "str",
-    "number": "float",
-    "boolean": "bool",
-    "integer": "int",
-    "array": "List",
-    "object": "Dict",
-}
-
-
-def _remove_signature_from_tool_description(name: str, description: str) -> str:
-    """
-    Removes the `{name}{signature} - ` prefix and Args: section from tool description.
-    The signature is usually present for tools created with the @tool decorator,
-    whereas the Args: section may be present in function doc blocks.
-    """
-    description = re.sub(rf"^{name}\(.*?\) -(?:> \w+? -)? ", "", description)
-    description = re.sub(r"(?s)(?:\n?\n\s*?)?Args:.*$", "", description)
-    return description
-
-
-def _format_oci_tool_calls(
-    tool_calls: Optional[List[Any]] = None,
-) -> List[Dict]:
-    """
-    Formats a OCI GenAI API response into the tool call format used in Langchain.
-    """
-    if not tool_calls:
-        return []
-
-    formatted_tool_calls = []
-    for tool_call in tool_calls:
-        formatted_tool_calls.append(
-            {
-                "id": uuid.uuid4().hex[:],
-                "function": {
-                    "name": tool_call.name,
-                    "arguments": json.dumps(tool_call.parameters),
-                },
-                "type": "function",
-            }
-        )
-    return formatted_tool_calls
-
-
-def _convert_oci_tool_call_to_langchain(tool_call: Any) -> ToolCall:
-    """Convert a OCI GenAI tool call into langchain_core.messages.ToolCall"""
-    _id = uuid.uuid4().hex[:]
-    return ToolCall(name=tool_call.name, args=tool_call.parameters, id=_id)
-
 
 class Provider(ABC):
     @property
@@ -110,28 +35,14 @@ class Provider(ABC):
     @abstractmethod
     def chat_stream_to_text(self, event_data: Dict) -> str: ...
 
-    @abstractmethod
-    def is_chat_stream_end(self, event_data: Dict) -> bool: ...
-
     @abstractmethod
     def chat_generation_info(self, response: Any) -> Dict[str, Any]: ...
 
-    @abstractmethod
-    def chat_stream_generation_info(self, event_data: Dict) -> Dict[str, Any]: ...
-
     @abstractmethod
     def get_role(self, message: BaseMessage) -> str: ...
 
     @abstractmethod
-    def messages_to_oci_params(
-        self, messages: Any, **kwargs: Any
-    ) -> Dict[str, Any]: ...
-
-    @abstractmethod
-    def convert_to_oci_tool(
-        self,
-        tool: Union[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
-    ) -> Dict[str, Any]: ...
+    def messages_to_oci_params(self, messages: Any) -> Dict[str, Any]: ...
 
 
 class CohereProvider(Provider):
@@ -141,15 +52,10 @@ class CohereProvider(Provider):
         from oci.generative_ai_inference import models
 
         self.oci_chat_request = models.CohereChatRequest
-        self.oci_tool = models.CohereTool
-        self.oci_tool_param = models.CohereParameterDefinition
-        self.oci_tool_result = models.CohereToolResult
-        self.oci_tool_call = models.CohereToolCall
         self.oci_chat_message = {
             "USER": models.CohereUserMessage,
             "CHATBOT": models.CohereChatBotMessage,
             "SYSTEM": models.CohereSystemMessage,
-            "TOOL": models.CohereToolMessage,
         }
         self.chat_api_format = models.BaseChatRequest.API_FORMAT_COHERE
 
@@ -157,54 +63,15 @@ class CohereProvider(Provider):
         return response.data.chat_response.text
 
     def chat_stream_to_text(self, event_data: Dict) -> str:
-        if "text" in event_data:
+        if "text" in event_data and "finishReason" not in event_data:
             return event_data["text"]
         else:
             return ""
 
-    def is_chat_stream_end(self, event_data: Dict) -> bool:
-        return "finishReason" in event_data
-
     def chat_generation_info(self, response: Any) -> Dict[str, Any]:
-        generation_info: Dict[str, Any] = {
-            "documents": response.data.chat_response.documents,
-            "citations": response.data.chat_response.citations,
-            "search_queries": response.data.chat_response.search_queries,
-            "is_search_required": response.data.chat_response.is_search_required,
+        return {
             "finish_reason": response.data.chat_response.finish_reason,
         }
-        if response.data.chat_response.tool_calls:
-            # Only populate tool_calls when 1) present on the response and
-            #  2) has one or more calls.
-            generation_info["tool_calls"] = _format_oci_tool_calls(
-                response.data.chat_response.tool_calls
-            )
-
-        return generation_info
-
-    def chat_stream_generation_info(self, event_data: Dict) -> Dict[str, Any]:
-        generation_info: Dict[str, Any] = {
-            "documents": event_data.get("documents"),
-            "citations": event_data.get("citations"),
-            "finish_reason": event_data.get("finishReason"),
-        }
-        if "toolCalls" in event_data:
-            generation_info["tool_calls"] = []
-            for tool_call in event_data["toolCalls"]:
-                generation_info["tool_calls"].append(
-                    {
-                        "id": uuid.uuid4().hex[:],
-                        "function": {
-                            "name": tool_call["name"],
-                            "arguments": json.dumps(tool_call["parameters"]),
-                        },
-                        "type": "function",
-                    }
-                )
-
-        generation_info = {k: v for k, v in generation_info.items() if v is not None}
-
-        return generation_info
 
     def get_role(self, message: BaseMessage) -> str:
         if isinstance(message, HumanMessage):
@@ -213,154 +80,21 @@ class CohereProvider(Provider):
             return "CHATBOT"
         elif isinstance(message, SystemMessage):
             return "SYSTEM"
-        elif isinstance(message, ToolMessage):
-            return "TOOL"
         else:
             raise ValueError(f"Got unknown type {message}")
 
-    def messages_to_oci_params(
-        self, messages: Sequence[ChatMessage], **kwargs: Any
-    ) -> Dict[str, Any]:
-        is_force_single_step = kwargs.get("is_force_single_step") or False
-
-        oci_chat_history = []
-
-        for msg in messages[:-1]:
-            if self.get_role(msg) == "USER" or self.get_role(msg) == "SYSTEM":
-                oci_chat_history.append(
-                    self.oci_chat_message[self.get_role(msg)](message=msg.content)
-                )
-            elif isinstance(msg, AIMessage):
-                if msg.tool_calls and is_force_single_step:
-                    continue
-                tool_calls = (
-                    [
-                        self.oci_tool_call(name=tc["name"], parameters=tc["args"])
-                        for tc in msg.tool_calls
-                    ]
-                    if msg.tool_calls
-                    else None
-                )
-                msg_content = msg.content if msg.content else " "
-                oci_chat_history.append(
-                    self.oci_chat_message[self.get_role(msg)](
-                        message=msg_content, tool_calls=tool_calls
-                    )
-                )
-
-        # Get the messages for the current chat turn
-        current_chat_turn_messages = []
-        for message in messages[::-1]:
-            current_chat_turn_messages.append(message)
-            if isinstance(message, HumanMessage):
-                break
-        current_chat_turn_messages = current_chat_turn_messages[::-1]
-
-        oci_tool_results: Union[List[Any], None] = []
-        for message in current_chat_turn_messages:
-            if isinstance(message, ToolMessage):
-                tool_message = message
-                previous_ai_msgs = [
-                    message
-                    for message in current_chat_turn_messages
-                    if isinstance(message, AIMessage) and message.tool_calls
-                ]
-                if previous_ai_msgs:
-                    previous_ai_msg = previous_ai_msgs[-1]
-                    for lc_tool_call in previous_ai_msg.tool_calls:
-                        if lc_tool_call["id"] == tool_message.tool_call_id:
-                            tool_result = self.oci_tool_result()
-                            tool_result.call = self.oci_tool_call(
-                                name=lc_tool_call["name"],
-                                parameters=lc_tool_call["args"],
-                            )
-                            tool_result.outputs = [{"output": tool_message.content}]
-                            oci_tool_results.append(tool_result)
-
-        if not oci_tool_results:
-            oci_tool_results = None
-
-        message_str = "" if oci_tool_results else messages[-1].content
-
+    def messages_to_oci_params(self, messages: Sequence[ChatMessage]) -> Dict[str, Any]:
+        oci_chat_history = [
+            self.oci_chat_message[self.get_role(msg)](message=msg.content)
+            for msg in messages[:-1]
+        ]
         oci_params = {
-            "message": message_str,
+            "message": messages[-1].content,
             "chat_history": oci_chat_history,
-            "tool_results": oci_tool_results,
             "api_format": self.chat_api_format,
         }
 
-        return {k: v for k, v in oci_params.items() if v is not None}
-
-    def convert_to_oci_tool(
-        self,
-        tool: Union[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
-    ) -> Dict[str, Any]:
-        """
-        Convert a BaseTool instance, JSON schema dict, or BaseModel type to a OCI tool.
-        """
-        if isinstance(tool, BaseTool):
-            return self.oci_tool(
-                name=tool.name,
-                description=_remove_signature_from_tool_description(
-                    tool.name, tool.description
-                ),
-                parameter_definitions={
-                    p_name: self.oci_tool_param(
-                        description=p_def.get("description")
-                        if "description" in p_def
-                        else "",
-                        type=JSON_TO_PYTHON_TYPES.get(
-                            p_def.get("type"), p_def.get("type")
-                        ),
-                        is_required="default" not in p_def,
-                    )
-                    for p_name, p_def in tool.args.items()
-                },
-            )
-        elif isinstance(tool, dict):
-            if not all(k in tool for k in ("title", "description", "properties")):
-                raise ValueError(
-                    "Unsupported dict type. Tool must be passed in as a BaseTool instance, JSON schema dict, or BaseModel type."  # noqa: E501
-                )
-            return self.oci_tool(
-                name=tool.get("title"),
-                description=tool.get("description"),
-                parameter_definitions={
-                    p_name: self.oci_tool_param(
-                        description=p_def.get("description"),
-                        type=JSON_TO_PYTHON_TYPES.get(
-                            p_def.get("type"), p_def.get("type")
-                        ),
-                        is_required="default" not in p_def,
-                    )
-                    for p_name, p_def in tool.get("properties", {}).items()
-                },
-            )
-        elif (isinstance(tool, type) and issubclass(tool, BaseModel)) or callable(tool):
-            as_json_schema_function = convert_to_openai_function(tool)
-            parameters = as_json_schema_function.get("parameters", {})
-            properties = parameters.get("properties", {})
-            return self.oci_tool(
-                name=as_json_schema_function.get("name"),
-                description=as_json_schema_function.get(
-                    "description",
-                    as_json_schema_function.get("name"),
-                ),
-                parameter_definitions={
-                    p_name: self.oci_tool_param(
-                        description=p_def.get("description"),
-                        type=JSON_TO_PYTHON_TYPES.get(
-                            p_def.get("type"), p_def.get("type")
-                        ),
-                        is_required=p_name in parameters.get("required", []),
-                    )
-                    for p_name, p_def in properties.items()
-                },
-            )
-        else:
-            raise ValueError(
-                f"Unsupported tool type {type(tool)}. Tool must be passed in as a BaseTool instance, JSON schema dict, or BaseModel type."  # noqa: E501
-            )
+        return oci_params
 
 
 class MetaProvider(Provider):
@@ -382,10 +116,10 @@ class MetaProvider(Provider):
         return response.data.chat_response.choices[0].message.content[0].text
 
     def chat_stream_to_text(self, event_data: Dict) -> str:
-        return event_data["message"]["content"][0]["text"]
-
-    def is_chat_stream_end(self, event_data: Dict) -> bool:
-        return "message" not in event_data
+        if "message" in event_data:
+            return event_data["message"]["content"][0]["text"]
+        else:
+            return ""
 
     def chat_generation_info(self, response: Any) -> Dict[str, Any]:
         return {
@@ -393,11 +127,6 @@ class MetaProvider(Provider):
             "time_created": str(response.data.chat_response.time_created),
         }
 
-    def chat_stream_generation_info(self, event_data: Dict) -> Dict[str, Any]:
-        return {
-            "finish_reason": event_data["finishReason"],
-        }
-
     def get_role(self, message: BaseMessage) -> str:
         # meta only supports alternating user/assistant roles
         if isinstance(message, HumanMessage):
@@ -409,9 +138,7 @@ class MetaProvider(Provider):
         else:
             raise ValueError(f"Got unknown type {message}")
 
-    def messages_to_oci_params(
-        self, messages: List[BaseMessage], **kwargs: Any
-    ) -> Dict[str, Any]:
+    def messages_to_oci_params(self, messages: List[BaseMessage]) -> Dict[str, Any]:
         oci_messages = [
             self.oci_chat_message[self.get_role(msg)](
                 content=[self.oci_chat_message_content(text=msg.content)]
@@ -426,12 +153,6 @@ class MetaProvider(Provider):
 
         return oci_params
 
-    def convert_to_oci_tool(
-        self,
-        tool: Union[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
-    ) -> Dict[str, Any]:
-        raise NotImplementedError("Tools not supported for Meta models")
-
 
 class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
     """ChatOCIGenAI chat model integration.
@@ -526,8 +247,8 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
         self,
         messages: List[BaseMessage],
         stop: Optional[List[str]],
+        kwargs: Dict[str, Any],
         stream: bool,
-        **kwargs: Any,
     ) -> Dict[str, Any]:
         try:
             from oci.generative_ai_inference import models
@@ -537,10 +258,8 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
                 "Could not import oci python package. "
                 "Please make sure you have the oci package installed."
             ) from ex
-
-        oci_params = self._provider.messages_to_oci_params(messages, **kwargs)
-
-        oci_params["is_stream"] = stream
+        oci_params = self._provider.messages_to_oci_params(messages)
+        oci_params["is_stream"] = stream  # self.is_stream
         _model_kwargs = self.model_kwargs or {}
 
         if stop is not None:
@@ -561,43 +280,6 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
 
         return request
 
-    def bind_tools(
-        self,
-        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
-        **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
-        formatted_tools = [self._provider.convert_to_oci_tool(tool) for tool in tools]
-        return super().bind(tools=formatted_tools, **kwargs)
-
-    def with_structured_output(
-        self,
-        schema: Union[Dict[Any, Any], Type[BaseModel]],
-        **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
-        """Model wrapper that returns outputs formatted to match the given schema.
-
-        Args:
-            schema: The output schema as a dict or a Pydantic class. If a Pydantic class
-                then the model output will be an object of that class. If a dict then
-                the model output will be a dict.
-
-        Returns:
-            A Runnable that takes any ChatModel input and returns either a dict or
-            Pydantic class as output.
-        """
-        llm = self.bind_tools([schema], **kwargs)
-        if isinstance(schema, type) and issubclass(schema, BaseModel):
-            output_parser: OutputParserLike = PydanticToolsParser(
-                tools=[schema], first_tool_only=True
-            )
-        else:
-            key_name = getattr(self._provider.convert_to_oci_tool(schema), "name")
-            output_parser = JsonOutputKeyToolsParser(
-                key_name=key_name, first_tool_only=True
-            )
-
-        return llm | output_parser
-
     def _generate(
         self,
         messages: List[BaseMessage],
@@ -631,7 +313,7 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
             )
             return generate_from_stream(stream_iter)
 
-        request = self._prepare_request(messages, stop=stop, stream=False, **kwargs)
+        request = self._prepare_request(messages, stop, kwargs, stream=False)
         response = self.client.chat(request)
 
         content = self._provider.chat_response_to_text(response)
@@ -648,22 +330,11 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
             "content-length": response.headers["content-length"],
         }
 
-        if "tool_calls" in generation_info:
-            tool_calls = [
-                _convert_oci_tool_call_to_langchain(tool_call)
-                for tool_call in response.data.chat_response.tool_calls
-            ]
-        else:
-            tool_calls = []
-
-        message = AIMessage(
-            content=content,
-            additional_kwargs=generation_info,
-            tool_calls=tool_calls,
-        )
         return ChatResult(
             generations=[
-                ChatGeneration(message=message, generation_info=generation_info)
+                ChatGeneration(
+                    message=AIMessage(content=content), generation_info=generation_info
+                )
             ],
             llm_output=llm_output,
         )
@@ -675,42 +346,12 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
-        request = self._prepare_request(messages, stop=stop, stream=True, **kwargs)
+        request = self._prepare_request(messages, stop, kwargs, stream=True)
         response = self.client.chat(request)
 
         for event in response.data.events():
-            event_data = json.loads(event.data)
-            if not self._provider.is_chat_stream_end(event_data):  # still streaming
-                delta = self._provider.chat_stream_to_text(event_data)
-                chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
-                if run_manager:
-                    run_manager.on_llm_new_token(delta, chunk=chunk)
-                yield chunk
-            else:  # stream end
-                generation_info = self._provider.chat_stream_generation_info(event_data)
-                tool_call_chunks = []
-                if tool_calls := generation_info.get("tool_calls"):
-                    content = self._provider.chat_stream_to_text(event_data)
-                    try:
-                        tool_call_chunks = [
-                            ToolCallChunk(
-                                name=tool_call["function"].get("name"),
-                                args=tool_call["function"].get("arguments"),
-                                id=tool_call.get("id"),
-                                index=tool_call.get("index"),
-                            )
-                            for tool_call in tool_calls
-                        ]
-                    except KeyError:
-                        pass
-                else:
-                    content = ""
-                message = AIMessageChunk(
-                    content=content,
-                    additional_kwargs=generation_info,
-                    tool_call_chunks=tool_call_chunks,
-                )
-                yield ChatGenerationChunk(
-                    message=message,
-                    generation_info=generation_info,
-                )
+            delta = self._provider.chat_stream_to_text(json.loads(event.data))
+            chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
+            if run_manager:
+                run_manager.on_llm_new_token(delta, chunk=chunk)
+            yield chunk

libs/community/langchain_community/chat_models/yi.py

@@ -1,339 +0,0 @@
-import json
-import logging
-from contextlib import asynccontextmanager
-from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, Type
-
-import requests
-from langchain_core.callbacks import (
-    AsyncCallbackManagerForLLMRun,
-    CallbackManagerForLLMRun,
-)
-from langchain_core.language_models.chat_models import (
-    BaseChatModel,
-    agenerate_from_stream,
-    generate_from_stream,
-)
-from langchain_core.messages import (
-    AIMessage,
-    AIMessageChunk,
-    BaseMessage,
-    BaseMessageChunk,
-    ChatMessage,
-    ChatMessageChunk,
-    HumanMessage,
-    HumanMessageChunk,
-    SystemMessage,
-)
-from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import Field, SecretStr
-from langchain_core.utils import (
-    convert_to_secret_str,
-    get_from_dict_or_env,
-    get_pydantic_field_names,
-)
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_API_BASE_CN = "https://api.lingyiwanwu.com/v1/chat/completions"
-DEFAULT_API_BASE_GLOBAL = "https://api.01.ai/v1/chat/completions"
-
-
-def _convert_message_to_dict(message: BaseMessage) -> dict:
-    message_dict: Dict[str, Any]
-    if isinstance(message, ChatMessage):
-        message_dict = {"role": message.role, "content": message.content}
-    elif isinstance(message, HumanMessage):
-        message_dict = {"role": "user", "content": message.content}
-    elif isinstance(message, AIMessage):
-        message_dict = {"role": "assistant", "content": message.content}
-    elif isinstance(message, SystemMessage):
-        message_dict = {"role": "assistant", "content": message.content}
-    else:
-        raise TypeError(f"Got unknown type {message}")
-
-    return message_dict
-
-
-def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
-    role = _dict["role"]
-    if role == "user":
-        return HumanMessage(content=_dict["content"])
-    elif role == "assistant":
-        return AIMessage(content=_dict.get("content", "") or "")
-    elif role == "system":
-        return AIMessage(content=_dict["content"])
-    else:
-        return ChatMessage(content=_dict["content"], role=role)
-
-
-def _convert_delta_to_message_chunk(
-    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
-) -> BaseMessageChunk:
-    role: str = _dict["role"]
-    content = _dict.get("content") or ""
-
-    if role == "user" or default_class == HumanMessageChunk:
-        return HumanMessageChunk(content=content)
-    elif role == "assistant" or default_class == AIMessageChunk:
-        return AIMessageChunk(content=content)
-    elif role or default_class == ChatMessageChunk:
-        return ChatMessageChunk(content=content, role=role)
-    else:
-        return default_class(content=content, type=role)
-
-
-@asynccontextmanager
-async def aconnect_httpx_sse(
-    client: Any, method: str, url: str, **kwargs: Any
-) -> AsyncIterator:
-    from httpx_sse import EventSource
-
-    async with client.stream(method, url, **kwargs) as response:
-        yield EventSource(response)
-
-
-class ChatYi(BaseChatModel):
-    """Yi chat models API."""
-
-    @property
-    def lc_secrets(self) -> Dict[str, str]:
-        return {
-            "yi_api_key": "YI_API_KEY",
-        }
-
-    @property
-    def lc_serializable(self) -> bool:
-        return True
-
-    yi_api_base: str = Field(default=DEFAULT_API_BASE_CN)
-    yi_api_key: SecretStr = Field(alias="api_key")
-    region: str = Field(default="cn")  # 默认使用中国区
-    streaming: bool = False
-    request_timeout: int = Field(default=60, alias="timeout")
-    model: str = "yi-large"
-    temperature: Optional[float] = Field(default=0.7)
-    top_p: float = 0.7
-    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-
-    class Config:
-        allow_population_by_field_name = True
-
-    def __init__(self, **kwargs: Any) -> None:
-        kwargs["yi_api_key"] = convert_to_secret_str(
-            get_from_dict_or_env(
-                kwargs,
-                ["yi_api_key", "api_key"],
-                "YI_API_KEY",
-            )
-        )
-        if kwargs.get("yi_api_base") is None:
-            region = kwargs.get("region", "cn").lower()
-            if region == "global":
-                kwargs["yi_api_base"] = DEFAULT_API_BASE_GLOBAL
-            else:
-                kwargs["yi_api_base"] = DEFAULT_API_BASE_CN
-
-        all_required_field_names = get_pydantic_field_names(self.__class__)
-        extra = kwargs.get("model_kwargs", {})
-        for field_name in list(kwargs):
-            if field_name in extra:
-                raise ValueError(f"Found {field_name} supplied twice.")
-            if field_name not in all_required_field_names:
-                extra[field_name] = kwargs.pop(field_name)
-
-        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
-        if invalid_model_kwargs:
-            raise ValueError(
-                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
-                f"Instead they were passed in as part of `model_kwargs` parameter."
-            )
-
-        kwargs["model_kwargs"] = extra
-        super().__init__(**kwargs)
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        return {
-            "model": self.model,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "stream": self.streaming,
-        }
-
-    def _generate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        if self.streaming:
-            stream_iter = self._stream(
-                messages=messages, stop=stop, run_manager=run_manager, **kwargs
-            )
-            return generate_from_stream(stream_iter)
-
-        res = self._chat(messages, **kwargs)
-        if res.status_code != 200:
-            raise ValueError(f"Error from Yi api response: {res}")
-        response = res.json()
-        return self._create_chat_result(response)
-
-    def _stream(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> Iterator[ChatGenerationChunk]:
-        res = self._chat(messages, stream=True, **kwargs)
-        if res.status_code != 200:
-            raise ValueError(f"Error from Yi api response: {res}")
-        default_chunk_class = AIMessageChunk
-        for chunk in res.iter_lines():
-            chunk = chunk.decode("utf-8").strip("\r\n")
-            parts = chunk.split("data: ", 1)
-            chunk = parts[1] if len(parts) > 1 else None
-            if chunk is None:
-                continue
-            if chunk == "[DONE]":
-                break
-            response = json.loads(chunk)
-            for m in response.get("choices"):
-                chunk = _convert_delta_to_message_chunk(
-                    m.get("delta"), default_chunk_class
-                )
-                default_chunk_class = chunk.__class__
-                cg_chunk = ChatGenerationChunk(message=chunk)
-                if run_manager:
-                    run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk)
-                yield cg_chunk
-
-    async def _agenerate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        stream: Optional[bool] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        should_stream = stream if stream is not None else self.streaming
-        if should_stream:
-            stream_iter = self._astream(
-                messages, stop=stop, run_manager=run_manager, **kwargs
-            )
-            return await agenerate_from_stream(stream_iter)
-
-        headers = self._create_headers_parameters(**kwargs)
-        payload = self._create_payload_parameters(messages, **kwargs)
-
-        import httpx
-
-        async with httpx.AsyncClient(
-            headers=headers, timeout=self.request_timeout
-        ) as client:
-            response = await client.post(self.yi_api_base, json=payload)
-            response.raise_for_status()
-        return self._create_chat_result(response.json())
-
-    async def _astream(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> AsyncIterator[ChatGenerationChunk]:
-        headers = self._create_headers_parameters(**kwargs)
-        payload = self._create_payload_parameters(messages, stream=True, **kwargs)
-        import httpx
-
-        async with httpx.AsyncClient(
-            headers=headers, timeout=self.request_timeout
-        ) as client:
-            async with aconnect_httpx_sse(
-                client, "POST", self.yi_api_base, json=payload
-            ) as event_source:
-                async for sse in event_source.aiter_sse():
-                    chunk = json.loads(sse.data)
-                    if len(chunk["choices"]) == 0:
-                        continue
-                    choice = chunk["choices"][0]
-                    chunk = _convert_delta_to_message_chunk(
-                        choice["delta"], AIMessageChunk
-                    )
-                    finish_reason = choice.get("finish_reason", None)
-
-                    generation_info = (
-                        {"finish_reason": finish_reason}
-                        if finish_reason is not None
-                        else None
-                    )
-                    chunk = ChatGenerationChunk(
-                        message=chunk, generation_info=generation_info
-                    )
-                    if run_manager:
-                        await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
-                    yield chunk
-                    if finish_reason is not None:
-                        break
-
-    def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
-        payload = self._create_payload_parameters(messages, **kwargs)
-        url = self.yi_api_base
-        headers = self._create_headers_parameters(**kwargs)
-
-        res = requests.post(
-            url=url,
-            timeout=self.request_timeout,
-            headers=headers,
-            json=payload,
-            stream=self.streaming,
-        )
-        return res
-
-    def _create_payload_parameters(
-        self, messages: List[BaseMessage], **kwargs: Any
-    ) -> Dict[str, Any]:
-        parameters = {**self._default_params, **kwargs}
-        temperature = parameters.pop("temperature", 0.7)
-        top_p = parameters.pop("top_p", 0.7)
-        model = parameters.pop("model")
-        stream = parameters.pop("stream", False)
-
-        payload = {
-            "model": model,
-            "messages": [_convert_message_to_dict(m) for m in messages],
-            "top_p": top_p,
-            "temperature": temperature,
-            "stream": stream,
-        }
-        return payload
-
-    def _create_headers_parameters(self, **kwargs: Any) -> Dict[str, Any]:
-        parameters = {**self._default_params, **kwargs}
-        default_headers = parameters.pop("headers", {})
-        api_key = ""
-        if self.yi_api_key:
-            api_key = self.yi_api_key.get_secret_value()
-
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}",
-            **default_headers,
-        }
-        return headers
-
-    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
-        generations = []
-        for c in response["choices"]:
-            message = _convert_dict_to_message(c["message"])
-            gen = ChatGeneration(message=message)
-            generations.append(gen)
-
-        token_usage = response["usage"]
-        llm_output = {"token_usage": token_usage, "model": self.model}
-        return ChatResult(generations=generations, llm_output=llm_output)
-
-    @property
-    def _llm_type(self) -> str:
-        return "yi-chat"

libs/community/langchain_community/document_compressors/flashrank_rerank.py

@@ -26,12 +26,8 @@ class FlashrankRerank(BaseDocumentCompressor):
     """Flashrank client to use for compressing documents"""
     top_n: int = 3
     """Number of documents to return."""
-    score_threshold: float = 0.0
-    """Minimum relevance threshold to return."""
     model: Optional[str] = None
     """Model to use for reranking."""
-    prefix_metadata: str = ""
-    """Prefix for flashrank_rerank metadata keys"""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -73,14 +69,11 @@ class FlashrankRerank(BaseDocumentCompressor):
         final_results = []
 
         for r in rerank_response:
-            if r["score"] >= self.score_threshold:
-                doc = Document(
-                    page_content=r["text"],
-                    metadata={
-                        self.prefix_metadata + "id": r["id"],
-                        self.prefix_metadata + "relevance_score": r["score"],
-                        **r["meta"],
-                    },
-                )
-                final_results.append(doc)
+            metadata = r["meta"]
+            metadata["relevance_score"] = r["score"]
+            doc = Document(
+                page_content=r["text"],
+                metadata=metadata,
+            )
+            final_results.append(doc)
         return final_results

libs/community/langchain_community/document_loaders/firecrawl.py

@@ -17,7 +17,6 @@ class FireCrawlLoader(BaseLoader):
         url: str,
         *,
         api_key: Optional[str] = None,
-        api_url: Optional[str] = None,
         mode: Literal["crawl", "scrape"] = "crawl",
         params: Optional[dict] = None,
     ):
@@ -27,8 +26,6 @@ class FireCrawlLoader(BaseLoader):
             url: The url to be crawled.
             api_key: The Firecrawl API key. If not specified will be read from env var
                 FIRECRAWL_API_KEY. Get an API key
-            api_url: The Firecrawl API URL. If not specified will be read from env var
-                FIRECRAWL_API_URL or defaults to https://api.firecrawl.dev.
             mode: The mode to run the loader in. Default is "crawl".
                  Options include "scrape" (single url) and
                  "crawl" (all accessible sub pages).
@@ -48,7 +45,7 @@ class FireCrawlLoader(BaseLoader):
                 f"Unrecognized mode '{mode}'. Expected one of 'crawl', 'scrape'."
             )
         api_key = api_key or get_from_env("api_key", "FIRECRAWL_API_KEY")
-        self.firecrawl = FirecrawlApp(api_key=api_key, api_url=api_url)
+        self.firecrawl = FirecrawlApp(api_key=api_key)
         self.url = url
         self.mode = mode
         self.params = params

libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py

@@ -11,45 +11,12 @@ logger = logging.getLogger(__name__)
 
 
 class QianfanEmbeddingsEndpoint(BaseModel, Embeddings):
-    """Baidu Qianfan Embeddings embedding models.
+    """`Baidu Qianfan Embeddings` embedding models."""
 
-    Setup:
-        To use, you should have the ``qianfan`` python package installed, and set
-        environment variables ``QIANFAN_AK``, ``QIANFAN_SK``.
-
-        .. code-block:: bash
-
-            pip install qianfan
-            export QIANFAN_AK="your-api-key"
-            export QIANFAN_SK="your-secret_key"
-
-    Instantiate:
-        .. code-block:: python
-
-            from langchain_community.embeddings import QianfanEmbeddingsEndpoint
-
-            embeddings = QianfanEmbeddingsEndpoint()
-
-     Embed:
-        .. code-block:: python
-
-            # embed the documents
-            vectors = embeddings.embed_documents([text1, text2, ...])
-
-            # embed the query
-            vectors = embeddings.embed_query(text)
-
-            # embed the documents with async
-            vectors = await embeddings.aembed_documents([text1, text2, ...])
-
-            # embed the query with async
-            vectors = await embeddings.aembed_query(text)
-    """  # noqa: E501
-
-    qianfan_ak: Optional[SecretStr] = Field(default=None, alias="api_key")
+    qianfan_ak: Optional[SecretStr] = None
     """Qianfan application apikey"""
 
-    qianfan_sk: Optional[SecretStr] = Field(default=None, alias="secret_key")
+    qianfan_sk: Optional[SecretStr] = None
     """Qianfan application secretkey"""
 
     chunk_size: int = 16

libs/community/langchain_community/embeddings/bedrock.py

@@ -4,17 +4,11 @@ import os
 from typing import Any, Dict, List, Optional
 
 import numpy as np
-from langchain_core._api.deprecation import deprecated
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain_core.runnables.config import run_in_executor
 
 
-@deprecated(
-    since="0.2.11",
-    removal="0.4.0",
-    alternative_import="langchain_aws.BedrockEmbeddings",
-)
 class BedrockEmbeddings(BaseModel, Embeddings):
     """Bedrock embedding models.
 

libs/community/langchain_community/embeddings/dashscope.py

@@ -48,11 +48,8 @@ def embed_with_retry(embeddings: DashScopeEmbeddings, **kwargs: Any) -> Any:
         result = []
         i = 0
         input_data = kwargs["input"]
-        input_len = len(input_data) if isinstance(input_data, list) else 1
-        while i < input_len:
-            kwargs["input"] = (
-                input_data[i : i + 25] if isinstance(input_data, list) else input_data
-            )
+        while i < len(input_data):
+            kwargs["input"] = input_data[i : i + 25]
             resp = embeddings.client.call(**kwargs)
             if resp.status_code == 200:
                 result += resp.output["embeddings"]

libs/community/langchain_community/embeddings/fastembed.py

@@ -1,5 +1,3 @@
-import importlib
-import importlib.metadata
 from typing import Any, Dict, List, Literal, Optional
 
 import numpy as np
@@ -7,8 +5,6 @@ from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra
 from langchain_core.utils import pre_init
 
-MIN_VERSION = "0.2.0"
-
 
 class FastEmbedEmbeddings(BaseModel, Embeddings):
     """Qdrant FastEmbedding models.
@@ -52,24 +48,12 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
     The available options are: "default" and "passage"
     """
 
-    batch_size: int = 256
-    """Batch size for encoding. Higher values will use more memory, but be faster.
-    Defaults to 256.
-    """
-
-    parallel: Optional[int] = None
-    """If `>1`, parallel encoding is used, recommended for encoding of large datasets.
-    If `0`, use all available cores.
-    If `None`, don't use data-parallel processing, use default onnxruntime threading.
-    Defaults to `None`.
-    """
-
     _model: Any  # : :meta private:
 
     class Config:
         """Configuration for this pydantic object."""
 
-        extra = Extra.allow
+        extra = Extra.forbid
 
     @pre_init
     def validate_environment(cls, values: Dict) -> Dict:
@@ -80,25 +64,31 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
         threads = values.get("threads")
 
         try:
-            fastembed = importlib.import_module("fastembed")
+            # >= v0.2.0
+            from fastembed import TextEmbedding
 
-        except ModuleNotFoundError:
-            raise ImportError(
-                "Could not import 'fastembed' Python package. "
-                "Please install it with `pip install fastembed`."
+            values["_model"] = TextEmbedding(
+                model_name=model_name,
+                max_length=max_length,
+                cache_dir=cache_dir,
+                threads=threads,
             )
+        except ImportError as ie:
+            try:
+                # < v0.2.0
+                from fastembed.embedding import FlagEmbedding
 
-        if importlib.metadata.version("fastembed") < MIN_VERSION:
-            raise ImportError(
-                'FastEmbedEmbeddings requires `pip install -U "fastembed>=0.2.0"`.'
-            )
-
-        values["_model"] = fastembed.TextEmbedding(
-            model_name=model_name,
-            max_length=max_length,
-            cache_dir=cache_dir,
-            threads=threads,
-        )
+                values["_model"] = FlagEmbedding(
+                    model_name=model_name,
+                    max_length=max_length,
+                    cache_dir=cache_dir,
+                    threads=threads,
+                )
+            except ImportError:
+                raise ImportError(
+                    "Could not import 'fastembed' Python package. "
+                    "Please install it with `pip install fastembed`."
+                ) from ie
         return values
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
@@ -112,13 +102,9 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
         """
         embeddings: List[np.ndarray]
         if self.doc_embed_type == "passage":
-            embeddings = self._model.passage_embed(
-                texts, batch_size=self.batch_size, parallel=self.parallel
-            )
+            embeddings = self._model.passage_embed(texts)
         else:
-            embeddings = self._model.embed(
-                texts, batch_size=self.batch_size, parallel=self.parallel
-            )
+            embeddings = self._model.embed(texts)
         return [e.tolist() for e in embeddings]
 
     def embed_query(self, text: str) -> List[float]:
@@ -130,9 +116,5 @@ class FastEmbedEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        query_embeddings: np.ndarray = next(
-            self._model.query_embed(
-                text, batch_size=self.batch_size, parallel=self.parallel
-            )
-        )
+        query_embeddings: np.ndarray = next(self._model.query_embed(text))
         return query_embeddings.tolist()