take usage from end of stream

Incompatibility between langchain-redis and langchain-ai21 `tenacity`
dep

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,8 +1,8 @@
 Thank you for contributing to LangChain!
 
 - [ ] **PR title**: "package: description"
-  - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
-  - Example: "community: add foobar LLM"
+  - Where "package" is whichever of langchain, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
+  - Example: "core: add foobar LLM"
 
 
 - [ ] **PR message**: ***Delete this entire checklist*** and replace with
@@ -24,6 +24,5 @@ Additional guidelines:
 - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests.
 - Most PRs should not touch more than one package.
 - Changes should be backwards compatible.
-- If you are adding something to community, do not re-import it in langchain.
 
 If no one reviews your PR within a few days, please @-mention one of baskaryan, eyurtsev, ccurme, vbarda, hwchase17.

.github/scripts/check_diff.py

@@ -16,7 +16,6 @@ LANGCHAIN_DIRS = [
     "libs/core",
     "libs/text-splitters",
     "libs/langchain",
-    "libs/community",
 ]
 
 # when set to True, we are ignoring core dependents
@@ -38,8 +37,8 @@ IGNORED_PARTNERS = [
 ]
 
 PY_312_MAX_PACKAGES = [
-    "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
     "libs/partners/voyageai",
+    "libs/partners/chroma", # https://github.com/chroma-core/chroma/issues/4382
 ]
 
 
@@ -134,12 +133,6 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ == "libs/langchain" and job == "extended-tests":
         py_versions = ["3.9", "3.13"]
 
-    elif dir_ == "libs/community" and job == "extended-tests":
-        py_versions = ["3.9", "3.12"]
-
-    elif dir_ == "libs/community" and job == "compile-integration-tests":
-        # community integration deps are slow in 3.12
-        py_versions = ["3.9", "3.11"]
     elif dir_ == ".":
         # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
         py_versions = ["3.9", "3.12"]
@@ -184,11 +177,6 @@ def _get_pydantic_test_configs(
         else "0"
     )
 
-    custom_mins = {
-        # depends on pydantic-settings 2.4 which requires pydantic 2.7
-        "libs/community": 7,
-    }
-
     max_pydantic_minor = min(
         int(dir_max_pydantic_minor),
         int(core_max_pydantic_minor),
@@ -196,7 +184,6 @@ def _get_pydantic_test_configs(
     min_pydantic_minor = max(
         int(dir_min_pydantic_minor),
         int(core_min_pydantic_minor),
-        custom_mins.get(dir_, 0),
     )
 
     configs = [

.github/scripts/get_min_versions.py

@@ -22,7 +22,6 @@ import re
 
 MIN_VERSION_LIBS = [
     "langchain-core",
-    "langchain-community",
     "langchain",
     "langchain-text-splitters",
     "numpy",
@@ -35,7 +34,6 @@ SKIP_IF_PULL_REQUEST = [
     "langchain-core",
     "langchain-text-splitters",
     "langchain",
-    "langchain-community",
 ]
 
 

.github/scripts/prep_api_docs_build.py

@@ -20,6 +20,8 @@ def get_target_dir(package_name: str) -> Path:
     base_path = Path("langchain/libs")
     if package_name_short == "experimental":
         return base_path / "experimental"
+    if package_name_short == "community":
+        return base_path / "community"
     return base_path / "partners" / package_name_short
 
 
@@ -69,7 +71,7 @@ def main():
         clean_target_directories([
             p
             for p in package_yaml["packages"]
-            if p["repo"].startswith("langchain-ai/")
+            if (p["repo"].startswith("langchain-ai/") or p.get("include_in_api_ref"))
             and p["repo"] != "langchain-ai/langchain"
         ])
 
@@ -78,7 +80,7 @@ def main():
             p
             for p in package_yaml["packages"]
             if not p.get("disabled", False)
-            and p["repo"].startswith("langchain-ai/")
+            and (p["repo"].startswith("langchain-ai/") or p.get("include_in_api_ref"))
             and p["repo"] != "langchain-ai/langchain"
         ])
 

.github/workflows/.codespell-exclude

@@ -1,4 +1,3 @@
-libs/community/langchain_community/llms/yuan2.py
 "NotIn": "not in",
 - `/checkin`: Check-in
 docs/docs/integrations/providers/trulens.mdx

.github/workflows/_integration_test.yml

@@ -34,11 +34,6 @@ jobs:
         shell: bash
         run: uv sync --group test --group test_integration
 
-      - name: Install deps outside pyproject
-        if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
-        shell: bash
-        run: VIRTUAL_ENV=.venv uv pip install "boto3<2" "google-cloud-aiplatform<2"
-
       - name: Run integration tests
         shell: bash
         env:

.github/workflows/_test_doc_imports.yml

@@ -30,7 +30,7 @@ jobs:
 
       - name: Install langchain editable
         run: |
-          VIRTUAL_ENV=.venv uv pip install langchain-experimental -e libs/core libs/langchain libs/community
+          VIRTUAL_ENV=.venv uv pip install langchain-experimental langchain-community -e libs/core libs/langchain
 
       - name: Check doc imports
         shell: bash

.github/workflows/api_doc_build.yml

@@ -26,7 +26,20 @@ jobs:
         id: get-unsorted-repos
         uses: mikefarah/yq@master
         with:
-          cmd: yq '.packages[].repo' langchain/libs/packages.yml
+          cmd: |
+            yq '
+              .packages[]
+              | select(
+                  (
+                    (.repo | test("^langchain-ai/"))
+                    and
+                    (.repo != "langchain-ai/langchain")
+                  )
+                  or
+                  (.include_in_api_ref // false)
+                )
+              | .repo
+            ' langchain/libs/packages.yml
 
       - name: Parse YAML and checkout repos
         env:
@@ -38,11 +51,9 @@ jobs:
           
           # Checkout each unique repository that is in langchain-ai org
           for repo in $REPOS; do
-            if [[ "$repo" != "langchain-ai/langchain" && "$repo" == langchain-ai/* ]]; then
-              REPO_NAME=$(echo $repo | cut -d'/' -f2)
-              echo "Checking out $repo to $REPO_NAME"
-              git clone --depth 1 https://github.com/$repo.git $REPO_NAME
-            fi
+            REPO_NAME=$(echo $repo | cut -d'/' -f2)
+            echo "Checking out $repo to $REPO_NAME"
+            git clone --depth 1 https://github.com/$repo.git $REPO_NAME
           done
 
       - name: Setup python ${{ env.PYTHON_VERSION }}

.pre-commit-config.yaml

@@ -7,12 +7,6 @@ repos:
       entry: make -C libs/core format
       files: ^libs/core/
       pass_filenames: false
-    - id: community
-      name: format community
-      language: system
-      entry: make -C libs/community format
-      files: ^libs/community/
-      pass_filenames: false
     - id: langchain
       name: format langchain
       language: system

docs/cassettes/configure_093d1c7d-1a64-4e6a-849f-075526b9b3ca.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/configure_277e3232-9b77-4828-8082-b62f4d97127f.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/configure_42647fb7.msgpack.zlib

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docs/cassettes/configure_48b45337.msgpack.zlib

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docs/cassettes/configure_4f83245c.msgpack.zlib

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docs/cassettes/configure_5cf53202.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/configure_61a67769-4a15-49e2-a945-1f4e7ef19d8c.msgpack.zlib

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docs/cassettes/configure_71248a9f.msgpack.zlib

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docs/cassettes/configure_7ba735f4.msgpack.zlib

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docs/cassettes/configure_927297a1.msgpack.zlib

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docs/cassettes/configure_97538c23.msgpack.zlib

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docs/cassettes/configure_9a9ea077.msgpack.zlib

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docs/cassettes/configure_9f6a7c6c.msgpack.zlib

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docs/cassettes/configure_c09fac15.msgpack.zlib

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docs/cassettes/configure_e4ee9fbc.msgpack.zlib

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docs/cassettes/configure_e75ae678.msgpack.zlib

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docs/cassettes/configure_f33f3cf2.msgpack.zlib

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docs/cassettes/sql_qa_11.msgpack.zlib

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docs/cassettes/sql_qa_39.msgpack.zlib

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docs/cassettes/tools_chain_4f5325ca-e5dc-4d1a-ba36-b085a029c90a.msgpack.zlib

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docs/cassettes/tools_chain_68f30343-14ef-48f1-badd-b6a03977316d.msgpack.zlib

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docs/cassettes/tools_chain_6be83879-9da3-4dd9-b147-a79f76affd7a.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/tools_chain_f7dbb240-809e-4e41-8f63-1a4636e8e26d.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/docs/concepts/multimodality.mdx

@@ -107,7 +107,7 @@ outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessag
 response object. See for example:
 
 - Generating [audio outputs](/docs/integrations/chat/openai/#audio-generation-preview) with OpenAI;
-- Generating [image outputs](/docs/integrations/chat/google_generative_ai/#image-generation) with Google Gemini.
+- Generating [image outputs](/docs/integrations/chat/google_generative_ai/#multimodal-usage) with Google Gemini.
 
 #### Tools
 

docs/docs/concepts/vectorstores.mdx

@@ -66,7 +66,7 @@ This API works with a list of [Document](https://python.langchain.com/api_refere
 from langchain_core.documents import Document
 
 document_1 = Document(
-    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
+    page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
     metadata={"source": "tweet"},
 )
 

docs/docs/contributing/how_to/code/setup.mdx

@@ -13,23 +13,33 @@ Install `uv`: **[documentation on how to install it](https://docs.astral.sh/uv/g
 
 This repository contains multiple packages:
 - `langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
-- `langchain-community`: Third-party integrations of various components.
 - `langchain`: Chains, agents, and retrieval logic that makes up the cognitive architecture of your applications.
-- `langchain-experimental`: Components and chains that are experimental, either in the sense that the techniques are novel and still being tested, or they require giving the LLM more access than would be possible in most production systems.
 - Partner integrations: Partner packages in `libs/partners` that are independently version controlled.
 
+:::note
+
+Some LangChain packages live outside the monorepo, see for example
+[langchain-community](https://github.com/langchain-ai/langchain-community) for various
+third-party integrations and
+[langchain-experimental](https://github.com/langchain-ai/langchain-experimental) for
+abstractions that are experimental (either in the sense that the techniques are novel
+and still being tested, or they require giving the LLM more access than would be
+possible in most production systems).
+
+:::
+
 Each of these has its own development environment. Docs are run from the top-level makefile, but development
 is split across separate test & release flows.
 
-For this quickstart, start with langchain-community:
+For this quickstart, start with `langchain`:
 
 ```bash
-cd libs/community
+cd libs/langchain
 ```
 
 ## Local Development Dependencies
 
-Install langchain-community development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
+Install development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
 
 ```bash
 uv sync
@@ -62,22 +72,15 @@ make docker_tests
 
 There are also [integration tests and code-coverage](../testing.mdx) available.
 
-### Only develop langchain_core or langchain_community
+### Developing langchain_core
 
-If you are only developing `langchain_core` or `langchain_community`, you can simply install the dependencies for the respective projects and run tests:
+If you are only developing `langchain_core`, you can simply install the dependencies for the project and run tests:
 
 ```bash
 cd libs/core
 make test
 ```
 
-Or:
-
-```bash
-cd libs/community
-make test
-```
-
 ## Formatting and Linting
 
 Run these locally before submitting a PR; the CI system will check also.

docs/docs/how_to/configure.ipynb

@@ -336,70 +336,6 @@
     "chain.with_config(configurable={\"llm_temperature\": 0.9}).invoke({\"x\": 0})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fb9637d0",
-   "metadata": {},
-   "source": [
-    "### With HubRunnables\n",
-    "\n",
-    "This is useful to allow for switching of prompts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9a9ea077",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ChatPromptValue(messages=[HumanMessage(content=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: foo \\nContext: bar \\nAnswer:\")])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.runnables.hub import HubRunnable\n",
-    "\n",
-    "prompt = HubRunnable(\"rlm/rag-prompt\").configurable_fields(\n",
-    "    owner_repo_commit=ConfigurableField(\n",
-    "        id=\"hub_commit\",\n",
-    "        name=\"Hub Commit\",\n",
-    "        description=\"The Hub commit to pull from\",\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "prompt.invoke({\"question\": \"foo\", \"context\": \"bar\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f33f3cf2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ChatPromptValue(messages=[HumanMessage(content=\"[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \\nQuestion: foo \\nContext: bar \\nAnswer: [/INST]\")])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prompt.with_config(configurable={\"hub_commit\": \"rlm/rag-prompt-llama\"}).invoke(\n",
-    "    {\"question\": \"foo\", \"context\": \"bar\"}\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "79d51519",

docs/docs/how_to/custom_tools.ipynb

@@ -530,7 +530,7 @@
     "\n",
     "    def _run(\n",
     "        self, a: int, b: int, run_manager: Optional[CallbackManagerForToolRun] = None\n",
-    "    ) -> str:\n",
+    "    ) -> int:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
     "        return a * b\n",
     "\n",
@@ -539,7 +539,7 @@
     "        a: int,\n",
     "        b: int,\n",
     "        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,\n",
-    "    ) -> str:\n",
+    "    ) -> int:\n",
     "        \"\"\"Use the tool asynchronously.\"\"\"\n",
     "        # If the calculation is cheap, you can just delegate to the sync implementation\n",
     "        # as shown below.\n",

docs/docs/how_to/graph_constructing.ipynb

@@ -167,7 +167,7 @@
     "She was, in 1906, the first woman to become a professor at the University of Paris.\n",
     "\"\"\"\n",
     "documents = [Document(page_content=text)]\n",
-    "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n",
+    "graph_documents = await llm_transformer.aconvert_to_graph_documents(documents)\n",
     "print(f\"Nodes:{graph_documents[0].nodes}\")\n",
     "print(f\"Relationships:{graph_documents[0].relationships}\")"
    ]
@@ -205,7 +205,7 @@
     "    allowed_nodes=[\"Person\", \"Country\", \"Organization\"],\n",
     "    allowed_relationships=[\"NATIONALITY\", \"LOCATED_IN\", \"WORKED_AT\", \"SPOUSE\"],\n",
     ")\n",
-    "graph_documents_filtered = llm_transformer_filtered.convert_to_graph_documents(\n",
+    "graph_documents_filtered = await llm_transformer_filtered.aconvert_to_graph_documents(\n",
     "    documents\n",
     ")\n",
     "print(f\"Nodes:{graph_documents_filtered[0].nodes}\")\n",
@@ -245,7 +245,9 @@
     "    allowed_nodes=[\"Person\", \"Country\", \"Organization\"],\n",
     "    allowed_relationships=allowed_relationships,\n",
     ")\n",
-    "graph_documents_filtered = llm_transformer_tuple.convert_to_graph_documents(documents)\n",
+    "graph_documents_filtered = await llm_transformer_tuple.aconvert_to_graph_documents(\n",
+    "    documents\n",
+    ")\n",
     "print(f\"Nodes:{graph_documents_filtered[0].nodes}\")\n",
     "print(f\"Relationships:{graph_documents_filtered[0].relationships}\")"
    ]
@@ -289,7 +291,9 @@
     "    allowed_relationships=[\"NATIONALITY\", \"LOCATED_IN\", \"WORKED_AT\", \"SPOUSE\"],\n",
     "    node_properties=[\"born_year\"],\n",
     ")\n",
-    "graph_documents_props = llm_transformer_props.convert_to_graph_documents(documents)\n",
+    "graph_documents_props = await llm_transformer_props.aconvert_to_graph_documents(\n",
+    "    documents\n",
+    ")\n",
     "print(f\"Nodes:{graph_documents_props[0].nodes}\")\n",
     "print(f\"Relationships:{graph_documents_props[0].relationships}\")"
    ]

docs/docs/how_to/prompts_composition.ipynb

@@ -100,7 +100,7 @@
    "id": "8554bae5",
    "metadata": {},
    "source": [
-    "A chat prompt is made up a of a list of messages. Similarly to the above example, we can concatenate chat prompt templates. Each new element is a new message in the final prompt.\n",
+    "A chat prompt is made up of a list of messages. Similarly to the above example, we can concatenate chat prompt templates. Each new element is a new message in the final prompt.\n",
     "\n",
     "First, let's initialize the a [`ChatPromptTemplate`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html) with a [`SystemMessage`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.system.SystemMessage.html)."
    ]

docs/docs/how_to/tool_calling_parallel.ipynb

@@ -6,9 +6,9 @@
    "source": [
     "# How to disable parallel tool calling\n",
     "\n",
-    ":::info OpenAI-specific\n",
+    ":::info Provider-specific\n",
     "\n",
-    "This API is currently only supported by OpenAI.\n",
+    "This API is currently only supported by OpenAI and Anthropic.\n",
     "\n",
     ":::\n",
     "\n",
@@ -55,12 +55,12 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain.chat_models import init_chat_model\n",
     "\n",
     "if \"OPENAI_API_KEY\" not in os.environ:\n",
     "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = init_chat_model(\"openai:gpt-4.1-mini\")"
    ]
   },
   {
@@ -121,7 +121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_chain.ipynb

@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "90187d07",
    "metadata": {},
    "outputs": [],
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d7009e1a",
    "metadata": {},
    "outputs": [
@@ -99,7 +99,7 @@
      "output_type": "stream",
      "text": [
       "multiply\n",
-      "multiply(first_int: int, second_int: int) -> int - Multiply two integers together.\n",
+      "Multiply two integers together.\n",
       "{'first_int': {'title': 'First Int', 'type': 'integer'}, 'second_int': {'title': 'Second Int', 'type': 'integer'}}\n"
      ]
     }
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "be77e780",
    "metadata": {},
    "outputs": [
@@ -122,7 +122,7 @@
        "20"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "9bce8935-1465-45ac-8a93-314222c753c4",
    "metadata": {},
    "outputs": [],
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "3bfe2cdc-7d72-457c-a9a1-5fa1e0bcde55",
    "metadata": {},
    "outputs": [],
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "68f30343-14ef-48f1-badd-b6a03977316d",
    "metadata": {},
    "outputs": [
@@ -204,10 +204,11 @@
       "text/plain": [
        "[{'name': 'multiply',\n",
        "  'args': {'first_int': 5, 'second_int': 42},\n",
-       "  'id': 'call_cCP9oA3tRz7HDrjFn1FdmDaG'}]"
+       "  'id': 'call_8QIg4QVFVAEeC1orWAgB2036',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "4f5325ca-e5dc-4d1a-ba36-b085a029c90a",
    "metadata": {},
    "outputs": [
@@ -247,7 +248,7 @@
        "92"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,58 +275,31 @@
    "source": [
     "## Agents\n",
     "\n",
-    "Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/tutorials/agents) let us do just this.\n",
+    "Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/concepts/agents/) let us do just this.\n",
     "\n",
-    "LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/concepts/agents).\n",
-    "\n",
-    "We'll use the [tool calling agent](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html), which is generally the most reliable kind and the recommended one for most use cases.\n",
+    "We'll demonstrate a simple example using a LangGraph agent. See [this tutorial](/docs/tutorials/agents) for more detail.\n",
     "\n",
     "![agent](../../static/img/tool_agent.svg)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "21723cf4-9421-4a8d-92a6-eeeb8f4367f1",
+   "execution_count": null,
+   "id": "86789cfb-f441-4453-adf8-961eeceb00bc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import hub\n",
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent"
+    "!pip install -qU langgraph"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "6be83879-9da3-4dd9-b147-a79f76affd7a",
+   "execution_count": 9,
+   "id": "21723cf4-9421-4a8d-92a6-eeeb8f4367f1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m System Message \u001b[0m================================\n",
-      "\n",
-      "You are a helpful assistant\n",
-      "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{chat_history}\u001b[0m\n",
-      "\n",
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n",
-      "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{agent_scratchpad}\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Get the prompt to use - can be replaced with any prompt that includes variables \"agent_scratchpad\" and \"input\"!\n",
-    "prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
-    "prompt.pretty_print()"
+    "from langgraph.prebuilt import create_react_agent"
    ]
   },
   {
@@ -338,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "95c86d32-ee45-4c87-a28c-14eff19b49e9",
    "metadata": {},
    "outputs": [],
@@ -360,24 +334,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "17b09ac6-c9b7-4340-a8a0-3d3061f7888c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Construct the tool calling agent\n",
-    "agent = create_tool_calling_agent(llm, tools, prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "675091d2-cac9-45c4-a5d7-b760ee6c1986",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create an agent executor by passing in the agent and tools\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
+    "agent = create_react_agent(llm, tools)"
    ]
   },
   {
@@ -390,62 +353,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "f7dbb240-809e-4e41-8f63-1a4636e8e26d",
+   "execution_count": 13,
+   "id": "71c84594-d420-4703-8bdd-ca4eb7efefb6",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
+      "Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  exponentiate (call_EHGS8gnEVNCJQ9rVOk11KCQH)\n",
+      " Call ID: call_EHGS8gnEVNCJQ9rVOk11KCQH\n",
+      "  Args:\n",
+      "    base: 3\n",
+      "    exponent: 5\n",
+      "  add (call_s2cxOrXEKqI6z7LWbMUG6s8c)\n",
+      " Call ID: call_s2cxOrXEKqI6z7LWbMUG6s8c\n",
+      "  Args:\n",
+      "    first_int: 12\n",
+      "    second_int: 3\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: add\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `exponentiate` with `{'base': 3, 'exponent': 5}`\n",
+      "15\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  multiply (call_25v5JEfDWuKNgmVoGBan0d7J)\n",
+      " Call ID: call_25v5JEfDWuKNgmVoGBan0d7J\n",
+      "  Args:\n",
+      "    first_int: 243\n",
+      "    second_int: 15\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: multiply\n",
       "\n",
+      "3645\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  exponentiate (call_x1yKEeBPrFYmCp2z5Kn8705r)\n",
+      " Call ID: call_x1yKEeBPrFYmCp2z5Kn8705r\n",
+      "  Args:\n",
+      "    base: 3645\n",
+      "    exponent: 2\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: exponentiate\n",
       "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m243\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `add` with `{'first_int': 12, 'second_int': 3}`\n",
+      "13286025\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3m15\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `multiply` with `{'first_int': 243, 'second_int': 15}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m3645\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `exponentiate` with `{'base': 405, 'exponent': 2}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of taking 3 to the fifth power is 243. \n",
-      "\n",
-      "The sum of twelve and three is 15. \n",
-      "\n",
-      "Multiplying 243 by 15 gives 3645. \n",
-      "\n",
-      "Finally, squaring 3645 gives 13286025.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "The final result of taking 3 to the fifth power, multiplying it by the sum of twelve and three, and then squaring the whole result is **13,286,025**.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
-       " 'output': 'The result of taking 3 to the fifth power is 243. \\n\\nThe sum of twelve and three is 15. \\n\\nMultiplying 243 by 15 gives 3645. \\n\\nFinally, squaring 3645 gives 13286025.'}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.invoke(\n",
-    "    {\n",
-    "        \"input\": \"Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result\"\n",
-    "    }\n",
-    ")"
+    "# Use the agent\n",
+    "\n",
+    "query = (\n",
+    "    \"Take 3 to the fifth power and multiply that by the sum of twelve and \"\n",
+    "    \"three, then square the whole result.\"\n",
+    ")\n",
+    "input_message = {\"role\": \"user\", \"content\": query}\n",
+    "\n",
+    "for step in agent.stream({\"messages\": [input_message]}, stream_mode=\"values\"):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
   },
   {
@@ -473,7 +446,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -1,35 +1,26 @@
 {
  "cells": [
   {
-   "cell_type": "raw",
-   "id": "afaf8039",
+   "cell_type": "markdown",
+   "id": "d982c99f",
    "metadata": {},
    "source": [
     "---\n",
-    "sidebar_label: Google AI\n",
+    "sidebar_label: Google Gemini\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
+   "id": "56a6d990",
    "metadata": {},
    "source": [
     "# ChatGoogleGenerativeAI\n",
     "\n",
-    "This docs will help you get started with Google AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
+    "Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. The `langchain-google-genai` package provides the LangChain integration for these models. This is often the best starting point for individual developers.\n",
     "\n",
-    "Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
+    "For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini). All examples use the `gemini-2.0-flash` model. Gemini 2.5 Pro and 2.5 Flash can be used via  `gemini-2.5-pro-preview-03-25` and `gemini-2.5-flash-preview-04-17`. All model ids can be found in the [Gemini API docs](https://ai.google.dev/gemini-api/docs/models).\n",
     "\n",
-    ":::info Google AI vs Google Cloud Vertex AI\n",
-    "\n",
-    "Google's Gemini models are accessible through Google AI and through Google Cloud Vertex AI. Using Google AI just requires a Google account and an API key. Using Google Cloud Vertex AI requires a Google Cloud account (with term agreements and billing) but offers enterprise features like customer encryption key, virtual private cloud, and more.\n",
-    "\n",
-    "To learn more about the key features of the two APIs see the [Google docs](https://cloud.google.com/vertex-ai/generative-ai/docs/migrate/migrate-google-ai#google-ai).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
     "### Integration details\n",
     "\n",
     "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_generativeai) | Package downloads | Package latest |\n",
@@ -37,23 +28,46 @@
     "| [ChatGoogleGenerativeAI](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) | [langchain-google-genai](https://python.langchain.com/api_reference/google_genai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-genai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-genai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
+    "\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",
+    "### Setup\n",
     "\n",
-    "To access Google AI models you'll need to create a Google Acount account, get a Google AI API key, and install the `langchain-google-genai` integration package.\n",
+    "To access Google AI models you'll need to create a Google Account, get a Google AI API key, and install the `langchain-google-genai` integration package.\n",
     "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to https://ai.google.dev/gemini-api/docs/api-key to generate a Google AI API key. Once you've done this set the GOOGLE_API_KEY environment variable:"
+    "**1. Installation:**"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "id": "8d12ce35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -U langchain-google-genai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60be0b38",
+   "metadata": {},
+   "source": [
+    "**2. Credentials:**\n",
+    "\n",
+    "Head to [https://ai.google.dev/gemini-api/docs/api-key](https://ai.google.dev/gemini-api/docs/api-key) (or via Google AI Studio) to generate a Google AI API key.\n",
+    "\n",
+    "### Chat Models\n",
+    "\n",
+    "Use the `ChatGoogleGenerativeAI` class to interact with Google's chat models. See the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) for full details.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fb18c875",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "id": "f050e8db",
    "metadata": {},
    "source": [
     "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
@@ -75,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "id": "82cb346f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,27 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Google AI integration lives in the `langchain-google-genai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-google-genai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "id": "273cefa0",
    "metadata": {},
    "source": [
     "## Instantiation\n",
@@ -115,15 +109,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "execution_count": 4,
+   "id": "7d3dc0b3",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
     "\n",
     "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-2.0-flash-001\",\n",
+    "    model=\"gemini-2.0-flash\",\n",
     "    temperature=0,\n",
     "    max_tokens=None,\n",
     "    timeout=None,\n",
@@ -134,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b4f3e15",
+   "id": "343a8c13",
    "metadata": {},
    "source": [
     "## Invocation"
@@ -142,19 +136,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 5,
+   "id": "82c5708c",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash-001', 'safety_ratings': []}, id='run-61cff164-40be-4f88-a2df-cca58297502f-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27, 'input_token_details': {'cache_read': 0}})"
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-3b28d4b8-8a62-4e6c-ad4e-b53e6e825749-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27, 'input_token_details': {'cache_read': 0}})"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,8 +165,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "execution_count": 6,
+   "id": "49d2d0c2",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "id": "ee3f6e1d",
    "metadata": {},
    "source": [
     "## Chaining\n",
@@ -201,17 +193,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "execution_count": 7,
+   "id": "3c8407ee",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash-001', 'safety_ratings': []}, id='run-dd2f8fb9-62d9-4b84-9c97-ed9c34cda313-0', usage_metadata={'input_tokens': 15, 'output_tokens': 7, 'total_tokens': 22, 'input_token_details': {'cache_read': 0}})"
+       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-e5561c6b-2beb-4411-9210-4796b576a7cd-0', usage_metadata={'input_tokens': 15, 'output_tokens': 7, 'total_tokens': 22, 'input_token_details': {'cache_read': 0}})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,22 +233,164 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41c2ff10-a3ba-4f40-b3aa-7a395854849e",
+   "id": "bdae9742",
    "metadata": {},
    "source": [
-    "## Image generation\n",
+    "## Multimodal Usage\n",
     "\n",
-    "Some Gemini models (specifically `gemini-2.0-flash-exp`) support image generation capabilities.\n",
+    "Gemini models can accept multimodal inputs (text, images, audio, video) and, for some models, generate multimodal outputs.\n",
     "\n",
-    "### Text to image\n",
+    "### Image Input\n",
     "\n",
-    "See a simple usage example below:"
+    "Provide image inputs along with text using a `HumanMessage` with a list content format. The `gemini-2.0-flash` model can handle images."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "7589e14d-8d1b-4c82-965f-5558d80cb677",
+   "execution_count": null,
+   "id": "6833fe5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "# Example using a public URL (remains the same)\n",
+    "message_url = HumanMessage(\n",
+    "    content=[\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the image at the URL.\",\n",
+    "        },\n",
+    "        {\"type\": \"image_url\", \"image_url\": \"https://picsum.photos/seed/picsum/200/300\"},\n",
+    "    ]\n",
+    ")\n",
+    "result_url = llm.invoke([message_url])\n",
+    "print(f\"Response for URL image: {result_url.content}\")\n",
+    "\n",
+    "# Example using a local image file encoded in base64\n",
+    "image_file_path = \"/Users/philschmid/projects/google-gemini/langchain/docs/static/img/agents_vs_chains.png\"\n",
+    "\n",
+    "with open(image_file_path, \"rb\") as image_file:\n",
+    "    encoded_image = base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
+    "\n",
+    "message_local = HumanMessage(\n",
+    "    content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the local image.\"},\n",
+    "        {\"type\": \"image_url\", \"image_url\": f\"data:image/png;base64,{encoded_image}\"},\n",
+    "    ]\n",
+    ")\n",
+    "result_local = llm.invoke([message_local])\n",
+    "print(f\"Response for local image: {result_local.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b422382",
+   "metadata": {},
+   "source": [
+    "Other supported `image_url` formats:\n",
+    "- A Google Cloud Storage URI (`gs://...`). Ensure the service account has access.\n",
+    "- A PIL Image object (the library handles encoding).\n",
+    "\n",
+    "### Audio Input\n",
+    "\n",
+    "Provide audio file inputs along with text. Use a model like `gemini-2.0-flash`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3461836",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "# Ensure you have an audio file named 'example_audio.mp3' or provide the correct path.\n",
+    "audio_file_path = \"example_audio.mp3\"\n",
+    "audio_mime_type = \"audio/mpeg\"\n",
+    "\n",
+    "\n",
+    "with open(audio_file_path, \"rb\") as audio_file:\n",
+    "    encoded_audio = base64.b64encode(audio_file.read()).decode(\"utf-8\")\n",
+    "\n",
+    "message = HumanMessage(\n",
+    "    content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Transcribe the audio.\"},\n",
+    "        {\n",
+    "            \"type\": \"media\",\n",
+    "            \"data\": encoded_audio,  # Use base64 string directly\n",
+    "            \"mime_type\": audio_mime_type,\n",
+    "        },\n",
+    "    ]\n",
+    ")\n",
+    "response = llm.invoke([message])  # Uncomment to run\n",
+    "print(f\"Response for audio: {response.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d898e27",
+   "metadata": {},
+   "source": [
+    "### Video Input\n",
+    "\n",
+    "Provide video file inputs along with text. Use a model like `gemini-2.0-flash`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3046e74b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "# Ensure you have a video file named 'example_video.mp4' or provide the correct path.\n",
+    "video_file_path = \"example_video.mp4\"\n",
+    "video_mime_type = \"video/mp4\"\n",
+    "\n",
+    "\n",
+    "with open(video_file_path, \"rb\") as video_file:\n",
+    "    encoded_video = base64.b64encode(video_file.read()).decode(\"utf-8\")\n",
+    "\n",
+    "message = HumanMessage(\n",
+    "    content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the first few frames of the video.\"},\n",
+    "        {\n",
+    "            \"type\": \"media\",\n",
+    "            \"data\": encoded_video,  # Use base64 string directly\n",
+    "            \"mime_type\": video_mime_type,\n",
+    "        },\n",
+    "    ]\n",
+    ")\n",
+    "response = llm.invoke([message])  # Uncomment to run\n",
+    "print(f\"Response for video: {response.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2df11d89",
+   "metadata": {},
+   "source": [
+    "### Image Generation (Multimodal Output)\n",
+    "\n",
+    "The `gemini-2.0-flash` model can generate text and images inline (image generation is experimental). You need to specify the desired `response_modalities`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b7180f",
    "metadata": {},
    "outputs": [
     {
@@ -266,17 +400,12 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import base64\n",
-    "from io import BytesIO\n",
     "\n",
     "from IPython.display import Image, display\n",
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
@@ -301,7 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b14c0d87-cf7e-4d88-bda1-2ab40ec0350a",
+   "id": "14bf00f1",
    "metadata": {},
    "source": [
     "### Image and text to image\n",
@@ -311,8 +440,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f4ed7a5-980c-4b54-b743-0b988909744c",
+   "execution_count": null,
+   "id": "d65e195c",
    "metadata": {},
    "outputs": [
     {
@@ -322,11 +451,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -349,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a62669d8-becd-495f-8f4a-82d7c5d87969",
+   "id": "43b54d3f",
    "metadata": {},
    "source": [
     "You can also represent an input image and query in a single message by encoding the base64 data in the [data URI scheme](https://en.wikipedia.org/wiki/Data_URI_scheme):"
@@ -357,8 +482,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "6241da43-e210-43bc-89af-b3c480ea06e9",
+   "execution_count": null,
+   "id": "0dfc7e1e",
    "metadata": {},
    "outputs": [
     {
@@ -368,11 +493,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -403,7 +524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfe228d3-6773-4283-9788-87bdf6912b1c",
+   "id": "789818d7",
    "metadata": {},
    "source": [
     "You can also use LangGraph to manage the conversation history for you as in [this tutorial](/docs/tutorials/chatbot/)."
@@ -411,7 +532,313 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "id": "b037e2dc",
+   "metadata": {},
+   "source": [
+    "## Tool Calling\n",
+    "\n",
+    "You can equip the model with tools to call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0d759f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'a6248087-74c5-4b7c-9250-f335e642927c', 'type': 'tool_call'}]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"OK. It's sunny in San Francisco.\", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-ac5bb52c-e244-4c72-9fbc-fb2a9cd7a72e-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_read': 0}})"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "\n",
+    "# Define the tool\n",
+    "@tool(description=\"Get the current weather in a given location\")\n",
+    "def get_weather(location: str) -> str:\n",
+    "    return \"It's sunny.\"\n",
+    "\n",
+    "\n",
+    "# Initialize the model and bind the tool\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "llm_with_tools = llm.bind_tools([get_weather])\n",
+    "\n",
+    "# Invoke the model with a query that should trigger the tool\n",
+    "query = \"What's the weather in San Francisco?\"\n",
+    "ai_msg = llm_with_tools.invoke(query)\n",
+    "\n",
+    "# Check the tool calls in the response\n",
+    "print(ai_msg.tool_calls)\n",
+    "\n",
+    "# Example tool call message would be needed here if you were actually running the tool\n",
+    "from langchain_core.messages import ToolMessage\n",
+    "\n",
+    "tool_message = ToolMessage(\n",
+    "    content=get_weather(*ai_msg.tool_calls[0][\"args\"]),\n",
+    "    tool_call_id=ai_msg.tool_calls[0][\"id\"],\n",
+    ")\n",
+    "llm_with_tools.invoke([ai_msg, tool_message])  # Example of passing tool result back"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91d42b86",
+   "metadata": {},
+   "source": [
+    "## Structured Output\n",
+    "\n",
+    "Force the model to respond with a specific structure using Pydantic models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7457dbe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name='Abraham Lincoln' height_m=1.93\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "\n",
+    "# Define the desired structure\n",
+    "class Person(BaseModel):\n",
+    "    \"\"\"Information about a person.\"\"\"\n",
+    "\n",
+    "    name: str = Field(..., description=\"The person's name\")\n",
+    "    height_m: float = Field(..., description=\"The person's height in meters\")\n",
+    "\n",
+    "\n",
+    "# Initialize the model\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
+    "structured_llm = llm.with_structured_output(Person)\n",
+    "\n",
+    "# Invoke the model with a query asking for structured information\n",
+    "result = structured_llm.invoke(\n",
+    "    \"Who was the 16th president of the USA, and how tall was he in meters?\"\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90d4725e",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Token Usage Tracking\n",
+    "\n",
+    "Access token usage information from the response metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "edcc003e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prompt engineering is the art and science of crafting effective text prompts to elicit desired and accurate responses from large language models.\n",
+      "\n",
+      "Usage Metadata:\n",
+      "{'input_tokens': 10, 'output_tokens': 24, 'total_tokens': 34, 'input_token_details': {'cache_read': 0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "\n",
+    "result = llm.invoke(\"Explain the concept of prompt engineering in one sentence.\")\n",
+    "\n",
+    "print(result.content)\n",
+    "print(\"\\nUsage Metadata:\")\n",
+    "print(result.usage_metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28950dbc",
+   "metadata": {},
+   "source": [
+    "## Built-in tools\n",
+    "\n",
+    "Google Gemini supports a variety of built-in tools ([google search](https://ai.google.dev/gemini-api/docs/grounding/search-suggestions), [code execution](https://ai.google.dev/gemini-api/docs/code-execution?lang=python)), which can be bound to the model in the usual way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd074816",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The next total solar eclipse visible in the United States will occur on August 23, 2044. However, the path of totality will only pass through Montana, North Dakota, and South Dakota.\n",
+      "\n",
+      "For a total solar eclipse that crosses a significant portion of the continental U.S., you'll have to wait until August 12, 2045. This eclipse will start in California and end in Florida.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.ai.generativelanguage_v1beta.types import Tool as GenAITool\n",
+    "\n",
+    "resp = llm.invoke(\n",
+    "    \"When is the next total solar eclipse in US?\",\n",
+    "    tools=[GenAITool(google_search={})],\n",
+    ")\n",
+    "\n",
+    "print(resp.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6964be2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executable code: print(2*2)\n",
+      "\n",
+      "Code execution result: 4\n",
+      "\n",
+      "2*2 is 4.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/philschmid/projects/google-gemini/langchain/.venv/lib/python3.9/site-packages/langchain_google_genai/chat_models.py:580: UserWarning: \n",
+      "        ⚠️ Warning: Output may vary each run.  \n",
+      "        - 'executable_code': Always present.  \n",
+      "        - 'execution_result' & 'image_url': May be absent for some queries.  \n",
+      "\n",
+      "        Validate before using in production.\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.ai.generativelanguage_v1beta.types import Tool as GenAITool\n",
+    "\n",
+    "resp = llm.invoke(\n",
+    "    \"What is 2*2, use python\",\n",
+    "    tools=[GenAITool(code_execution={})],\n",
+    ")\n",
+    "\n",
+    "for c in resp.content:\n",
+    "    if isinstance(c, dict):\n",
+    "        if c[\"type\"] == \"code_execution_result\":\n",
+    "            print(f\"Code execution result: {c['code_execution_result']}\")\n",
+    "        elif c[\"type\"] == \"executable_code\":\n",
+    "            print(f\"Executable code: {c['executable_code']}\")\n",
+    "    else:\n",
+    "        print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a27e6ff4",
+   "metadata": {},
+   "source": [
+    "## Native Async\n",
+    "\n",
+    "Use asynchronous methods for non-blocking calls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c6803e57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Async Invoke Result: The sky is blue due to a phenomenon called **Rayle...\n",
+      "\n",
+      "Async Stream Result:\n",
+      "The thread is free, it does not wait,\n",
+      "For answers slow, or tasks of fate.\n",
+      "A promise made, a future bright,\n",
+      "It moves ahead, with all its might.\n",
+      "\n",
+      "A callback waits, a signal sent,\n",
+      "When data's read, or job is spent.\n",
+      "Non-blocking code, a graceful dance,\n",
+      "Responsive apps, a fleeting glance.\n",
+      "\n",
+      "Async Batch Results: ['1 + 1 = 2', '2 + 2 = 4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "\n",
+    "\n",
+    "async def run_async_calls():\n",
+    "    # Async invoke\n",
+    "    result_ainvoke = await llm.ainvoke(\"Why is the sky blue?\")\n",
+    "    print(\"Async Invoke Result:\", result_ainvoke.content[:50] + \"...\")\n",
+    "\n",
+    "    # Async stream\n",
+    "    print(\"\\nAsync Stream Result:\")\n",
+    "    async for chunk in llm.astream(\n",
+    "        \"Write a short poem about asynchronous programming.\"\n",
+    "    ):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "    print(\"\\n\")\n",
+    "\n",
+    "    # Async batch\n",
+    "    results_abatch = await llm.abatch([\"What is 1+1?\", \"What is 2+2?\"])\n",
+    "    print(\"Async Batch Results:\", [res.content for res in results_abatch])\n",
+    "\n",
+    "\n",
+    "await run_async_calls()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99204b32",
    "metadata": {},
    "source": [
     "## Safety Settings\n",
@@ -421,8 +848,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "238b2f96-e573-4fac-bbf2-7e52ad926833",
+   "execution_count": null,
+   "id": "d4c14039",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,7 +869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5805d40c-deb8-4924-8e72-a294a0482fc9",
+   "id": "dea38fb1",
    "metadata": {},
    "source": [
     "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
@@ -450,7 +877,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "id": "d6d0e853",
    "metadata": {},
    "source": [
     "## API reference\n",
@@ -461,7 +888,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -475,7 +902,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/litellm.ipynb

@@ -19,18 +19,50 @@
     "id": "5bcea387"
    },
    "source": [
-    "# ChatLiteLLM\n",
+    "# ChatLiteLLM and ChatLiteLLMRouter\n",
     "\n",
     "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.\n",
     "\n",
     "This notebook covers how to get started with using Langchain + the LiteLLM I/O library.\n",
     "\n",
+    "This integration contains two main classes:\n",
+    "\n",
+    "- ```ChatLiteLLM```: The main Langchain wrapper for basic usage of LiteLLM ([docs](https://docs.litellm.ai/docs/)).\n",
+    "- ```ChatLiteLLMRouter```: A ```ChatLiteLLM``` wrapper that leverages LiteLLM's Router ([docs](https://docs.litellm.ai/docs/routing))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ddb7fd3",
+   "metadata": {},
+   "source": [
+    "## Table of Contents\n",
+    "1. [Overview](#overview)\n",
+    "   - [Integration Details](#integration-details)\n",
+    "   - [Model Features](#model-features)\n",
+    "2. [Setup](#setup)\n",
+    "3. [Credentials](#credentials)\n",
+    "4. [Installation](#installation)\n",
+    "5. [Instantiation](#instantiation)\n",
+    "   - [ChatLiteLLM](#chatlitellm)\n",
+    "   - [ChatLiteLLMRouter](#chatlitellmrouter)\n",
+    "6. [Invocation](#invocation)\n",
+    "7. [Async and Streaming Functionality](#async-and-streaming-functionality)\n",
+    "8. [API Reference](#api-reference)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37be6ef8",
+   "metadata": {},
+   "source": [
     "## Overview\n",
     "### Integration details\n",
     "\n",
     "| Class | Package | Local | Serializable | JS support| Package downloads | Package latest |\n",
     "| :---  | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatLiteLLM](https://python.langchain.com/docs/integrations/chat/litellm/) | [langchain-litellm](https://pypi.org/project/langchain-litellm/)| ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-litellm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-litellm?style=flat-square&label=%20) |\n",
+    "| [ChatLiteLLM](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm) | [langchain-litellm](https://pypi.org/project/langchain-litellm/)| ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-litellm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-litellm?style=flat-square&label=%20) |\n",
+    "| [ChatLiteLLMRouter](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellmrouter) | [langchain-litellm](https://pypi.org/project/langchain-litellm/)| ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-litellm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-litellm?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](https://python.langchain.com/docs/how_to/tool_calling/) | [Structured output](https://python.langchain.com/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm-also-supports-async-and-streaming-functionality) | [Native async](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm-also-supports-async-and-streaming-functionality) | [Token usage](https://python.langchain.com/docs/how_to/chat_token_usage_tracking/) | [Logprobs](https://python.langchain.com/docs/how_to/logprobs/) |\n",
@@ -38,7 +70,7 @@
     "| ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
     "\n",
     "### Setup\n",
-    "To access ChatLiteLLM models you'll need to install the `langchain-litellm` package and create an OpenAI, Anthropic, Azure, Replicate, OpenRouter, Hugging Face, Together AI or Cohere account. Then you have to get an API key, and export it as an environment variable."
+    "To access ```ChatLiteLLM``` and ```ChatLiteLLMRouter``` models, you'll need to install the `langchain-litellm` package and create an OpenAI, Anthropic, Azure, Replicate, OpenRouter, Hugging Face, Together AI, or Cohere account. Then, you have to get an API key and export it as an environment variable."
    ]
   },
   {
@@ -53,23 +85,23 @@
     "You have to choose the LLM provider you want and sign up with them to get their API key.\n",
     "\n",
     "### Example - Anthropic\n",
-    "Head to https://console.anthropic.com/ to sign up for Anthropic and generate an API key. Once you've done this set the ANTHROPIC_API_KEY environment variable.\n",
+    "Head to https://console.anthropic.com/ to sign up for Anthropic and generate an API key. Once you've done this, set the ANTHROPIC_API_KEY environment variable.\n",
     "\n",
     "\n",
     "### Example - OpenAI\n",
-    "Head to https://platform.openai.com/api-keys to sign up for OpenAI and generate an API key. Once you've done this set the OPENAI_API_KEY environment variable."
+    "Head to https://platform.openai.com/api-keys to sign up for OpenAI and generate an API key. Once you've done this, set the OPENAI_API_KEY environment variable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "7595eddf",
    "metadata": {
     "id": "7595eddf"
    },
    "outputs": [],
    "source": [
-    "## set ENV variables\n",
+    "## Set ENV variables\n",
     "import os\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-key\"\n",
@@ -85,7 +117,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain LiteLLM integration lives in the `langchain-litellm` package:"
+    "The LangChain LiteLLM integration is available in the `langchain-litellm` package:"
    ]
   },
   {
@@ -107,13 +139,21 @@
     "id": "bc1182b4"
    },
    "source": [
-    "## Instantiation\n",
-    "Now we can instantiate our model object and generate chat completions:"
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d439241a",
+   "metadata": {},
+   "source": [
+    "### ChatLiteLLM\n",
+    "You can instantiate a ```ChatLiteLLM``` model by providing a ```model``` name [supported by LiteLLM](https://docs.litellm.ai/docs/providers)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
@@ -123,7 +163,50 @@
    "source": [
     "from langchain_litellm import ChatLiteLLM\n",
     "\n",
-    "llm = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
+    "llm = ChatLiteLLM(model=\"gpt-4.1-nano\", temperature=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d0ed306",
+   "metadata": {},
+   "source": [
+    "### ChatLiteLLMRouter\n",
+    "You can also leverage LiteLLM's routing capabilities by defining your model list as specified [here](https://docs.litellm.ai/docs/routing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d26393a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_litellm import ChatLiteLLMRouter\n",
+    "from litellm import Router\n",
+    "\n",
+    "model_list = [\n",
+    "    {\n",
+    "        \"model_name\": \"gpt-4.1\",\n",
+    "        \"litellm_params\": {\n",
+    "            \"model\": \"azure/gpt-4.1\",\n",
+    "            \"api_key\": \"<your-api-key>\",\n",
+    "            \"api_version\": \"2024-10-21\",\n",
+    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
+    "        },\n",
+    "    },\n",
+    "    {\n",
+    "        \"model_name\": \"gpt-4o\",\n",
+    "        \"litellm_params\": {\n",
+    "            \"model\": \"azure/gpt-4o\",\n",
+    "            \"api_key\": \"<your-api-key>\",\n",
+    "            \"api_version\": \"2024-10-21\",\n",
+    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
+    "        },\n",
+    "    },\n",
+    "]\n",
+    "litellm_router = Router(model_list=model_list)\n",
+    "llm = ChatLiteLLMRouter(router=litellm_router, model_name=\"gpt-4.1\", temperature=0.1)"
    ]
   },
   {
@@ -133,7 +216,8 @@
     "id": "63d98454"
    },
    "source": [
-    "## Invocation"
+    "## Invocation\n",
+    "Whether you've instantiated a `ChatLiteLLM` or a `ChatLiteLLMRouter`, you can now use the ChatModel through Langchain's API."
    ]
   },
   {
@@ -171,7 +255,8 @@
     "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c"
    },
    "source": [
-    "## `ChatLiteLLM` also supports async and streaming functionality:"
+    "## Async and Streaming Functionality\n",
+    "`ChatLiteLLM` and `ChatLiteLLMRouter` also support async and streaming functionality:"
    ]
   },
   {
@@ -212,7 +297,7 @@
    },
    "source": [
     "## API reference\n",
-    "For detailed documentation of all `ChatLiteLLM` features and configurations head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm"
+    "For detailed documentation of all `ChatLiteLLM` and `ChatLiteLLMRouter` features and configurations, head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm"
    ]
   }
  ],

docs/docs/integrations/chat/litellm_router.ipynb

@@ -1,218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "59148044",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: LiteLLM Router\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "247da7a6",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# ChatLiteLLMRouter\n",
-    "\n",
-    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
-    "\n",
-    "This notebook covers how to get started with using Langchain + the LiteLLM Router I/O library. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models import ChatLiteLLMRouter\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from litellm import Router"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_list = [\n",
-    "    {\n",
-    "        \"model_name\": \"gpt-4\",\n",
-    "        \"litellm_params\": {\n",
-    "            \"model\": \"azure/gpt-4-1106-preview\",\n",
-    "            \"api_key\": \"<your-api-key>\",\n",
-    "            \"api_version\": \"2023-05-15\",\n",
-    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"model_name\": \"gpt-35-turbo\",\n",
-    "        \"litellm_params\": {\n",
-    "            \"model\": \"azure/gpt-35-turbo\",\n",
-    "            \"api_key\": \"<your-api-key>\",\n",
-    "            \"api_version\": \"2023-05-15\",\n",
-    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "litellm_router = Router(model_list=model_list)\n",
-    "chat = ChatLiteLLMRouter(router=litellm_router, model_name=\"gpt-35-turbo\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'aime programmer.\")"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "## `ChatLiteLLMRouter` also supports async and streaming functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\"J'adore programmer.\", generation_info={'finish_reason': 'stop'}, message=AIMessage(content=\"J'adore programmer.\"))]], llm_output={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 19, 'total_tokens': 25}, 'model_name': None}, run=[RunInfo(run_id=UUID('75003ec9-1e2b-43b7-a216-10dcc0f75e00'))])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.agenerate([messages])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\")"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatLiteLLMRouter(\n",
-    "    router=litellm_router,\n",
-    "    model_name=\"gpt-35-turbo\",\n",
-    "    streaming=True,\n",
-    "    verbose=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    ")\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c253883f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/openai.ipynb

@@ -1413,6 +1413,23 @@
     "second_output_message = llm.invoke(history)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "90c18d18-b25c-4509-a639-bd652b92f518",
+   "metadata": {},
+   "source": [
+    "## Flex processing\n",
+    "\n",
+    "OpenAI offers a variety of [service tiers](https://platform.openai.com/docs/guides/flex-processing). The \"flex\" tier offers cheaper pricing for requests, with the trade-off that responses may take longer and resources might not always be available. This approach is best suited for non-critical tasks, including model testing, data enhancement, or jobs that can be run asynchronously.\n",
+    "\n",
+    "To use it, initialize the model with `service_tier=\"flex\"`:\n",
+    "```python\n",
+    "llm = ChatOpenAI(model=\"o4-mini\", service_tier=\"flex\")\n",
+    "```\n",
+    "\n",
+    "Note that this is a beta feature that is only available for a subset of models. See OpenAI [docs](https://platform.openai.com/docs/guides/flex-processing) for more detail."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a796d728-971b-408b-88d5-440015bbb941",
@@ -1420,7 +1437,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
+    "For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html)."
    ]
   }
  ],

docs/docs/integrations/document_loaders/astradb.ipynb

@@ -14,7 +14,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API."
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API."
    ]
   },
   {
@@ -34,33 +36,46 @@
     "id": "juAmbgoWD17u"
    },
    "source": [
-    "The AstraDB Document Loader returns a list of Langchain Documents from an AstraDB database.\n",
+    "The Astra DB Document Loader returns a list of Langchain `Document` objects read from an Astra DB collection.\n",
     "\n",
-    "The Loader takes the following parameters:\n",
+    "The loader takes the following parameters:\n",
     "\n",
-    "* `api_endpoint`: AstraDB API endpoint. Looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
-    "* `token`: AstraDB token. Looks like `AstraCS:6gBhNmsk135....`\n",
+    "* `api_endpoint`: Astra DB API endpoint. Looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
+    "* `token`: Astra DB token. Looks like `AstraCS:aBcD0123...`\n",
     "* `collection_name` : AstraDB collection name\n",
-    "* `namespace`: (Optional) AstraDB namespace\n",
+    "* `namespace`: (Optional) AstraDB namespace (called _keyspace_ in Astra DB)\n",
     "* `filter_criteria`: (Optional) Filter used in the find query\n",
     "* `projection`: (Optional) Projection used in the find query\n",
-    "* `find_options`: (Optional) Options used in the find query\n",
-    "* `nb_prefetched`: (Optional) Number of documents pre-fetched by the loader\n",
+    "* `limit`: (Optional) Maximum number of documents to retrieve\n",
     "* `extraction_function`: (Optional) A function to convert the AstraDB document to the LangChain `page_content` string. Defaults to `json.dumps`\n",
     "\n",
-    "The following metadata is set to the LangChain Documents metadata output:\n",
+    "The loader sets the following metadata for the documents it reads:\n",
     "\n",
     "```python\n",
-    "{\n",
-    "    metadata : {\n",
-    "        \"namespace\": \"...\", \n",
-    "        \"api_endpoint\": \"...\", \n",
-    "        \"collection\": \"...\"\n",
-    "    }\n",
+    "metadata={\n",
+    "    \"namespace\": \"...\", \n",
+    "    \"api_endpoint\": \"...\", \n",
+    "    \"collection\": \"...\"\n",
     "}\n",
     "```"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"langchain-astradb>=0.6,<0.7\""
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -71,24 +86,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import AstraDBLoader"
+    "from langchain_astradb import AstraDBLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[**API Reference:** `AstraDBLoader`](https://python.langchain.com/api_reference/astradb/document_loaders/langchain_astradb.document_loaders.AstraDBLoader.html#langchain_astradb.document_loaders.AstraDBLoader)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:41:22.643335Z",
      "start_time": "2024-01-08T12:40:57.759116Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
+      "ASTRA_DB_APPLICATION_TOKEN =  ········\n"
+     ]
+    }
+   ],
    "source": [
     "from getpass import getpass\n",
     "\n",
@@ -98,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:25.395162Z",
@@ -112,19 +146,22 @@
     "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
     "    collection_name=\"movie_reviews\",\n",
     "    projection={\"title\": 1, \"reviewtext\": 1},\n",
-    "    find_options={\"limit\": 10},\n",
+    "    limit=10,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:30.236489Z",
      "start_time": "2024-01-08T12:42:29.612133Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -133,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:31.369394Z",
@@ -144,10 +181,10 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='{\"_id\": \"659bdffa16cbc4586b11a423\", \"title\": \"Dangerous Men\", \"reviewtext\": \"\\\\\"Dangerous Men,\\\\\" the picture\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\"}', metadata={'namespace': 'default_keyspace', 'api_endpoint': 'https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com', 'collection': 'movie_reviews'})"
+       "Document(metadata={'namespace': 'default_keyspace', 'api_endpoint': 'https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com', 'collection': 'movie_reviews'}, page_content='{\"_id\": \"659bdffa16cbc4586b11a423\", \"title\": \"Dangerous Men\", \"reviewtext\": \"\\\\\"Dangerous Men,\\\\\" the picture\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\"}')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -179,7 +216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/browserbase.ipynb

@@ -49,7 +49,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import BrowserbaseLoader"
+    "import os\n",
+    "\n",
+    "from langchain_community.document_loaders import BrowserbaseLoader\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "BROWSERBASE_API_KEY = os.getenv(\"BROWSERBASE_API_KEY\")\n",
+    "BROWSERBASE_PROJECT_ID = os.getenv(\"BROWSERBASE_PROJECT_ID\")"
    ]
   },
   {
@@ -59,6 +66,8 @@
    "outputs": [],
    "source": [
     "loader = BrowserbaseLoader(\n",
+    "    api_key=BROWSERBASE_API_KEY,\n",
+    "    project_id=BROWSERBASE_PROJECT_ID,\n",
     "    urls=[\n",
     "        \"https://example.com\",\n",
     "    ],\n",
@@ -78,52 +87,11 @@
     "\n",
     "- `urls` Required. A list of URLs to fetch.\n",
     "- `text_content` Retrieve only text content. Default is `False`.\n",
-    "- `api_key` Optional. Browserbase API key. Default is `BROWSERBASE_API_KEY` env variable.\n",
-    "- `project_id` Optional. Browserbase Project ID. Default is `BROWSERBASE_PROJECT_ID` env variable.\n",
+    "- `api_key` Browserbase API key. Default is `BROWSERBASE_API_KEY` env variable.\n",
+    "- `project_id` Browserbase Project ID. Default is `BROWSERBASE_PROJECT_ID` env variable.\n",
     "- `session_id` Optional. Provide an existing Session ID.\n",
     "- `proxy` Optional. Enable/Disable Proxies."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading images\n",
-    "\n",
-    "You can also load screenshots of webpages (as bytes) for multi-modal models.\n",
-    "\n",
-    "Full example using GPT-4V:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from browserbase import Browserbase\n",
-    "from browserbase.helpers.gpt4 import GPT4VImage, GPT4VImageDetail\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "chat = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=256)\n",
-    "browser = Browserbase()\n",
-    "\n",
-    "screenshot = browser.screenshot(\"https://browserbase.com\")\n",
-    "\n",
-    "result = chat.invoke(\n",
-    "    [\n",
-    "        HumanMessage(\n",
-    "            content=[\n",
-    "                {\"type\": \"text\", \"text\": \"What color is the logo?\"},\n",
-    "                GPT4VImage(screenshot, GPT4VImageDetail.auto),\n",
-    "            ]\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "print(result.content)"
-   ]
   }
  ],
  "metadata": {

docs/docs/integrations/llm_caching.ipynb

@@ -1214,9 +1214,7 @@
    "source": [
     "### Connecting to the DB\n",
     "\n",
-    "The Cassandra caches shown in this page can be used with Cassandra as well as other derived databases, such as Astra DB, which use the CQL (Cassandra Query Language) protocol.\n",
-    "\n",
-    "> DataStax [Astra DB](https://docs.datastax.com/en/astra-serverless/docs/vector-search/quickstart.html) is a managed serverless database built on Cassandra, offering the same interface and strengths.\n",
+    "The Cassandra caches shown in this page can be used with Cassandra as well as other derived databases that can use the CQL (Cassandra Query Language) protocol, such as DataStax Astra DB.\n",
     "\n",
     "Depending on whether you connect to a Cassandra cluster or to Astra DB through CQL, you will provide different parameters when instantiating the cache (through initialization of a CassIO connection)."
    ]
@@ -1517,6 +1515,12 @@
    "source": [
     "You can easily use [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) as an LLM cache, with either the \"exact\" or the \"semantic-based\" cache.\n",
     "\n",
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API.\n",
+    "\n",
+    "_This approach differs from the `Cassandra` caches mentioned above in that it natively uses the HTTP Data API. The Data API is specific to Astra DB. Keep in mind that the storage format will also differ._\n",
+    "\n",
     "Make sure you have a running database (it must be a Vector-enabled database to use the Semantic cache) and get the required credentials on your Astra dashboard:\n",
     "\n",
     "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
@@ -3112,8 +3116,8 @@
     "|------------|---------|\n",
     "| langchain_astradb.cache | [AstraDBCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBCache.html) |\n",
     "| langchain_astradb.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) |\n",
-    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) (deprecated since `langchain-community==0.0.28`) |\n",
+    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) (deprecated since `langchain-community==0.0.28`) |\n",
     "| langchain_community.cache | [AzureCosmosDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AzureCosmosDBSemanticCache.html) |\n",
     "| langchain_community.cache | [CassandraCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraCache.html) |\n",
     "| langchain_community.cache | [CassandraSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraSemanticCache.html) |\n",
@@ -3160,7 +3164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,

docs/docs/integrations/memory/astradb_chat_message_history.ipynb

@@ -7,7 +7,9 @@
    "source": [
     "# Astra DB \n",
     "\n",
-    "> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API.\n",
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently availablev\n",
+    "> through an easy-to-use JSON API.\n",
     "\n",
     "This notebook goes over how to use Astra DB to store chat message history."
    ]
@@ -17,22 +19,22 @@
    "id": "f507f58b-bf22-4a48-8daf-68d869bcd1ba",
    "metadata": {},
    "source": [
-    "## Setting up\n",
+    "## Setup\n",
     "\n",
     "To run this notebook you need a running Astra DB. Get the connection secrets on your Astra dashboard:\n",
     "\n",
     "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`;\n",
-    "- the Token looks like `AstraCS:6gBhNmsk135...`."
+    "- the Database Token looks like `AstraCS:aBcD0123...`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d7092199",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  \"astrapy>=0.7.1 langchain-community\" "
+    "!pip install \"langchain-astradb>=0.6,<0.7\""
    ]
   },
   {
@@ -45,12 +47,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "163d97f0",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stdin",
      "output_type": "stream",
      "text": [
       "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
@@ -65,14 +67,6 @@
     "ASTRA_DB_APPLICATION_TOKEN = getpass.getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "55860b2d",
-   "metadata": {},
-   "source": [
-    "Depending on whether local or cloud-based Astra DB, create the corresponding database connection \"Session\" object."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "36c163e8",
@@ -83,12 +77,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d15e3302",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_message_histories import AstraDBChatMessageHistory\n",
+    "from langchain_astradb import AstraDBChatMessageHistory\n",
     "\n",
     "message_history = AstraDBChatMessageHistory(\n",
     "    session_id=\"test-session\",\n",
@@ -98,22 +92,31 @@
     "\n",
     "message_history.add_user_message(\"hi!\")\n",
     "\n",
-    "message_history.add_ai_message(\"whats up?\")"
+    "message_history.add_ai_message(\"hello, how are you?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53acb4a8-d536-4a58-9fee-7d70033d9c81",
+   "metadata": {},
+   "source": [
+    "[**API Reference:** `AstraDBChatMessageHistory`](https://python.langchain.com/api_reference/astradb/chat_message_histories/langchain_astradb.chat_message_histories.AstraDBChatMessageHistory.html#langchain_astradb.chat_message_histories.AstraDBChatMessageHistory)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "64fc465e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[HumanMessage(content='hi!'), AIMessage(content='whats up?')]"
+       "[HumanMessage(content='hi!', additional_kwargs={}, response_metadata={}),\n",
+       " AIMessage(content='hello, how are you?', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -139,7 +142,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

docs/docs/integrations/providers/astradb.mdx

@@ -1,8 +1,6 @@
 # Astra DB
 
-> [DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless 
-> vector-capable database built on `Apache Cassandra®`and made conveniently available
-> through an easy-to-use JSON API.
+> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless AI-ready database built on `Apache Cassandra®` and made conveniently available through an easy-to-use JSON API.
 
 See a [tutorial provided by DataStax](https://docs.datastax.com/en/astra/astra-db-vector/tutorials/chatbot.html).
 
@@ -10,19 +8,21 @@ See a [tutorial provided by DataStax](https://docs.datastax.com/en/astra/astra-d
 
 Install the following Python package:
 ```bash
-pip install "langchain-astradb>=0.1.0"
+pip install "langchain-astradb>=0.6,<0.7"
 ```
 
-Get the [connection secrets](https://docs.datastax.com/en/astra/astra-db-vector/get-started/quickstart.html).
-Set up the following environment variables:
+Create a database (if needed) and get the [connection secrets](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html#create-a-database-and-store-your-credentials).
+Set the following variables:
 
 ```python
-ASTRA_DB_APPLICATION_TOKEN="TOKEN"
 ASTRA_DB_API_ENDPOINT="API_ENDPOINT"
+ASTRA_DB_APPLICATION_TOKEN="TOKEN"
 ```
 
 ## Vector Store
 
+A few typical initialization patterns are shown here:
+
 ```python
 from langchain_astradb import AstraDBVectorStore
 
@@ -32,8 +32,56 @@ vector_store = AstraDBVectorStore(
     api_endpoint=ASTRA_DB_API_ENDPOINT,
     token=ASTRA_DB_APPLICATION_TOKEN,
 )
+
+
+from astrapy.info import VectorServiceOptions
+
+vector_store_vectorize = AstraDBVectorStore(
+    collection_name="my_vectorize_store",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
+    collection_vector_service_options=VectorServiceOptions(
+        provider="nvidia",
+        model_name="NV-Embed-QA",
+    ),
+)
+
+
+from astrapy.info import (
+    CollectionLexicalOptions,
+    CollectionRerankOptions,
+    RerankServiceOptions,
+    VectorServiceOptions,
+)
+
+vector_store_hybrid = AstraDBVectorStore(
+    collection_name="my_hybrid_store",
+    api_endpoint=ASTRA_DB_API_ENDPOINT,
+    token=ASTRA_DB_APPLICATION_TOKEN,
+    collection_vector_service_options=VectorServiceOptions(
+        provider="nvidia",
+        model_name="NV-Embed-QA",
+    ),
+    collection_lexical=CollectionLexicalOptions(analyzer="standard"),
+    collection_rerank=CollectionRerankOptions(
+        service=RerankServiceOptions(
+            provider="nvidia",
+            model_name="nvidia/llama-3.2-nv-rerankqa-1b-v2",
+        ),
+    ),
+)
 ```
 
+Notable features of class `AstraDBVectorStore`:
+
+- native async API;
+- metadata filtering in search;
+- MMR (maximum marginal relevance) search;
+- server-side embedding computation (["vectorize"](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html) in Astra DB parlance);
+- auto-detect its settings from an existing, pre-populated Astra DB collection;
+- [hybrid search](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html#the-hybrid-search-process) (vector + BM25 and then a rerank step);
+- support for non-Astra Data API (e.g. self-hosted [HCD](https://docs.datastax.com/en/hyper-converged-database/1.1/get-started/get-started-hcd.html) deployments);
+
 Learn more in the [example notebook](/docs/integrations/vectorstores/astradb).
 
 See the [example provided by DataStax](https://docs.datastax.com/en/astra/astra-db-vector/integrations/langchain.html).
@@ -82,8 +130,6 @@ set_llm_cache(AstraDBSemanticCache(
 
 Learn more in the [example notebook](/docs/integrations/llm_caching#astra-db-caches) (scroll to the appropriate section).
 
-Learn more in the [example notebook](/docs/integrations/memory/astradb_chat_message_history).
-
 ## Document loader
 
 ```python

docs/docs/integrations/providers/litellm.mdx

@@ -12,7 +12,10 @@ pip install langchain-litellm
 ```python
 from langchain_litellm import ChatLiteLLM
 ```
+```python
+from langchain_litellm import ChatLiteLLMRouter
+```
 See more detail in the guide [here](/docs/integrations/chat/litellm).
 
 ## API reference
-For detailed documentation of all `ChatLiteLLM` features and configurations head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm
+For detailed documentation of all `ChatLiteLLM` and `ChatLiteLLMRouter` features and configurations head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm

docs/docs/integrations/providers/pinecone.mdx

@@ -27,6 +27,48 @@ from langchain_pinecone import PineconeVectorStore
 
 For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone)
 
+### Sparse Vector store
+
+LangChain's `PineconeSparseVectorStore` enables sparse retrieval using Pinecone's sparse English model. It maps text to sparse vectors and supports adding documents and similarity search.
+
+```python
+from langchain_pinecone import PineconeSparseVectorStore
+
+# Initialize sparse vector store
+vector_store = PineconeSparseVectorStore(
+    index=my_index,
+    embedding_model="pinecone-sparse-english-v0"
+)
+# Add documents
+vector_store.add_documents(documents)
+# Query
+results = vector_store.similarity_search("your query", k=3)
+```
+
+For a more detailed walkthrough, see the [Pinecone Sparse Vector Store notebook](/docs/integrations/vectorstores/pinecone_sparse).
+
+### Sparse Embedding
+
+LangChain's `PineconeSparseEmbeddings` provides sparse embedding generation using Pinecone's `pinecone-sparse-english-v0` model.
+
+```python
+from langchain_pinecone.embeddings import PineconeSparseEmbeddings
+
+# Initialize sparse embeddings
+sparse_embeddings = PineconeSparseEmbeddings(
+    model="pinecone-sparse-english-v0"
+)
+# Embed a single query (returns SparseValues)
+query_embedding = sparse_embeddings.embed_query("sample text")
+
+# Embed multiple documents (returns list of SparseValues)
+docs = ["Document 1 content", "Document 2 content"]
+doc_embeddings = sparse_embeddings.embed_documents(docs)
+```
+
+For more detailed usage, see the [Pinecone Sparse Embeddings notebook](/docs/integrations/vectorstores/pinecone_sparse).
+
+
 ## Retrievers
 
 ### Pinecone Hybrid Search

docs/docs/integrations/providers/sap.mdx

@@ -7,19 +7,19 @@
 
 ## Installation and Setup
 
-We need to install the `hdbcli` python package.
+We need to install the `langchain-hana` python package.
 
 ```bash
-pip install hdbcli
+pip install langchain-hana
 ```
 
 ## Vectorstore
 
->[SAP HANA Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is 
+>[SAP HANA Cloud Vector Engine](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-vector-engine-guide/sap-hana-cloud-sap-hana-database-vector-engine-guide) 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
+from langchain_hana import HanaDB
 ```

docs/docs/integrations/providers/vectara.ipynb

@@ -315,6 +315,17 @@
     "Vectara offers Intelligent Query Rewriting option which  enhances search precision by automatically generating metadata filter expressions from natural language queries. This capability analyzes user queries, extracts relevant metadata filters, and rephrases the query to focus on the core information need. For more details [go to this notebook](../retrievers/self_query/vectara_self_query.ipynb)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Vectara tools\n",
+    "Vectara provides serval tools that can be used with Langchain. For more details [go to this notebook](../tools/vectara.ipynb)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "beadf6f485c1a69"
+  },
   {
    "cell_type": "code",
    "execution_count": null,

docs/docs/integrations/retrievers/pinecone_rerank.ipynb

@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
+   "metadata": {},
+   "source": [
+    "# Pinecone Rerank\n",
+    "\n",
+    "> This notebook shows how to use **PineconeRerank** for two-stage vector retrieval reranking using Pinecone's hosted reranking API as demonstrated in `langchain_pinecone/libs/pinecone/rerank.py`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acae54e37e7d407bbb7b55eff062a284",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "Install the `langchain-pinecone` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU \"langchain-pinecone\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dd0d8092fe74a7c96281538738b07e2",
+   "metadata": {},
+   "source": [
+    "## Credentials\n",
+    "Set your Pinecone API key to use the reranking API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72eea5119410473aa328ad9291626812",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n",
+    "    \"Enter your Pinecone API key: \"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8edb47106e1a46a883d545849b8ab81b",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "Use `PineconeRerank` to rerank a list of documents by relevance to a query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "10185d26023b46108eb7d9f57d49d2b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jakit/customers/aurelio/langchain-pinecone/libs/pinecone/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score: 0.9998 | Content: Paris is the capital of France.\n",
+      "Score: 0.1950 | Content: The Eiffel Tower is in Paris.\n",
+      "Score: 0.0042 | Content: Berlin is the capital of Germany.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "from langchain_pinecone import PineconeRerank\n",
+    "\n",
+    "# Initialize reranker\n",
+    "reranker = PineconeRerank(model=\"bge-reranker-v2-m3\")\n",
+    "\n",
+    "# Sample documents\n",
+    "documents = [\n",
+    "    Document(page_content=\"Paris is the capital of France.\"),\n",
+    "    Document(page_content=\"Berlin is the capital of Germany.\"),\n",
+    "    Document(page_content=\"The Eiffel Tower is in Paris.\"),\n",
+    "]\n",
+    "\n",
+    "# Rerank documents\n",
+    "query = \"What is the capital of France?\"\n",
+    "reranked_docs = reranker.compress_documents(documents, query)\n",
+    "\n",
+    "# Print results\n",
+    "for doc in reranked_docs:\n",
+    "    score = doc.metadata.get(\"relevance_score\")\n",
+    "    print(f\"Score: {score:.4f} | Content: {doc.page_content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8763a12b2bbd4a93a75aff182afb95dc",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "### Reranking with Top-N\n",
+    "Specify `top_n` to limit the number of returned documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7623eae2785240b9bd12b16a66d81610",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top-1 Result:\n",
+      "Score: 0.9998 | Content: Paris is the capital of France.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Return only top-1 result\n",
+    "reranker_top1 = PineconeRerank(model=\"bge-reranker-v2-m3\", top_n=1)\n",
+    "top1_docs = reranker_top1.compress_documents(documents, query)\n",
+    "print(\"Top-1 Result:\")\n",
+    "for doc in top1_docs:\n",
+    "    print(f\"Score: {doc.metadata['relevance_score']:.4f} | Content: {doc.page_content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cdc8c89c7104fffa095e18ddfef8986",
+   "metadata": {},
+   "source": [
+    "## Reranking with Custom Rank Fields\n",
+    "If your documents are dictionaries or have custom fields, use `rank_fields` to specify the field to rank on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b118ea5561624da68c537baed56e602f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ID: doc3 | Score: 0.9892\n",
+      "ID: doc1 | Score: 0.0006\n",
+      "ID: doc2 | Score: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample dictionary documents with 'text' field\n",
+    "docs_dict = [\n",
+    "    {\n",
+    "        \"id\": \"doc1\",\n",
+    "        \"text\": \"Article about renewable energy.\",\n",
+    "        \"title\": \"Renewable Energy\",\n",
+    "    },\n",
+    "    {\"id\": \"doc2\", \"text\": \"Report on economic growth.\", \"title\": \"Economic Growth\"},\n",
+    "    {\n",
+    "        \"id\": \"doc3\",\n",
+    "        \"text\": \"News on climate policy changes.\",\n",
+    "        \"title\": \"Climate Policy\",\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Initialize reranker with rank_fields\n",
+    "reranker_text = PineconeRerank(model=\"bge-reranker-v2-m3\", rank_fields=[\"text\"])\n",
+    "climate_docs = reranker_text.rerank(docs_dict, \"Latest news on climate change.\")\n",
+    "\n",
+    "# Show IDs and scores\n",
+    "for res in climate_docs:\n",
+    "    print(f\"ID: {res['id']} | Score: {res['score']:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a80bb6c3",
+   "metadata": {},
+   "source": [
+    "We can rerank based on title field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a6f2768e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ID: doc2 | Score: 0.8918 | Title: Economic Growth\n",
+      "ID: doc3 | Score: 0.0002 | Title: Climate Policy\n",
+      "ID: doc1 | Score: 0.0000 | Title: Renewable Energy\n"
+     ]
+    }
+   ],
+   "source": [
+    "economic_docs = reranker_text.rerank(docs_dict, \"Economic forecast.\")\n",
+    "\n",
+    "# Show IDs and scores\n",
+    "for res in economic_docs:\n",
+    "    print(\n",
+    "        f\"ID: {res['id']} | Score: {res['score']:.4f} | Title: {res['document']['title']}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "938c804e27f84196a10c8828c723f798",
+   "metadata": {},
+   "source": [
+    "## Reranking with Additional Parameters\n",
+    "You can pass model-specific parameters (e.g., `truncate`) directly to `.rerank()`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a94c501c",
+   "metadata": {},
+   "source": [
+    "How to handle inputs longer than those supported by the model. Accepted values: END or NONE.\n",
+    "END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "504fb2a444614c0babb325280ed9130a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ID: docA | Score: 0.6950\n",
+      "ID: docB | Score: 0.0001\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rerank with custom truncate parameter\n",
+    "docs_simple = [\n",
+    "    {\"id\": \"docA\", \"text\": \"Quantum entanglement is a physical phenomenon...\"},\n",
+    "    {\"id\": \"docB\", \"text\": \"Classical mechanics describes motion...\"},\n",
+    "]\n",
+    "\n",
+    "reranked = reranker.rerank(\n",
+    "    documents=docs_simple,\n",
+    "    query=\"Explain the concept of quantum entanglement.\",\n",
+    "    truncate=\"END\",\n",
+    ")\n",
+    "# Print reranked IDs and scores\n",
+    "for res in reranked:\n",
+    "    print(f\"ID: {res['id']} | Score: {res['score']:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab78bcd8",
+   "metadata": {},
+   "source": [
+    "## Use within a chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59bbdb311c014d738909a11f9e486628",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "- `PineconeRerank(model, top_n, rank_fields, return_documents)`\n",
+    "- `.rerank(documents, query, rank_fields=None, model=None, top_n=None, truncate=\"END\")`\n",
+    "- `.compress_documents(documents, query)` (returns `Document` objects with `relevance_score` in metadata)"
+   ]
+  }
+ ],
+ "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": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/retrievers/self_query/astradb.ipynb

@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Astra DB (Cassandra)\n",
+    "# Astra DB\n",
     "\n",
-    ">[DataStax Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on `Cassandra` and made conveniently available through an easy-to-use JSON API.\n",
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API.\n",
     "\n",
     "In the walkthrough, we'll demo the `SelfQueryRetriever` with an `Astra DB` vector store."
    ]
@@ -16,32 +18,46 @@
    "metadata": {},
    "source": [
     "## Creating an Astra DB vector store\n",
-    "First we'll want to create an Astra DB VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "First, create an Astra DB vector store and seed it with some data.\n",
     "\n",
-    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `astrapy` package."
+    "We've created a small demo set of documents containing movie summaries.\n",
+    "\n",
+    "NOTE: The self-query retriever requires the `lark` package installed (`pip install lark`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet lark astrapy langchain-openai"
+    "!pip install \"langchain-astradb>=0.6,<0.7\" \\\n",
+    "  \"langchain_openai>=0.3,<0.4\" \\\n",
+    "  \"lark>=1.2,<2.0\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+    "In this example, you'll use the `OpenAIEmbeddings`. Please enter an OpenAI API Key."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
@@ -69,14 +85,23 @@
     "Create the Astra DB VectorStore:\n",
     "\n",
     "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
-    "- the Token looks like `AstraCS:6gBhNmsk135....`"
+    "- the Token looks like `AstraCS:aBcD0123...`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
+      "ASTRA_DB_APPLICATION_TOKEN =  ········\n"
+     ]
+    }
+   ],
    "source": [
     "ASTRA_DB_API_ENDPOINT = input(\"ASTRA_DB_API_ENDPOINT = \")\n",
     "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")"
@@ -84,11 +109,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import AstraDB\n",
+    "from langchain_astradb import AstraDBVectorStore\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "docs = [\n",
@@ -101,11 +126,13 @@
     "        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
     "    ),\n",
     "    Document(\n",
-    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
+    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams \"\n",
+    "        \"within dreams and Inception reused the idea\",\n",
     "        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
     "    ),\n",
     "    Document(\n",
-    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
+    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men \"\n",
+    "        \"pine after them\",\n",
     "        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
     "    ),\n",
     "    Document(\n",
@@ -123,7 +150,7 @@
     "    ),\n",
     "]\n",
     "\n",
-    "vectorstore = AstraDB.from_documents(\n",
+    "vectorstore = AstraDBVectorStore.from_documents(\n",
     "    docs,\n",
     "    embeddings,\n",
     "    collection_name=\"astra_self_query_demo\",\n",
@@ -136,13 +163,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Creating our self-querying retriever\n",
-    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
+    "## Creating a self-querying retriever\n",
+    "\n",
+    "Now you can instantiate the retriever.\n",
+    "\n",
+    "To do this, you need to provide some information upfront about the metadata fields that the documents support, along with a short description of the documents' contents."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +204,11 @@
     "llm = OpenAI(temperature=0)\n",
     "\n",
     "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
+    "    llm,\n",
+    "    vectorstore,\n",
+    "    document_content_description,\n",
+    "    metadata_field_info,\n",
+    "    verbose=True,\n",
     ")"
    ]
   },
@@ -183,14 +217,29 @@
    "metadata": {},
    "source": [
     "## Testing it out\n",
-    "And now we can try actually using our retriever!"
+    "\n",
+    "Now you can try actually using our retriever:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='d7b9ec1edafa467caab524455e8c1f5d', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
+       " Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so'),\n",
+       " Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       " Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example only specifies a relevant query\n",
     "retriever.invoke(\"What are some movies about dinosaurs?\")"
@@ -198,9 +247,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
+       " Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example specifies a filter\n",
     "retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
@@ -208,9 +269,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='0539843fd203484c9be486c2a0e2454c', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3}, page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them')]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example only specifies a query and a filter\n",
     "retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
@@ -218,9 +290,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='a0cef19e27c341929098ac4793602829', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea'),\n",
+       " Document(id='5b07e600d3494506952b60e0a45a0546', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'rating': 9.9}, page_content='Three men walk into the Zone, three men walk out of the Zone')]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example specifies a composite filter\n",
     "retriever.invoke(\"What's a highly rated (above 8.5), science fiction movie ?\")"
@@ -228,9 +312,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so')]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example specifies a query and composite filter\n",
     "retriever.invoke(\n",
@@ -242,20 +337,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Filter k\n",
+    "## Set a limit ('k')\n",
     "\n",
-    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
+    "you can also use the self-query retriever to specify `k`, the number of documents to fetch.\n",
     "\n",
-    "We can do this by passing `enable_limit=True` to the constructor."
+    "You achieve this by passing `enable_limit=True` to the constructor."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "retriever = SelfQueryRetriever.from_llm(\n",
+    "retriever_k = SelfQueryRetriever.from_llm(\n",
     "    llm,\n",
     "    vectorstore,\n",
     "    document_content_description,\n",
@@ -267,12 +362,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='d7b9ec1edafa467caab524455e8c1f5d', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
+       " Document(id='8ad04ef2a73d4f74897a51e49be1a8d2', metadata={'year': 1995, 'genre': 'animated'}, page_content='Toys come alive and have a blast doing so')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# This example only specifies a relevant query\n",
-    "retriever.invoke(\"What are two movies about dinosaurs?\")"
+    "retriever_k.invoke(\"What are two movies about dinosaurs?\")"
    ]
   },
   {
@@ -293,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -322,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

docs/docs/integrations/stores/astradb.ipynb

@@ -23,7 +23,9 @@
     "\n",
     "## Overview\n",
     "\n",
-    "DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API.\n",
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API.\n",
     "\n",
     "### Integration details\n",
     "\n",

docs/docs/integrations/text_embedding/google_generative_ai.ipynb

@@ -1,13 +1,76 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8543d632",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Google Gemini\n",
+    "keywords: [google gemini embeddings]\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "afab8b36-10bb-4795-bc98-75ab2d2081bb",
    "metadata": {},
    "source": [
-    "# Google Generative AI Embeddings\n",
+    "# Google Generative AI Embeddings (AI Studio & Gemini API)\n",
     "\n",
-    "Connect to Google's generative AI embeddings service using the `GoogleGenerativeAIEmbeddings` class, found in the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) package."
+    "Connect to Google's generative AI embeddings service using the `GoogleGenerativeAIEmbeddings` class, found in the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) package.\n",
+    "\n",
+    "This will help you get started with Google's Generative AI embedding models (like Gemini) using LangChain. For detailed documentation on `GoogleGenerativeAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_genai/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"text_embedding\" item=\"Google Gemini\" />\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Google Generative AI embedding models you'll need to create a Google Cloud project, enable the Generative Language API, get an API key, and install the `langchain-google-genai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "To use Google Generative AI models, you must have an API key. You can create one in Google AI Studio. See the [Google documentation](https://ai.google.dev/gemini-api/docs/api-key) for instructions.\n",
+    "\n",
+    "Once you have a key, set it as an environment variable `GOOGLE_API_KEY`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47652620",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"GOOGLE_API_KEY\"):\n",
+    "    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67283790",
+   "metadata": {},
+   "source": [
+    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eccf1968",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
@@ -28,28 +91,6 @@
     "%pip install --upgrade --quiet  langchain-google-genai"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "25f3f88e-164e-400d-b371-9fa488baba19",
-   "metadata": {},
-   "source": [
-    "## Credentials"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec89153f-8999-4aab-a21b-0bfba1cc3893",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"GOOGLE_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Provide your Google API key here\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f2437b22-e364-418a-8c13-490a026cb7b5",
@@ -60,17 +101,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "id": "eedc551e-a1f3-4fd8-8d65-4e0784c4441b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.05636945, 0.0048285457, -0.0762591, -0.023642512, 0.05329321]"
+       "[-0.024917153641581535,\n",
+       " 0.012005362659692764,\n",
+       " -0.003886754624545574,\n",
+       " -0.05774897709488869,\n",
+       " 0.0020742062479257584]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -78,7 +123,7 @@
    "source": [
     "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
     "\n",
-    "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/text-embedding-004\")\n",
+    "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/gemini-embedding-exp-03-07\")\n",
     "vector = embeddings.embed_query(\"hello, world!\")\n",
     "vector[:5]"
    ]
@@ -95,17 +140,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "6ec53aba-404f-4778-acd9-5d6664e79ed2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(3, 768)"
+       "(3, 3072)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -121,6 +166,56 @@
     "len(vectors), len(vectors[0])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c362bfbf",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "606a7f65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LangChain is the framework for building context-aware reasoning applications'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1482486f-5617-498a-8a44-1974d3212dda",
@@ -129,70 +224,74 @@
     "## Task type\n",
     "`GoogleGenerativeAIEmbeddings` optionally support a `task_type`, which currently must be one of:\n",
     "\n",
-    "- task_type_unspecified\n",
-    "- retrieval_query\n",
-    "- retrieval_document\n",
-    "- semantic_similarity\n",
-    "- classification\n",
-    "- clustering\n",
+    "- `SEMANTIC_SIMILARITY`: Used to generate embeddings that are optimized to assess text similarity.\n",
+    "- `CLASSIFICATION`: Used to generate embeddings that are optimized to classify texts according to preset labels.\n",
+    "- `CLUSTERING`: Used to generate embeddings that are optimized to cluster texts based on their similarities.\n",
+    "- `RETRIEVAL_DOCUMENT`, `RETRIEVAL_QUERY`, `QUESTION_ANSWERING`, and `FACT_VERIFICATION`: Used to generate embeddings that are optimized for document search or information retrieval.\n",
+    "- `CODE_RETRIEVAL_QUERY`: Used to retrieve a code block based on a natural language query, such as sort an array or reverse a linked list. Embeddings of the code blocks are computed using `RETRIEVAL_DOCUMENT`.\n",
     "\n",
-    "By default, we use `retrieval_document` in the `embed_documents` method and `retrieval_query` in the `embed_query` method. If you provide a task type, we will use that for all methods."
+    "By default, we use `RETRIEVAL_DOCUMENT` in the `embed_documents` method and `RETRIEVAL_QUERY` in the `embed_query` method. If you provide a task type, we will use that for all methods."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "a223bb25-2b1b-418e-a570-2f543083132e",
+   "execution_count": null,
+   "id": "b7acc5c2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet  matplotlib scikit-learn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "id": "f1f077db-8eb4-49f7-8866-471a8528dcdb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document 1\n",
+      "Cosine similarity with query: 0.7892893360164779\n",
+      "---\n",
+      "Document 2\n",
+      "Cosine similarity with query: 0.5438283285204146\n",
+      "---\n"
+     ]
+    }
+   ],
    "source": [
+    "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "\n",
     "query_embeddings = GoogleGenerativeAIEmbeddings(\n",
-    "    model=\"models/embedding-001\", task_type=\"retrieval_query\"\n",
+    "    model=\"models/gemini-embedding-exp-03-07\", task_type=\"RETRIEVAL_QUERY\"\n",
     ")\n",
     "doc_embeddings = GoogleGenerativeAIEmbeddings(\n",
-    "    model=\"models/embedding-001\", task_type=\"retrieval_document\"\n",
-    ")"
+    "    model=\"models/gemini-embedding-exp-03-07\", task_type=\"RETRIEVAL_DOCUMENT\"\n",
+    ")\n",
+    "\n",
+    "q_embed = query_embeddings.embed_query(\"What is the capital of France?\")\n",
+    "d_embed = doc_embeddings.embed_documents(\n",
+    "    [\"The capital of France is Paris.\", \"Philipp is likes to eat pizza.\"]\n",
+    ")\n",
+    "\n",
+    "for i, d in enumerate(d_embed):\n",
+    "    print(f\"Document {i+1}:\")\n",
+    "    print(f\"Cosine similarity with query: {cosine_similarity([q_embed], [d])[0][0]}\")\n",
+    "    print(\"---\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "79bd4a5e-75ba-413c-befa-86167c938caf",
+   "id": "f45ea7b1",
    "metadata": {},
    "source": [
-    "All of these will be embedded with the 'retrieval_query' task set\n",
-    "```python\n",
-    "query_vecs = [query_embeddings.embed_query(q) for q in [query, query_2, answer_1]]\n",
-    "```\n",
-    "All of these will be embedded with the 'retrieval_document' task set\n",
-    "```python\n",
-    "doc_vecs = [doc_embeddings.embed_query(q) for q in [query, query_2, answer_1]]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e1fae5e-0f84-4812-89f5-7d4d71affbc1",
-   "metadata": {},
-   "source": [
-    "In retrieval, relative distance matters. In the image above, you can see the difference in similarity scores between the \"relevant doc\" and \"simil stronger delta between the similar query and relevant doc on the latter case."
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `GoogleGenerativeAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_genai/embeddings/langchain_google_genai.embeddings.GoogleGenerativeAIEmbeddings.html).\n"
    ]
   },
   {
@@ -211,7 +310,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -225,7 +324,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/bash.ipynb

@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f7b3767b",
    "metadata": {
     "tags": []
@@ -83,40 +83,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "851fee9f",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "id": "7f056cc3-628d-46ba-b394-ee1d89f8650a",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
+      "Download the README here and identify the link for LangChain tutorials: https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  terminal (call_mr86V0d6E9nQiJZT7Xw5fH0G)\n",
+      " Call ID: call_mr86V0d6E9nQiJZT7Xw5fH0G\n",
+      "  Args:\n",
+      "    commands: ['curl -o README.md https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md']\n",
+      "Executing command:\n",
+      " ['curl -o README.md https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md']\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: terminal\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mQuestion: What is the task?\n",
-      "Thought: We need to download the langchain.com webpage and extract all the URLs from it. Then we need to sort the URLs and return them.\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"shell\",\n",
-      "  \"action_input\": {\n",
-      "    \"commands\": [\n",
-      "      \"curl -s https://langchain.com | grep -o 'http[s]*://[^\\\" ]*' | sort\"\n",
-      "    ]\n",
-      "  }\n",
-      "}\n",
-      "```\n",
-      "\u001b[0m"
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100  5169  100  5169    0     0   114k      0 --:--:-- --:--:-- --:--:--  114k\n",
+      "\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
+      "/langchain/libs/community/langchain_community/tools/shell/tool.py:33: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -124,50 +123,58 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  terminal (call_LF8TGrgS84WvUvaazYnVfib8)\n",
+      " Call ID: call_LF8TGrgS84WvUvaazYnVfib8\n",
+      "  Args:\n",
+      "    commands: [\"grep -i 'tutorial' README.md\"]\n",
+      "Executing command:\n",
+      " [\"grep -i 'tutorial' README.md\"]\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: terminal\n",
       "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mhttps://blog.langchain.dev/\n",
-      "https://discord.gg/6adMQxSpJS\n",
-      "https://docs.langchain.com/docs/\n",
-      "https://github.com/hwchase17/chat-langchain\n",
-      "https://github.com/hwchase17/langchain\n",
-      "https://github.com/hwchase17/langchainjs\n",
-      "https://github.com/sullivan-sean/chat-langchainjs\n",
-      "https://js.langchain.com/docs/\n",
-      "https://python.langchain.com/en/latest/\n",
-      "https://twitter.com/langchainai\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe URLs have been successfully extracted and sorted. We can return the list of URLs as the final answer.\n",
-      "Final Answer: [\"https://blog.langchain.dev/\", \"https://discord.gg/6adMQxSpJS\", \"https://docs.langchain.com/docs/\", \"https://github.com/hwchase17/chat-langchain\", \"https://github.com/hwchase17/langchain\", \"https://github.com/hwchase17/langchainjs\", \"https://github.com/sullivan-sean/chat-langchainjs\", \"https://js.langchain.com/docs/\", \"https://python.langchain.com/en/latest/\", \"https://twitter.com/langchainai\"]\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with\n",
+      "\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "'[\"https://blog.langchain.dev/\", \"https://discord.gg/6adMQxSpJS\", \"https://docs.langchain.com/docs/\", \"https://github.com/hwchase17/chat-langchain\", \"https://github.com/hwchase17/langchain\", \"https://github.com/hwchase17/langchainjs\", \"https://github.com/sullivan-sean/chat-langchainjs\", \"https://js.langchain.com/docs/\", \"https://python.langchain.com/en/latest/\", \"https://twitter.com/langchainai\"]'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/langchain/libs/community/langchain_community/tools/shell/tool.py:33: UserWarning: The shell tool has no safeguards by default. Use at your own risk.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The link for LangChain tutorials in the README is: https://python.langchain.com/docs/tutorials/\n"
+     ]
     }
    ],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "llm = ChatOpenAI(temperature=0)\n",
+    "tools = [shell_tool]\n",
+    "agent = create_react_agent(\"openai:gpt-4.1-mini\", tools)\n",
     "\n",
-    "shell_tool.description = shell_tool.description + f\"args {shell_tool.args}\".replace(\n",
-    "    \"{\", \"{{\"\n",
-    ").replace(\"}\", \"}}\")\n",
-    "self_ask_with_search = initialize_agent(\n",
-    "    [shell_tool], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")\n",
-    "self_ask_with_search.run(\n",
-    "    \"Download the langchain.com webpage and grep for all urls. Return only a sorted list of them. Be sure to use double quotes.\"\n",
-    ")"
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": (\n",
+    "        \"Download the README here and identify the link for LangChain tutorials: \"\n",
+    "        \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": [input_message]},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
   },
   {
@@ -195,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/compass.ipynb

@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Compass DeFi Toolkit\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# Compass LangChain Toolkit\n",
+    "\n",
+    "Interact with various DeFi protocols. Non-custodial.",
+    "Tools return *unsigned transactions*. The toolkit is built on top of a Universal DeFi API ([Compass API](https://api.compasslabs.ai/)) allowing agents to perform financial operations like:\n",
+    "\n",
+    "- **Swapping tokens** on Uniswap and Aerodrome\n",
+    "- **Lending** or **borrowing** assets using protocols on Aave\n",
+    "- **Providing liquidity** on Aerodrome and Uniswap\n",
+    "- **Transferring funds** between wallets.\n",
+    "- Querying balances, portfolios and **monitoring positions**.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class                    | Package             | Serializable | JS support |                                         Package latest                                         |\n",
+    "|:-------------------------|:--------------------| :---: | :---: |:----------------------------------------------------------------------------------------------:|\n",
+    "| LangchainCompassToolkit  | `langchain-compass` | ❌ | ❌ | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-compass?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Tool features\n",
+    "\n",
+    "Here’s a sample of the tools this toolkit provides (subject to change daily):\n",
+    "\n",
+    "- `aave_supply`: Supply assets to Aave to earn interest.\n",
+    "- `aave_borrow`: Borrow assets from Aave using collateral.\n",
+    "- `uniswap_swap_sell_exactly`: Swap a specific amount of one token on Uniswap.\n",
+    "- `generic_portfolio_get`: Retrieve a wallet’s portfolio in USD and token balances.\n",
+    "- `generic_transfer_erc20`: Transfer ERC20 tokens between addresses.\n",
+    "\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Here we will:\n",
+    "\n",
+    "1. Install the langchain package\n",
+    "2. Import and instantiate the toolkit\n",
+    "3. Pass the tools to your agent with `toolkit.get_tools()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This toolkit lives in the `langchain-compass` package:"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "%pip install -qU langchain-compass",
+   "id": "652d6238"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65",
+   "metadata": {},
+   "source": [
+    "#### Environment Setup\n",
+    "\n",
+    "To run these examples, ensure LangChain has access to an LLM service. For instance, if you're using GPT-4o, create a `.env` file containing:\n",
+    "\n",
+    "```plaintext\n",
+    "# .env file\n",
+    "OPENAI_API_KEY=<your_openai_api_key_here>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839",
+   "metadata": {},
+   "source": [
+    "### Instantiation\n",
+    "\n",
+    "Now we can instantiate our toolkit:"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T15:00:25.188941Z",
+     "start_time": "2025-04-16T15:00:23.842165Z"
+    }
+   },
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": 3,
+   "source": [
+    "from langchain_compass.toolkits import LangchainCompassToolkit\n",
+    "\n",
+    "toolkit = LangchainCompassToolkit(compass_api_key=None)"
+   ],
+   "id": "51a60dbe"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d11245ad",
+   "metadata": {},
+   "source": [
+    "### Tools\n",
+    "\n",
+    "View [available tools](#tool-features):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "310bf18e",
+   "metadata": {},
+   "source": [
+    "tools = toolkit.get_tools()\n",
+    "for tool in tools:\n",
+    "    print(tool.name)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "```\n",
+    "# Expected output:\n",
+    "\n",
+    "aave_supply\n",
+    "aave_borrow\n",
+    "aave_repay\n",
+    "aave_withdraw\n",
+    "aave_asset_price_get\n",
+    "aave_liquidity_change_get\n",
+    "aave_user_position_summary_get\n",
+    "...\n",
+    "```"
+   ],
+   "id": "c3fd41b52c203e03"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "To invoke a single tool programmatically:"
+   ],
+   "id": "73b871e54cf1996"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T15:16:33.924523Z",
+     "start_time": "2025-04-16T15:16:33.564016Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "tool_name = \"generic_ens_get\"\n",
+    "tool = next(tool for tool in tools if tool.name == tool_name)\n",
+    "tool.invoke({\"ens_name\": \"vitalik.eth\", \"chain\": \"ethereum:mainnet\"})"
+   ],
+   "id": "e384e604c38f07de",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EnsNameInfoResponse(wallet_address='0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045', registrant='0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e11cc9",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "\n",
+    "We will need a LLM or chat model:"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T15:00:27.027533Z",
+     "start_time": "2025-04-16T15:00:26.364789Z"
+    }
+   },
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": 5,
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o\")"
+   ],
+   "id": "d1ee55bc"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca",
+   "metadata": {},
+   "source": [
+    "Initialize the agent with the tools:"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-16T15:00:27.948912Z",
+     "start_time": "2025-04-16T15:00:27.033842Z"
+    }
+   },
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": 6,
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "tools = toolkit.get_tools()\n",
+    "agent_executor = create_react_agent(llm, tools)"
+   ],
+   "id": "f8a2c4b1"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4a7c9d2",
+   "metadata": {},
+   "source": "Example usage:"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "example_query = \"please set an allowance on Uniswap of 10 WETH for vitalic.eth.\"  # spelt wrong intentionally\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()"
+   ],
+   "id": "c9a8e4f3"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5a7c9d4",
+   "metadata": {},
+   "source": [
+    "Expected output:\n",
+    "```\n",
+    "================================\u001B[1m Human Message \u001B[0m=================================\n",
+    "\n",
+    "please set an allowance on Uniswap of 10 WETH for vitalic.eth.\n",
+    "==================================\u001B[1m Ai Message \u001B[0m==================================\n",
+    "Tool Calls:\n",
+    "  generic_ens_get (call_MHIXRXxWH0L7iUEYHwvDUdU1)\n",
+    " Call ID: call_MHIXRXxWH0L7iUEYHwvDUdU1\n",
+    "  Args:\n",
+    "    chain: ethereum:mainnet\n",
+    "    ens_name: vitalic.eth\n",
+    "=================================\u001B[1m Tool Message \u001B[0m=================================\n",
+    "Name: generic_ens_get\n",
+    "\n",
+    "wallet_address='0x44761Ef63FaD902D8f8dC77e559Ab116929881Db' registrant='0x44761Ef63FaD902D8f8dC77e559Ab116929881Db'\n",
+    "==================================\u001B[1m Ai Message \u001B[0m==================================\n",
+    "Tool Calls:\n",
+    "  generic_allowance_set (call_IEBftbtBfKCkI1zFXXtEY8tq)\n",
+    " Call ID: call_IEBftbtBfKCkI1zFXXtEY8tq\n",
+    "  Args:\n",
+    "    amount: 10\n",
+    "    chain: ethereum:mainnet\n",
+    "    contract_name: UniswapV3Router\n",
+    "    sender: 0x44761Ef63FaD902D8f8dC77e559Ab116929881Db\n",
+    "    token: WETH\n",
+    "=================================\u001B[1m Tool Message \u001B[0m=================================\n",
+    "Name: generic_allowance_set\n",
+    "\n",
+    "{\"type\": \"unsigned_transaction\", \"content\": {\"chainId\": 1, \"data\": \"0x095ea7b300000000000000000000000068b3465833fb72a70ecdf485e0e4c7bd8665fc450000000000000000000000000000000000000000000000008ac7230489e80000\", \"from\": \"0x44761Ef63FaD902D8f8dC77e559Ab116929881Db\", \"gas\": 46434, \"to\": \"0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2\", \"value\": 0, \"nonce\": 79, \"maxFeePerGas\": 2265376912, \"maxPriorityFeePerGas\": 6400594}}\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## API reference\n",
+    "\n",
+    "`langchain-compass` is built on top of the Compass API. Each tool corresponds to an API endpoint. [Please check out the docs here](https://api.compasslabs.ai/)"
+   ],
+   "id": "2fe1ec8c22e5e79f"
+  }
+ ],
+ "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/integrations/tools/google_drive.ipynb

@@ -16,7 +16,7 @@
     "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n",
     "\n",
     "## Instructions for retrieving your Google Docs data\n",
-    "By default, the `GoogleDriveTools` and `GoogleDriveWrapper` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `GOOGLE_ACCOUNT_FILE` environment variable. \n",
+    "By default, the `GoogleDriveTools` and `GoogleDriveWrapper` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable by setting the `GOOGLE_ACCOUNT_FILE` environment variable to your `custom/path/to/credentials.json`. \n",
     "The location of `token.json` use the same directory (or use the parameter `token_path`). Note that `token.json` will be created automatically the first time you use the tool.\n",
     "\n",
     "`GoogleDriveSearchTool` can retrieve a selection of files with some requests. \n",
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  unstructured"
+    "%pip install --upgrade --quiet  unstructured langchain-googledrive"
    ]
   },
   {
@@ -99,9 +99,13 @@
    },
    "outputs": [],
    "source": [
+    "import os\n",
+    "\n",
     "from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool\n",
     "from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper\n",
     "\n",
+    "os.environ[\"GOOGLE_ACCOUNT_FILE\"] = \"custom/path/to/credentials.json\"\n",
+    "\n",
     "# By default, search only in the filename.\n",
     "tool = GoogleDriveSearchTool(\n",
     "    api_wrapper=GoogleDriveAPIWrapper(\n",
@@ -114,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,33 +138,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"A wrapper around Google Drive Search. Useful for when you need to find a document in google drive. The input should be formatted as a list of entities separated with a space. As an example, a list of keywords is 'hello word'.\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tool.description"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import load_tools\n",
-    "\n",
-    "tools = load_tools(\n",
-    "    [\"google-drive-search\"],\n",
-    "    folder_id=folder_id,\n",
-    "    template=\"gdrive-query-in-folder\",\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Use within an Agent"
+    "## Use the tool within a ReAct agent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to create an agent that uses the Google Jobs tool install Langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and use the `create_react_agent` functionality to initialize a ReAct agent. You will also need to set up your OPEN_API_KEY (visit https://platform.openai.com) in order to access OpenAI's chat models."
    ]
   },
   {
@@ -171,32 +194,29 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain_openai import OpenAI\n",
+    "import os\n",
     "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "agent = initialize_agent(\n",
-    "    tools=tools,\n",
-    "    llm=llm,\n",
-    "    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "agent.run(\"Search in google drive, who is 'Yann LeCun' ?\")"
+    "from langchain.chat_models import init_chat_model\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"\n",
+    "\n",
+    "\n",
+    "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\", temperature=0)\n",
+    "agent = create_react_agent(llm, tools=[tool])\n",
+    "\n",
+    "events = agent.stream(\n",
+    "    {\"messages\": [(\"user\", \"Search in google drive, who is 'Yann LeCun' ?\")]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -210,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/google_finance.ipynb

@@ -6,24 +6,11 @@
    "source": [
     "# Google Finance\n",
     "\n",
-    "This notebook goes over how to use the Google Finance Tool to get information from the Google Finance page\n",
+    "This notebook goes over how to use the Google Finance Tool to get information from the Google Finance page.\n",
     "\n",
     "To get an SerpApi key key, sign up at: https://serpapi.com/users/sign_up.\n",
     "\n",
-    "Then install google-search-results with the command: \n",
-    "\n",
-    "pip install google-search-results\n",
-    "\n",
-    "Then set the environment variable SERPAPI_API_KEY to your SerpApi key\n",
-    "\n",
-    "Or pass the key in as a argument to the wrapper serp_api_key=\"your secret key\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the Tool"
+    "To use the tool with Langchain install following packages"
    ]
   },
   {
@@ -35,9 +22,16 @@
     "%pip install --upgrade --quiet  google-search-results langchain-community"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then set the environment variable SERPAPI_API_KEY to your SerpApi key or pass the key in as a argument to the wrapper serp_api_key=\"your secret key\"."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,15 +40,26 @@
     "from langchain_community.tools.google_finance import GoogleFinanceQueryRun\n",
     "from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper\n",
     "\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"\"\n",
+    "os.environ[\"SERPAPI_API_KEY\"] = \"[your serpapi key]\"\n",
     "tool = GoogleFinanceQueryRun(api_wrapper=GoogleFinanceAPIWrapper())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nQuery: Google\\nstock: GOOGL:NASDAQ\\nprice: $161.96\\npercentage: 1.68\\nmovement: Up\\n'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tool.run(\"Google\")"
    ]
@@ -63,7 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using it with Langchain"
+    "In order to create an agent that uses the Google Finance tool install Langgraph"
    ]
   },
   {
@@ -71,26 +76,77 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and use the `create_react_agent` functionality to initialize a ReAct agent. You will also need to set up your OPEN_API_KEY (visit https://platform.openai.com) in order to access OpenAI's chat models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "What is Google's stock?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  google_finance (call_u676mJAkdojgkW806ZGSE8mF)\n",
+      " Call ID: call_u676mJAkdojgkW806ZGSE8mF\n",
+      "  Args:\n",
+      "    query: Google\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: google_finance\n",
+      "\n",
+      "\n",
+      "Query: Google\n",
+      "stock: GOOGL:NASDAQ\n",
+      "price: $161.96\n",
+      "percentage: 1.68\n",
+      "movement: Up\n",
+      "\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Google's stock (Ticker: GOOGL) is currently priced at $161.96, showing an increase of 1.68%.\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
-    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain_openai import OpenAI\n",
+    "from langchain.agents import load_tools\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
-    "os.environ[\"SERP_API_KEY\"] = \"\"\n",
-    "llm = OpenAI()\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"[your openai key]\"\n",
+    "os.environ[\"SERP_API_KEY\"] = \"[your serpapi key]\"\n",
+    "\n",
+    "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\")\n",
     "tools = load_tools([\"google-scholar\", \"google-finance\"], llm=llm)\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    "agent = create_react_agent(llm, tools)\n",
+    "\n",
+    "events = agent.stream(\n",
+    "    {\"messages\": [(\"user\", \"What is Google's stock?\")]},\n",
+    "    stream_mode=\"values\",\n",
     ")\n",
-    "agent.run(\"what is google's stock\")"
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -104,7 +160,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/graphql.ipynb

@@ -12,33 +12,49 @@
     "\n",
     "This Jupyter Notebook demonstrates how to use the `GraphQLAPIWrapper` component with an Agent.\n",
     "\n",
-    "In this example, we'll be using the public `Star Wars GraphQL API` available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.\n",
+    "In this example, we'll be using the public `Star Wars GraphQL API` available at the following endpoint: https://swapi-graphql.netlify.app/graphql .\n",
     "\n",
     "First, you need to install `httpx` and `gql` Python packages."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "vscode": {
      "languageId": "shellscript"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "pip install httpx gql > /dev/null"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "vscode": {
      "languageId": "shellscript"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install --upgrade --quiet  langchain-community"
    ]
@@ -56,21 +72,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain_openai import OpenAI\n",
+    "import os\n",
     "\n",
-    "llm = OpenAI(temperature=0)\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
     "\n",
     "tools = load_tools(\n",
     "    [\"graphql\"],\n",
-    "    graphql_endpoint=\"https://swapi-graphql.netlify.app/.netlify/functions/index\",\n",
-    ")\n",
-    "\n",
-    "agent = initialize_agent(\n",
-    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    "    graphql_endpoint=\"https://swapi-graphql.netlify.app/graphql\",\n",
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent = create_react_agent(\"openai:gpt-4.1-mini\", tools)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -80,35 +111,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Search for the titles of all the stawars films stored in the graphql database that has this schema allFilms {\n",
+      "    films {\n",
+      "      title\n",
+      "      director\n",
+      "      releaseDate\n",
+      "      speciesConnection {\n",
+      "        species {\n",
+      "          name\n",
+      "          classification\n",
+      "          homeworld {\n",
+      "            name\n",
+      "          }\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to query the graphql database to get the titles of all the star wars films\n",
-      "Action: query_graphql\n",
-      "Action Input: query { allFilms { films { title } } }\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m\"{\\n  \\\"allFilms\\\": {\\n    \\\"films\\\": [\\n      {\\n        \\\"title\\\": \\\"A New Hope\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"The Empire Strikes Back\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"Return of the Jedi\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"The Phantom Menace\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"Attack of the Clones\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"Revenge of the Sith\\\"\\n      }\\n    ]\\n  }\\n}\"\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the titles of all the star wars films\n",
-      "Final Answer: The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.\u001b[0m\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  query_graphql (call_tN5A0dBbfOMewuw8Yy13bYpW)\n",
+      " Call ID: call_tN5A0dBbfOMewuw8Yy13bYpW\n",
+      "  Args:\n",
+      "    tool_input: query { allFilms { films { title } } }\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: query_graphql\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\"{\\n  \\\"allFilms\\\": {\\n    \\\"films\\\": [\\n      {\\n        \\\"title\\\": \\\"A New Hope\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"The Empire Strikes Back\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"Return of the Jedi\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"The Phantom Menace\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"Attack of the Clones\\\"\\n      },\\n      {\\n        \\\"title\\\": \\\"Revenge of the Sith\\\"\\n      }\\n    ]\\n  }\\n}\"\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The titles of all the Star Wars films stored in the database are:\n",
+      "1. A New Hope\n",
+      "2. The Empire Strikes Back\n",
+      "3. Return of the Jedi\n",
+      "4. The Phantom Menace\n",
+      "5. Attack of the Clones\n",
+      "6. Revenge of the Sith\n",
+      "\n",
+      "If you would like more information about any of these films, please let me know!\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -133,9 +184,24 @@
     "\n",
     "suffix = \"Search for the titles of all the stawars films stored in the graphql database that has this schema \"\n",
     "\n",
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": suffix + graphql_fields,\n",
+    "}\n",
     "\n",
-    "agent.run(suffix + graphql_fields)"
+    "for step in agent.stream(\n",
+    "    {\"messages\": [input_message]},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -157,7 +223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/openweathermap.ipynb

@@ -22,8 +22,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "34bb5968",
+   "execution_count": 1,
+   "id": "8b81a74e-db10-4e8d-9f90-83219df30ab3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install --upgrade --quiet pyowm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "78ab9fcd-bb7b-434b-9a38-0a9249e35768",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,8 +56,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "ac4910f8",
+   "execution_count": 3,
+   "id": "0a8aa4b0-6aea-4172-9546-361e127a4a02",
    "metadata": {},
    "outputs": [
     {
@@ -47,17 +65,17 @@
      "output_type": "stream",
      "text": [
       "In London,GB, the current weather is as follows:\n",
-      "Detailed status: broken clouds\n",
-      "Wind speed: 2.57 m/s, direction: 240°\n",
-      "Humidity: 55%\n",
+      "Detailed status: overcast clouds\n",
+      "Wind speed: 4.12 m/s, direction: 10°\n",
+      "Humidity: 51%\n",
       "Temperature: \n",
-      "  - Current: 20.12°C\n",
-      "  - High: 21.75°C\n",
-      "  - Low: 18.68°C\n",
-      "  - Feels like: 19.62°C\n",
+      "  - Current: 12.82°C\n",
+      "  - High: 13.98°C\n",
+      "  - Low: 12.01°C\n",
+      "  - Feels like: 11.49°C\n",
       "Rain: {}\n",
       "Heat index: None\n",
-      "Cloud cover: 75%\n"
+      "Cloud cover: 100%\n"
      ]
     }
    ],
@@ -76,76 +94,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "b3367417",
+   "execution_count": 4,
+   "id": "402c832c-87c7-4088-b80f-ec1924a43796",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "\n",
-    "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain_openai import OpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
     "os.environ[\"OPENWEATHERMAP_API_KEY\"] = \"\"\n",
     "\n",
-    "llm = OpenAI(temperature=0)\n",
-    "\n",
-    "tools = load_tools([\"openweathermap-api\"], llm)\n",
-    "\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
-    ")"
+    "tools = [weather.run]\n",
+    "agent = create_react_agent(\"openai:gpt-4.1-mini\", tools)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "bf4f6854",
+   "execution_count": 5,
+   "id": "9b423a92-1568-4ee2-9c7d-3b9acf7756a1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
+      "What's the weather like in London?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  run (call_6vPq9neyy7oOnht29ExidE2g)\n",
+      " Call ID: call_6vPq9neyy7oOnht29ExidE2g\n",
+      "  Args:\n",
+      "    location: London\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: run\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out the current weather in London.\n",
-      "Action: OpenWeatherMap\n",
-      "Action Input: London,GB\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mIn London,GB, the current weather is as follows:\n",
-      "Detailed status: broken clouds\n",
-      "Wind speed: 2.57 m/s, direction: 240°\n",
-      "Humidity: 56%\n",
+      "In London, the current weather is as follows:\n",
+      "Detailed status: overcast clouds\n",
+      "Wind speed: 4.12 m/s, direction: 10°\n",
+      "Humidity: 51%\n",
       "Temperature: \n",
-      "  - Current: 20.11°C\n",
-      "  - High: 21.75°C\n",
-      "  - Low: 18.68°C\n",
-      "  - Feels like: 19.64°C\n",
+      "  - Current: 12.82°C\n",
+      "  - High: 13.98°C\n",
+      "  - Low: 12.01°C\n",
+      "  - Feels like: 11.49°C\n",
       "Rain: {}\n",
       "Heat index: None\n",
-      "Cloud cover: 75%\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the current weather in London.\n",
-      "Final Answer: The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None.\u001b[0m\n",
+      "Cloud cover: 100%\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "The weather in London is currently overcast with 100% cloud cover. The temperature is around 12.82°C, feeling like 11.49°C. The wind is blowing at 4.12 m/s from the direction of 10°. Humidity is at 51%. The high for the day is 13.98°C, and the low is 12.01°C.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None.'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_chain.run(\"What's the weather like in London?\")"
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": \"What's the weather like in London?\",\n",
+    "}\n",
+    "\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": [input_message]},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2af226a-9cca-468d-b07f-0a928ea61f48",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -164,7 +188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/tavily_extract.ipynb

@@ -19,9 +19,9 @@
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class                                                         | Package                                                        | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/tavily_search) |  Package latest |\n",
+    "| Class                                                         | Package                                                        | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/tavily_extract/) |  Package latest |\n",
     "|:--------------------------------------------------------------|:---------------------------------------------------------------| :---: | :---: | :---: |\n",
-    "| [TavilyExtract](https://github.com/tavily-ai/langchain-tavily) | [langchain-tavily](https://pypi.org/project/langchain-tavily/) | ✅ | ❌  |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-tavily?style=flat-square&label=%20) |\n",
+    "| [TavilyExtract](https://github.com/tavily-ai/langchain-tavily) | [langchain-tavily](https://pypi.org/project/langchain-tavily/) | ✅ | ✅  |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-tavily?style=flat-square&label=%20) |\n",
     "\n",
     "### Tool features\n",
     "| [Returns artifact](/docs/how_to/tool_artifacts/) | Native async |                       Return data                        | Pricing |\n",

docs/docs/integrations/tools/tavily_search.ipynb

@@ -20,7 +20,7 @@
     "### Integration details\n",
     "| Class                                                         | Package                                                        | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/tavily_search) |  Package latest |\n",
     "|:--------------------------------------------------------------|:---------------------------------------------------------------| :---: | :---: | :---: |\n",
-    "| [TavilySearch](https://github.com/tavily-ai/langchain-tavily) | [langchain-tavily](https://pypi.org/project/langchain-tavily/) | ✅ | ❌  |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-tavily?style=flat-square&label=%20) |\n",
+    "| [TavilySearch](https://github.com/tavily-ai/langchain-tavily) | [langchain-tavily](https://pypi.org/project/langchain-tavily/) | ✅ | ✅  |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-tavily?style=flat-square&label=%20) |\n",
     "\n",
     "### Tool features\n",
     "| [Returns artifact](/docs/how_to/tool_artifacts/) | Native async |                       Return data                        | Pricing |\n",

docs/docs/integrations/tools/yahoo_finance_news.ipynb

@@ -17,10 +17,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "38717a85-2c3c-4452-a1c7-1ed4dea3da86",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install --upgrade --quiet  yfinance"
    ]
@@ -35,121 +43,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "d137dd6c-d3d3-4813-af65-59eaaa6b3d76",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"...\""
+    "os.environ[\"OPENAI_API_KEY\"] = \"..\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "fc42f766-9ce6-4ba3-be6c-5ba8a345b0d3",
+   "execution_count": null,
+   "id": "af297977-4fc3-421f-9ce1-f62c1c5b026a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
    "source": [
-    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain_community.tools.yahoo_finance_news import YahooFinanceNewsTool\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "llm = ChatOpenAI(temperature=0.0)\n",
     "tools = [YahooFinanceNewsTool()]\n",
-    "agent_chain = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    verbose=True,\n",
-    ")"
+    "agent = create_react_agent(\"openai:gpt-4.1-mini\", tools)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "3d1614b4-508e-4689-84b1-2a387f80aeb1",
+   "execution_count": 4,
+   "id": "ac3cbec8-4135-4f5a-bb35-299730c000bd",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
+      "What happened today with Microsoft stocks?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  yahoo_finance_news (call_s1Waj1rAoJ89CfxWX1RWDiWL)\n",
+      " Call ID: call_s1Waj1rAoJ89CfxWX1RWDiWL\n",
+      "  Args:\n",
+      "    query: MSFT\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: yahoo_finance_news\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI should check the latest financial news about Microsoft stocks.\n",
-      "Action: yahoo_finance_news\n",
-      "Action Input: MSFT\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mMicrosoft (MSFT) Gains But Lags Market: What You Should Know\n",
-      "In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI have the latest information on Microsoft stocks.\n",
-      "Final Answer: Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.\u001b[0m\n",
+      "Microsoft (MSFT), Meta Platforms (META) Reported “Home Run” Results: Dan Ives’ Recent Comments\n",
+      "Microsoft (MSFT) and Meta Platforms (META) delivered “home run” results yesterday, as the AI Revolution has not been slowed by the Trump administration’s tariffs, Dan Ives, the Managing Director and Senior Equity Research Analyst at Wedbush Securities said on CNBC recently. Ives covers tech stocks. Tech Is Poised for a Comeback, Ives Indicates “The tech […]\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Today, Microsoft (MSFT) reported strong financial results, described as \"home run\" results by Dan Ives, Managing Director and Senior Equity Research Analyst at Wedbush Securities. Despite the Trump administration’s tariffs, the AI Revolution driving tech stocks like Microsoft has not slowed down, indicating a positive outlook for the company and the tech sector overall.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_chain.invoke(\n",
-    "    \"What happened today with Microsoft stocks?\",\n",
-    ")"
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": \"What happened today with Microsoft stocks?\",\n",
+    "}\n",
+    "\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": [input_message]},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "c899b64d-86a5-452c-b576-e94f485c27ea",
+   "execution_count": 5,
+   "id": "4496b06b-8b57-4fa8-9b86-4db407caa807",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
+      "How does Microsoft feels today comparing with Nvidia?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  yahoo_finance_news (call_r9m4YxdEqWeXotkNgK8jGzeJ)\n",
+      " Call ID: call_r9m4YxdEqWeXotkNgK8jGzeJ\n",
+      "  Args:\n",
+      "    query: MSFT\n",
+      "  yahoo_finance_news (call_fxj3AIKPB4MYuquvFFWrBD8B)\n",
+      " Call ID: call_fxj3AIKPB4MYuquvFFWrBD8B\n",
+      "  Args:\n",
+      "    query: NVDA\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: yahoo_finance_news\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mI should compare the current sentiment of Microsoft and Nvidia.\n",
-      "Action: yahoo_finance_news\n",
-      "Action Input: MSFT\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mMicrosoft (MSFT) Gains But Lags Market: What You Should Know\n",
-      "In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day.\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI need to find the current sentiment of Nvidia as well.\n",
-      "Action: yahoo_finance_news\n",
-      "Action Input: NVDA\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the current sentiment of both Microsoft and Nvidia.\n",
-      "Final Answer: I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.\u001b[0m\n",
+      "NVIDIA Corporation (NVDA): Among Ken Fisher’s Technology Stock Picks with Huge Upside Potential\n",
+      "We recently published an article titled Billionaire Ken Fisher’s 10 Technology Stock Picks with Huge Upside Potential. In this article, we are going to take a look at where NVIDIA Corporation (NASDAQ:NVDA) stands against the other technology stocks. Technology stocks have faced heightened volatility in 2025, with market sentiment swinging sharply in response to President Donald […]\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "Nvidia (NVDA) Redesigns Chips to Sidestep U.S. Export Ban, Eyes June China Rollout\n",
+      "Nvidia plans China-specific AI chip revamp after new U.S. export limits\n",
+      "\n",
+      "Is NVIDIA (NVDA) the Best NASDAQ Stock to Buy According to Billionaires?\n",
+      "We recently published a list of 10 Best NASDAQ Stocks to Buy According to Billionaires. In this article, we are going to take a look at where NVIDIA Corporation (NASDAQ:NVDA) stands against other best NASDAQ stocks to buy according to billionaires. The latest market data shows that the US economy contracted at an annualized rate […]\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Today, Microsoft (MSFT) is viewed positively with recent strong earnings reported, described as \"home run\" results, indicating confidence in its performance amid an ongoing AI revolution.\n",
+      "\n",
+      "Nvidia (NVDA) is also in focus with its strategic moves, such as redesigning AI chips to bypass U.S. export bans and targeting a China rollout. It is considered one of the technology stocks with significant upside potential, attracting attention from notable investors.\n",
+      "\n",
+      "In summary, both Microsoft and Nvidia have positive sentiments today, with Microsoft showing strong financial results and Nvidia making strategic advancements in AI technology and market positioning.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.'"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_chain.invoke(\n",
-    "    \"How does Microsoft feels today comparing with Nvidia?\",\n",
-    ")"
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": \"How does Microsoft feels today comparing with Nvidia?\",\n",
+    "}\n",
+    "\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": [input_message]},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
   },
   {
@@ -162,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "id": "7879b79c-b5c7-4a5d-8338-edda53ff41a6",
    "metadata": {},
    "outputs": [],
@@ -172,17 +195,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "id": "ac989456-33bc-4478-874e-98b9cb24d113",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'No news found for company that searched with NVDA ticker.'"
+       "'NVIDIA Corporation (NVDA): Among Ken Fisher’s Technology Stock Picks with Huge Upside Potential\\nWe recently published an article titled Billionaire Ken Fisher’s 10 Technology Stock Picks with Huge Upside Potential. In this article, we are going to take a look at where NVIDIA Corporation (NASDAQ:NVDA) stands against the other technology stocks. Technology stocks have faced heightened volatility in 2025, with market sentiment swinging sharply in response to President Donald […]\\n\\nNvidia (NVDA) Redesigns Chips to Sidestep U.S. Export Ban, Eyes June China Rollout\\nNvidia plans China-specific AI chip revamp after new U.S. export limits\\n\\nIs NVIDIA (NVDA) the Best NASDAQ Stock to Buy According to Billionaires?\\nWe recently published a list of 10 Best NASDAQ Stocks to Buy According to Billionaires. In this article, we are going to take a look at where NVIDIA Corporation (NASDAQ:NVDA) stands against other best NASDAQ stocks to buy according to billionaires. The latest market data shows that the US economy contracted at an annualized rate […]'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 8,
    "id": "46c697aa-102e-48d4-9834-081671aad40a",
    "metadata": {},
    "outputs": [
@@ -201,11 +224,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Top Research Reports for Apple, Broadcom & Caterpillar\n",
-      "Today's Research Daily features new research reports on 16 major stocks, including Apple Inc. (AAPL), Broadcom Inc. (AVGO) and Caterpillar Inc. (CAT).\n",
-      "\n",
-      "Apple Stock on Pace for Worst Month of the Year\n",
-      "Apple (AAPL) shares are on pace for their worst month of the year, according to Dow Jones Market Data.  The stock is down 4.8% so far in August, putting it on pace for its worst month since December 2022, when it fell 12%.\n"
+      "No news found for company that searched with AAPL ticker.\n"
      ]
     }
    ],
@@ -239,7 +258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/astradb.ipynb

@@ -7,9 +7,11 @@
    "source": [
     "# Astra DB Vector Store\n",
     "\n",
-    "This page provides a quickstart for using [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) as a Vector Store.\n",
+    "This page provides a quickstart for using Astra DB as a Vector Store.\n",
     "\n",
-    "> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Apache Cassandra® and made conveniently available through an easy-to-use JSON API.\n",
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API.\n",
     "\n",
     "## Setup"
    ]
@@ -19,6 +21,8 @@
    "id": "dbe7c156-0413-47e3-9237-4769c4248869",
    "metadata": {},
    "source": [
+    "### Dependencies\n",
+    "\n",
     "Use of the integration requires the `langchain-astradb` partner package:"
    ]
   },
@@ -26,10 +30,15 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8d00fcf4-9798-4289-9214-d9734690adfc",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "pip install -qU \"langchain-astradb>=0.3.3\""
+    "!pip install \\\n",
+    "    \"langchain>=0.3.23,<0.4\" \\\n",
+    "    \"langchain-core>=0.3.52,<0.4\" \\\n",
+    "    \"langchain-astradb>=0.6,<0.7\""
    ]
   },
   {
@@ -41,30 +50,40 @@
     "\n",
     "In order to use the AstraDB vector store, you must first head to the [AstraDB website](https://astra.datastax.com), create an account, and then create a new database - the initialization might take a few minutes. \n",
     "\n",
-    "Once the database has been initialized, you should [create an application token](https://docs.datastax.com/en/astra-db-serverless/administration/manage-application-tokens.html#generate-application-token) and save it for later use. \n",
+    "Once the database has been initialized, retrieve your [connection secrets](https://docs.datastax.com/en/astra-db-serverless/get-started/quickstart.html#create-a-database-and-store-your-credentials), which you'll need momentarily. These are:\n",
+    "- an **`API Endpoint`**, such as `\"https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com/\"`\n",
+    "- and a **`Database Token`**, e.g. `\"AstraCS:aBcD123......\"`\n",
     "\n",
-    "You will also want to copy the `API Endpoint` from the `Database Details` and store that in the `ASTRA_DB_API_ENDPOINT` variable.\n",
-    "\n",
-    "You may optionally provide a namespace, which you can manage from the `Data Explorer` tab of your database dashboard. If you don't wish to set a namespace, you can leave the `getpass` prompt for `ASTRA_DB_NAMESPACE` empty."
+    "You may optionally provide a **`keyspace`** (called \"namespace\" in the LangChain components), which you can manage from the `Data Explorer` tab of your database dashboard. If you wish, you can leave it empty in the prompt below and fall back to a default keyspace."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "id": "b7843c22",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
+      "ASTRA_DB_APPLICATION_TOKEN =  ········\n",
+      "(optional) ASTRA_DB_KEYSPACE =  \n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "\n",
-    "ASTRA_DB_API_ENDPOINT = getpass.getpass(\"ASTRA_DB_API_ENDPOINT = \")\n",
-    "ASTRA_DB_APPLICATION_TOKEN = getpass.getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")\n",
+    "ASTRA_DB_API_ENDPOINT = input(\"ASTRA_DB_API_ENDPOINT = \").strip()\n",
+    "ASTRA_DB_APPLICATION_TOKEN = getpass.getpass(\"ASTRA_DB_APPLICATION_TOKEN = \").strip()\n",
     "\n",
-    "desired_namespace = getpass.getpass(\"ASTRA_DB_NAMESPACE = \")\n",
-    "if desired_namespace:\n",
-    "    ASTRA_DB_NAMESPACE = desired_namespace\n",
+    "desired_keyspace = input(\"(optional) ASTRA_DB_KEYSPACE = \").strip()\n",
+    "if desired_keyspace:\n",
+    "    ASTRA_DB_KEYSPACE = desired_keyspace\n",
     "else:\n",
-    "    ASTRA_DB_NAMESPACE = None"
+    "    ASTRA_DB_KEYSPACE = None"
    ]
   },
   {
@@ -77,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3cb739c0",
    "metadata": {},
    "outputs": [],
@@ -93,28 +112,46 @@
    "source": [
     "## Initialization\n",
     "\n",
-    "There are two ways to create an Astra DB vector store, which differ in how the embeddings are computed.\n",
+    "There are various ways to create an Astra DB vector store:\n",
     "\n",
     "#### Method 1: Explicit embeddings\n",
     "\n",
     "You can separately instantiate a `langchain_core.embeddings.Embeddings` class and pass it to the `AstraDBVectorStore` constructor, just like with most other LangChain vector stores.\n",
     "\n",
-    "#### Method 2: Integrated embedding computation\n",
+    "#### Method 2: Server-side embeddings ('vectorize')\n",
     "\n",
-    "Alternatively, you can use the [Vectorize](https://www.datastax.com/blog/simplifying-vector-embedding-generation-with-astra-vectorize) feature of Astra DB and simply specify the name of a supported embedding model when creating the store. The embedding computations are entirely handled within the database. (To proceed with this method, you must have enabled the desired embedding integration for your database, as described [in the docs](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).)\n",
+    "Alternatively, you can use the [server-side embedding computation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html) feature of Astra DB ('vectorize') and simply specify an embedding model when creating the server infrastructure for the store. The embedding computations will then be entirely handled within the database in subsequent read and write operations. (To proceed with this method, you must have enabled the desired embedding integration for your database, as described [in the docs](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).)\n",
     "\n",
-    "### Explicit Embedding Initialization\n",
+    "#### Method 3: Auto-detect from a pre-existing collection\n",
     "\n",
-    "Below, we instantiate our vector store using the explicit embedding class:\n",
+    "You may already have a [collection](https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html) in your Astra DB, possibly pre-populated with data through other means (e.g. via the Astra UI or a third-party application), and just want to start querying it within LangChain. In this case, the right approach is to enable the `autodetect_collection` mode in the vector store constructor and let the class figure out the details. (Of course, if your collection has no 'vectorize', you still need to provide an `Embeddings` object).\n",
+    "\n",
+    "#### A note on \"hybrid search\"\n",
+    "\n",
+    "Astra DB vector stores support metadata search in vector searches; furthermore, version 0.6 introduced full support for _hybrid search_ through the [findAndRerank](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-and-rerank.html) database primitive: documents are retrieved from both a vector-similarity _and_ a keyword-based (\"lexical\") search, and are then merged through a reranker model. This search strategy, entirely handled on server-side, can boost the accuracy of your results, thus improving the quality of your RAG application. Whenever available, hybrid search is used automatically by the vector store (though you can exert manual control over it if you wish to do so).\n",
+    "\n",
+    "#### Additional information\n",
+    "\n",
+    "The `AstraDBVectorStore` can be configured in many ways; see the [API Reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html) for a full guide covering e.g. asynchronous initialization; non-Astra-DB databases; custom indexing allow-/deny-lists; manual hybrid-search control; and much more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d7e33e0-f948-47b5-a9c2-6407fdde170e",
+   "metadata": {},
+   "source": [
+    "### Explicit embedding initialization (method 1)\n",
+    "\n",
+    "Instantiate our vector store using an explicit embedding class:\n",
     "\n",
     "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
     "\n",
-    "<EmbeddingTabs/>\n"
+    "<EmbeddingTabs/>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "id": "d71a1dcb",
    "metadata": {},
    "outputs": [],
@@ -128,19 +165,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "id": "0b32730d-176e-414c-9d91-fd3644c54211",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_astradb import AstraDBVectorStore\n",
     "\n",
-    "vector_store = AstraDBVectorStore(\n",
+    "vector_store_explicit_embeddings = AstraDBVectorStore(\n",
     "    collection_name=\"astra_vector_langchain\",\n",
     "    embedding=embeddings,\n",
     "    api_endpoint=ASTRA_DB_API_ENDPOINT,\n",
     "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
-    "    namespace=ASTRA_DB_NAMESPACE,\n",
+    "    namespace=ASTRA_DB_KEYSPACE,\n",
     ")"
    ]
   },
@@ -149,26 +186,26 @@
    "id": "84a1fe85-a42c-4f15-92e1-f79f1dd43ea2",
    "metadata": {},
    "source": [
-    "### Integrated Embedding Initialization\n",
+    "### Server-side embedding initialization (\"vectorize\", method 2)\n",
     "\n",
-    "Here it is assumed that you have\n",
+    "In this example code, it is assumed that you have\n",
     "\n",
     "- Enabled the OpenAI integration in your Astra DB organization,\n",
     "- Added an API Key named `\"OPENAI_API_KEY\"` to the integration, and scoped it to the database you are using.\n",
     "\n",
-    "For more details on how to do this, please consult the [documentation](https://docs.datastax.com/en/astra-db-serverless/integrations/embedding-providers/openai.html)."
+    "For more details, including instructions to switch provider/model, please consult the [documentation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "9d18455d-3fa6-4f9e-b687-3a2bc71c9a23",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from astrapy.info import CollectionVectorServiceOptions\n",
+    "from astrapy.info import VectorServiceOptions\n",
     "\n",
-    "openai_vectorize_options = CollectionVectorServiceOptions(\n",
+    "openai_vectorize_options = VectorServiceOptions(\n",
     "    provider=\"openai\",\n",
     "    model_name=\"text-embedding-3-small\",\n",
     "    authentication={\n",
@@ -176,125 +213,183 @@
     "    },\n",
     ")\n",
     "\n",
-    "vector_store_integrated = AstraDBVectorStore(\n",
-    "    collection_name=\"astra_vector_langchain_integrated\",\n",
+    "vector_store_integrated_embeddings = AstraDBVectorStore(\n",
+    "    collection_name=\"astra_vectorize_langchain\",\n",
     "    api_endpoint=ASTRA_DB_API_ENDPOINT,\n",
     "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
-    "    namespace=ASTRA_DB_NAMESPACE,\n",
+    "    namespace=ASTRA_DB_KEYSPACE,\n",
     "    collection_vector_service_options=openai_vectorize_options,\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "24508a60-9591-4b24-a9b7-ecc90ed71b68",
+   "metadata": {},
+   "source": [
+    "### Auto-detect initialization (method 3)\n",
+    "\n",
+    "You can use this pattern if the collection already exists on the database and your `AstraDBVectorStore` needs to use it (for reads and writes). The LangChain component will inspect the collection and figure out the details.\n",
+    "\n",
+    "This is the recommended approach if the collection has been created and -- most importantly -- populated by tools other than LangChain, for example if the data has been ingested through the Astra DB Web interface.\n",
+    "\n",
+    "Auto-detect mode cannot coexist with _collection_ settings (such as the similarity metric and such); on the other hand, if no server-side embeddings are employed, one still needs to pass an `Embeddings` object to the constructor.\n",
+    "\n",
+    "In the following example code, we will \"auto-detect\" the very same collection that was created by method 2 above (\"vectorize\"). Hence, no `Embeddings` object needs to be supplied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "683b0f6e-884f-4a09-bc3a-454bb1eefd30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_store_autodetected = AstraDBVectorStore(\n",
+    "    collection_name=\"astra_vectorize_langchain\",\n",
+    "    api_endpoint=ASTRA_DB_API_ENDPOINT,\n",
+    "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
+    "    namespace=ASTRA_DB_KEYSPACE,\n",
+    "    autodetect_collection=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbcfe8e8-2f4e-4fc7-a332-7a2fa2c401bf",
+   "metadata": {},
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "Once you have created your vector store, interact with it by adding and deleting different items.\n",
+    "\n",
+    "All interactions with the vector store proceed regardless of the initialization method: please **adapt the following cell**, if you desire, to select a vector store you have created and want to put to test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "54d63f59-1e6b-49b4-a7c1-ac7717c92ac0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If desired, uncomment a different line here:\n",
+    "\n",
+    "# vector_store = vector_store_explicit_embeddings\n",
+    "vector_store = vector_store_integrated_embeddings\n",
+    "# vector_store = vector_store_autodetected"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d3796b39",
    "metadata": {},
    "source": [
-    "## Manage vector store\n",
-    "\n",
-    "Once you have created your vector store, we can interact with it by adding and deleting different items.\n",
-    "\n",
     "### Add items to vector store\n",
     "\n",
-    "We can add items to our vector store by using the `add_documents` function."
+    "Add documents to the vector store by using the `add_documents` method.\n",
+    "\n",
+    "_The \"id\" field can be supplied separately, in a matching `ids=[...]` parameter to `add_documents`, or even left out entirely to let the store generate IDs._"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "id": "afb3e155",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[UUID('89a5cea1-5f3d-47c1-89dc-7e36e12cf4de'),\n",
-       " UUID('d4e78c48-f954-4612-8a38-af22923ba23b'),\n",
-       " UUID('058e4046-ded0-4fc1-b8ac-60e5a5f08ea0'),\n",
-       " UUID('50ab2a9a-762c-4b78-b102-942a86d77288'),\n",
-       " UUID('1da5a3c1-ba51-4f2f-aaaf-79a8f5011ce3'),\n",
-       " UUID('f3055d9e-2eb1-4d25-838e-2c70548f91b5'),\n",
-       " UUID('4bf0613d-08d0-4fbc-a43c-4955e4c9e616'),\n",
-       " UUID('18008625-8fd4-45c2-a0d7-92a2cde23dbc'),\n",
-       " UUID('c712e06f-790b-4fd4-9040-7ab3898965d0'),\n",
-       " UUID('a9b84820-3445-4810-a46c-e77b76ab85bc')]"
+       "['entry_00',\n",
+       " 'entry_01',\n",
+       " 'entry_02',\n",
+       " 'entry_03',\n",
+       " 'entry_04',\n",
+       " 'entry_05',\n",
+       " 'entry_06',\n",
+       " 'entry_07',\n",
+       " 'entry_08',\n",
+       " 'entry_09',\n",
+       " 'entry_10']"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from uuid import uuid4\n",
-    "\n",
     "from langchain_core.documents import Document\n",
     "\n",
-    "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_2 = Document(\n",
-    "    page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
-    "    metadata={\"source\": \"news\"},\n",
-    ")\n",
-    "\n",
-    "document_3 = Document(\n",
-    "    page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_4 = Document(\n",
-    "    page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
-    "    metadata={\"source\": \"news\"},\n",
-    ")\n",
-    "\n",
-    "document_5 = Document(\n",
-    "    page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_6 = Document(\n",
-    "    page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
-    "    metadata={\"source\": \"website\"},\n",
-    ")\n",
-    "\n",
-    "document_7 = Document(\n",
-    "    page_content=\"The top 10 soccer players in the world right now.\",\n",
-    "    metadata={\"source\": \"website\"},\n",
-    ")\n",
-    "\n",
-    "document_8 = Document(\n",
-    "    page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_9 = Document(\n",
-    "    page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
-    "    metadata={\"source\": \"news\"},\n",
-    ")\n",
-    "\n",
-    "document_10 = Document(\n",
-    "    page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "documents = [\n",
-    "    document_1,\n",
-    "    document_2,\n",
-    "    document_3,\n",
-    "    document_4,\n",
-    "    document_5,\n",
-    "    document_6,\n",
-    "    document_7,\n",
-    "    document_8,\n",
-    "    document_9,\n",
-    "    document_10,\n",
+    "documents_to_insert = [\n",
+    "    Document(\n",
+    "        page_content=\"ZYX, just another tool in the world, is actually my agent-based superhero\",\n",
+    "        metadata={\"source\": \"tweet\"},\n",
+    "        id=\"entry_00\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"I had chocolate chip pancakes and scrambled eggs \"\n",
+    "        \"for breakfast this morning.\",\n",
+    "        metadata={\"source\": \"tweet\"},\n",
+    "        id=\"entry_01\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"The weather forecast for tomorrow is cloudy and \"\n",
+    "        \"overcast, with a high of 62 degrees.\",\n",
+    "        metadata={\"source\": \"news\"},\n",
+    "        id=\"entry_02\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Building an exciting new project with LangChain \"\n",
+    "        \"- come check it out!\",\n",
+    "        metadata={\"source\": \"tweet\"},\n",
+    "        id=\"entry_03\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Robbers broke into the city bank and stole \"\n",
+    "        \"$1 million in cash.\",\n",
+    "        metadata={\"source\": \"news\"},\n",
+    "        id=\"entry_04\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Thanks to her sophisticated language skills, the agent \"\n",
+    "        \"managed to extract strategic information all right.\",\n",
+    "        metadata={\"source\": \"tweet\"},\n",
+    "        id=\"entry_05\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Is the new iPhone worth the price? Read this \"\n",
+    "        \"review to find out.\",\n",
+    "        metadata={\"source\": \"website\"},\n",
+    "        id=\"entry_06\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"The top 10 soccer players in the world right now.\",\n",
+    "        metadata={\"source\": \"website\"},\n",
+    "        id=\"entry_07\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"LangGraph is the best framework for building stateful, \"\n",
+    "        \"agentic applications!\",\n",
+    "        metadata={\"source\": \"tweet\"},\n",
+    "        id=\"entry_08\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"The stock market is down 500 points today due to \"\n",
+    "        \"fears of a recession.\",\n",
+    "        metadata={\"source\": \"news\"},\n",
+    "        id=\"entry_09\",\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
+    "        metadata={\"source\": \"tweet\"},\n",
+    "        id=\"entry_10\",\n",
+    "    ),\n",
     "]\n",
-    "uuids = [str(uuid4()) for _ in range(len(documents))]\n",
     "\n",
-    "vector_store.add_documents(documents=documents, ids=uuids)"
+    "\n",
+    "vector_store.add_documents(documents=documents_to_insert)"
    ]
   },
   {
@@ -304,12 +399,12 @@
    "source": [
     "### Delete items from vector store\n",
     "\n",
-    "We can delete items from our vector store by ID by using the `delete` function."
+    "Delete items by ID by using the `delete` function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "id": "d3f69315",
    "metadata": {},
    "outputs": [
@@ -319,13 +414,13 @@
        "True"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "vector_store.delete(ids=uuids[-1])"
+    "vector_store.delete(ids=[\"entry_10\", \"entry_02\"])"
    ]
   },
   {
@@ -333,20 +428,20 @@
    "id": "d12e1a07",
    "metadata": {},
    "source": [
-    "## Query vector store\n",
+    "## Query the 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",
+    "Once the vector store is created and populated, you can query it (e.g. as part of your chain or agent).\n",
     "\n",
     "### Query directly\n",
     "\n",
     "#### Similarity search\n",
     "\n",
-    "Performing a simple similarity search with filtering on metadata can be done as follows:"
+    "Search for documents similar to a provided text, with additional metadata filters if desired:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "770b3467",
    "metadata": {},
    "outputs": [
@@ -354,19 +449,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
-      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
+      "* \"Building an exciting new project with LangChain - come check it out!\", metadata={'source': 'tweet'}\n",
+      "* \"LangGraph is the best framework for building stateful, agentic applications!\", metadata={'source': 'tweet'}\n",
+      "* \"Thanks to her sophisticated language skills, the agent managed to extract strategic information all right.\", metadata={'source': 'tweet'}\n"
      ]
     }
    ],
    "source": [
     "results = vector_store.similarity_search(\n",
     "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
-    "    k=2,\n",
+    "    k=3,\n",
     "    filter={\"source\": \"tweet\"},\n",
     ")\n",
     "for res in results:\n",
-    "    print(f\"* {res.page_content} [{res.metadata}]\")"
+    "    print(f'* \"{res.page_content}\", metadata={res.metadata}')"
    ]
   },
   {
@@ -376,12 +472,12 @@
    "source": [
     "#### Similarity search with score\n",
     "\n",
-    "You can also search with score:"
+    "You can return the similarity score as well:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "5924309a",
    "metadata": {},
    "outputs": [
@@ -389,16 +485,69 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* [SIM=0.776585] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
+      "* [SIM=0.71] \"Building an exciting new project with LangChain - come check it out!\", metadata={'source': 'tweet'}\n",
+      "* [SIM=0.70] \"LangGraph is the best framework for building stateful, agentic applications!\", metadata={'source': 'tweet'}\n",
+      "* [SIM=0.61] \"Thanks to her sophisticated language skills, the agent managed to extract strategic information all right.\", metadata={'source': 'tweet'}\n"
      ]
     }
    ],
    "source": [
     "results = vector_store.similarity_search_with_score(\n",
-    "    \"Will it be hot tomorrow?\", k=1, filter={\"source\": \"news\"}\n",
+    "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
+    "    k=3,\n",
+    "    filter={\"source\": \"tweet\"},\n",
     ")\n",
     "for res, score in results:\n",
-    "    print(f\"* [SIM={score:3f}] {res.page_content} [{res.metadata}]\")"
+    "    print(f'* [SIM={score:.2f}] \"{res.page_content}\", metadata={res.metadata}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73b8f418-91a7-46d0-91c3-3c76e9586193",
+   "metadata": {},
+   "source": [
+    "#### Specify a different keyword query (requires hybrid search)\n",
+    "\n",
+    "> Note: this cell can be run only if the collection supports the [find-and-rerank](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-and-rerank.html) command and if the vector store is aware of this fact.\n",
+    "\n",
+    "If the vector store is using a hybrid-enabled collection and has detected this fact, by default it will use that capability when running searches.\n",
+    "\n",
+    "In that case, the same query text is used for both the vector-similarity and the lexical-based retrieval steps in the find-and-rerank process, _unless you explicitly provide a different query for the latter_:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e282a48b-081a-4d94-9483-33407e8d6da7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* \"Building an exciting new project with LangChain - come check it out!\", metadata={'source': 'tweet'}\n",
+      "* \"LangGraph is the best framework for building stateful, agentic applications!\", metadata={'source': 'tweet'}\n",
+      "* \"ZYX, just another tool in the world, is actually my agent-based superhero\", metadata={'source': 'tweet'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store_autodetected.similarity_search(\n",
+    "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
+    "    k=3,\n",
+    "    filter={\"source\": \"tweet\"},\n",
+    "    lexical_query=\"agent\",\n",
+    ")\n",
+    "for res in results:\n",
+    "    print(f'* \"{res.page_content}\", metadata={res.metadata}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60688e8c-d74d-4921-b213-b48d88600f95",
+   "metadata": {},
+   "source": [
+    "_The above example hardcodes the \"autodetected\" vector store, which has surely inspected the collection and figured out if hybrid is available. Another option is to explicitly supply hybrid-search parameters to the constructor (refer to the API Reference for more details/examples)._"
    ]
   },
   {
@@ -408,7 +557,9 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search and search by vector.\n",
+    "\n",
+    "For a full list of the search modes available in `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
    ]
   },
   {
@@ -418,24 +569,24 @@
    "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",
+    "You can also make the vector store into a retriever, for easier usage in your chains. \n",
     "\n",
-    "Here is how to transform your vector store into a retriever and then invoke the retreiever with a simple query and filter."
+    "Transform the vector store into a retriever and invoke it with a simple query + metadata filter:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "id": "dcee50e6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
+       "[Document(id='entry_04', metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "fd405a13-6f71-46fa-87e6-167238e9c25e",
    "metadata": {},
    "outputs": [],
@@ -505,7 +656,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AstraDBVectorStore` features and configurations head to the API reference: https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html"
+    "For detailed documentation of all `AstraDBVectorStore` features and configurations, consult the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
    ]
   }
  ],
@@ -525,7 +676,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/chroma.ipynb

@@ -402,7 +402,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* I had chocalate chip pancakes and fried eggs for breakfast this morning. [{'source': 'tweet'}]\n"
+      "* I had chocolate chip pancakes and fried eggs for breakfast this morning. [{'source': 'tweet'}]\n"
      ]
     }
    ],

docs/docs/integrations/vectorstores/clickhouse.ipynb

@@ -144,7 +144,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",

docs/docs/integrations/vectorstores/couchbase.ipynb

@@ -301,7 +301,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",
@@ -492,7 +492,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "page_content='I had chocalate chip pancakes and scrambled eggs for breakfast this morning.' metadata={'source': 'tweet'}\n"
+      "page_content='I had chocolate chip pancakes and scrambled eggs for breakfast this morning.' metadata={'source': 'tweet'}\n"
      ]
     }
    ],

docs/docs/integrations/vectorstores/elasticsearch.ipynb

@@ -255,7 +255,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",

docs/docs/integrations/vectorstores/faiss.ipynb

@@ -150,7 +150,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",

docs/docs/integrations/vectorstores/kinetica.ipynb

@@ -138,7 +138,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -206,7 +206,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -26,7 +26,7 @@
    },
    "outputs": [],
    "source": [
-    "pip install -qU langchain-pinecone pinecone-notebooks"
+    "pip install -qU langchain langchain-pinecone langchain-openai"
    ]
   },
   {
@@ -34,7 +34,7 @@
    "id": "1917d123",
    "metadata": {},
    "source": [
-    "Migration note: if you are migrating from the `langchain_community.vectorstores` implementation of Pinecone, you may need to remove your `pinecone-client` v2 dependency before installing `langchain-pinecone`, which relies on `pinecone-client` v3."
+    "Migration note: if you are migrating from the `langchain_community.vectorstores` implementation of Pinecone, you may need to remove your `pinecone-client` v2 dependency before installing `langchain-pinecone`, which relies on `pinecone-client` v6."
    ]
   },
   {
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "eb554814",
    "metadata": {},
    "outputs": [],
@@ -57,7 +57,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "from pinecone import Pinecone, ServerlessSpec\n",
+    "from pinecone import Pinecone\n",
     "\n",
     "if not os.getenv(\"PINECONE_API_KEY\"):\n",
     "    os.environ[\"PINECONE_API_KEY\"] = getpass.getpass(\"Enter your Pinecone API key: \")\n",
@@ -98,59 +98,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "276a06dd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import time\n",
+    "from pinecone import ServerlessSpec\n",
     "\n",
     "index_name = \"langchain-test-index\"  # change if desired\n",
     "\n",
-    "existing_indexes = [index_info[\"name\"] for index_info in pc.list_indexes()]\n",
-    "\n",
-    "if index_name not in existing_indexes:\n",
+    "if not pc.has_index(index_name):\n",
     "    pc.create_index(\n",
     "        name=index_name,\n",
-    "        dimension=3072,\n",
+    "        dimension=1536,\n",
     "        metric=\"cosine\",\n",
     "        spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n",
     "    )\n",
-    "    while not pc.describe_index(index_name).status[\"ready\"]:\n",
-    "        time.sleep(1)\n",
     "\n",
     "index = pc.Index(index_name)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a4d377f",
-   "metadata": {},
-   "source": [
-    "Now that our Pinecone index is setup, we can initialize our vector store. \n",
-    "\n",
-    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
-    "\n",
-    "<EmbeddingTabs/>\n"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "1485db56",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
-    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
+    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "id": "6e104aee",
    "metadata": {},
    "outputs": [],
@@ -176,37 +158,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "70e688f4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['167b8681-5974-467f-adcb-6e987a18df01',\n",
-       " 'd16010fd-41f8-4d49-9c22-c66d5555a3fe',\n",
-       " 'ffcacfb3-2bc2-44c3-a039-c2256a905c0e',\n",
-       " 'cf3bfc9f-5dc7-4f5e-bb41-edb957394126',\n",
-       " 'e99b07eb-fdff-4cb9-baa8-619fd8efeed3',\n",
-       " '68c93033-a24f-40bd-8492-92fa26b631a4',\n",
-       " 'b27a4ecb-b505-4c5d-89ff-526e3d103558',\n",
-       " '4868a9e6-e6fb-4079-b400-4a1dfbf0d4c4',\n",
-       " '921c0e9c-0550-4eb5-9a6c-ed44410788b2',\n",
-       " 'c446fc23-64e8-47e7-8c19-ecf985e9411e']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from uuid import uuid4\n",
     "\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",
@@ -268,7 +230,6 @@
     "    document_10,\n",
     "]\n",
     "uuids = [str(uuid4()) for _ in range(len(documents))]\n",
-    "\n",
     "vector_store.add_documents(documents=documents, ids=uuids)"
    ]
   },
@@ -282,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "id": "5b8437cd",
    "metadata": {},
    "outputs": [],
@@ -306,19 +267,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "id": "ffbcb3fb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
-      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "results = vector_store.similarity_search(\n",
     "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
@@ -341,18 +293,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "5fb24583",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "* [SIM=0.553187] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "results = vector_store.similarity_search_with_score(\n",
     "    \"Will it be hot tomorrow?\", k=1, filter={\"source\": \"news\"}\n",
@@ -377,25 +321,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "78140e87",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "retriever = vector_store.as_retriever(\n",
     "    search_type=\"similarity_score_threshold\",\n",
-    "    search_kwargs={\"k\": 1, \"score_threshold\": 0.5},\n",
+    "    search_kwargs={\"k\": 1, \"score_threshold\": 0.4},\n",
     ")\n",
     "retriever.invoke(\"Stealing from the bank is a crime\", filter={\"source\": \"news\"})"
    ]
@@ -421,13 +354,13 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html"
+    "For detailed documentation of all features and configurations head to the API reference: https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -441,7 +374,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/pinecone_sparse.ipynb

@@ -0,0 +1,576 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pinecone (sparse)\n",
+    "\n",
+    ">[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `Pinecone` vector database.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To use the `PineconeSparseVectorStore` you first need to install the partner package, as well as the other packages used throughout this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5g1-ZqcEONGD",
+    "outputId": "2d49c259-683b-46c2-994f-642f35e30357"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: pinecone 6.0.2 does not provide the extra 'async'\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU \"langchain-pinecone==0.2.5\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-1vfBVLhONGE"
+   },
+   "source": [
+    "### Credentials\n",
+    "Create a new Pinecone account, or sign into your existing one, and create an API key to use in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k_Dp_DIlONGF",
+    "outputId": "01728754-8708-4f05-e53d-2e251541370e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter your Pinecone API key: ··········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from pinecone import Pinecone\n",
+    "\n",
+    "# get API key at app.pinecone.io\n",
+    "os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n",
+    "    \"Enter your Pinecone API key: \"\n",
+    ")\n",
+    "\n",
+    "# initialize client\n",
+    "pc = Pinecone()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OFqeT2xHONGF"
+   },
+   "source": [
+    "## Initialization\n",
+    "Before initializing our vector store, let's connect to a Pinecone index. If one named index_name doesn't exist, it will be created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9xNxmZsRONGF",
+    "outputId": "b661d0af-26bd-43b2-f277-5366efd1d865"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index `langchain-sparse-vector-search` host: https://langchain-sparse-vector-search-yrrgefy.svc.aped-4627-b74a.pinecone.io\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pinecone import AwsRegion, CloudProvider, Metric, ServerlessSpec\n",
+    "\n",
+    "index_name = \"langchain-sparse-vector-search\"  # change if desired\n",
+    "model_name = \"pinecone-sparse-english-v0\"\n",
+    "\n",
+    "if not pc.has_index(index_name):\n",
+    "    pc.create_index_for_model(\n",
+    "        name=index_name,\n",
+    "        cloud=CloudProvider.AWS,\n",
+    "        region=AwsRegion.US_EAST_1,\n",
+    "        embed={\n",
+    "            \"model\": model_name,\n",
+    "            \"field_map\": {\"text\": \"chunk_text\"},\n",
+    "            \"metric\": Metric.DOTPRODUCT,\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "index = pc.Index(index_name)\n",
+    "print(f\"Index `{index_name}` host: {index.config.host}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E0fDNTF9ONGF"
+   },
+   "source": [
+    "For our sparse embedding model we use [`pinecone-sparse-english-v0`](https://docs.pinecone.io/models/pinecone-sparse-english-v0), we initialize it like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "swM_SFhOONGF"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_pinecone.embeddings import PineconeSparseEmbeddings\n",
+    "\n",
+    "sparse_embeddings = PineconeSparseEmbeddings(model=model_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZDL58ZRZONGF"
+   },
+   "source": [
+    "Now that our Pinecone index and embedding model are both ready, we can initialize our sparse vector store in LangChain:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "GrBUli1VONGF"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_pinecone import PineconeSparseVectorStore\n",
+    "\n",
+    "vector_store = PineconeSparseVectorStore(index=index, embedding=sparse_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Bp8aeN4SONGG"
+   },
+   "source": [
+    "## Manage vector store\n",
+    "Once you have created your vector store, we can interact with it by adding and deleting different items.\n",
+    "\n",
+    "### Add items to vector store\n",
+    "We can add items to our vector store by using the `add_documents` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ef0s0ovdONGG",
+    "outputId": "bd61d82f-902d-4010-9701-b0e2ecb02d17"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['95b598af-c3dc-4a8a-bdb7-5d21283e5a86',\n",
+       " '838614a5-5635-4efd-9ac3-5237a37a542b',\n",
+       " '093fd11f-c85b-4c83-83f0-117df64ff442',\n",
+       " 'fb3ba32f-f802-410a-ad79-56f7bce938fe',\n",
+       " '75cde9bf-7e91-4f06-8bae-c824dab16a08',\n",
+       " '9de8f769-d604-4e56-b677-ee333cbc8e34',\n",
+       " 'f5f4ae97-88e6-4669-bcf7-87072bb08550',\n",
+       " 'f9f82811-187c-4b25-85b5-7a42b4da3bff',\n",
+       " 'ce45957c-e8fc-41ef-819b-1bd52b6fc815',\n",
+       " '66cacc6f-b8e2-441b-9f7f-468788aad88f']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from uuid import uuid4\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "documents = [\n",
+    "    Document(\n",
+    "        page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "        metadata={\"source\": \"social\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
+    "        metadata={\"source\": \"news\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
+    "        metadata={\"source\": \"social\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
+    "        metadata={\"source\": \"news\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
+    "        metadata={\"source\": \"social\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
+    "        metadata={\"source\": \"website\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"The top 10 soccer players in the world right now.\",\n",
+    "        metadata={\"source\": \"website\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
+    "        metadata={\"source\": \"social\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
+    "        metadata={\"source\": \"news\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
+    "        metadata={\"source\": \"social\"},\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "uuids = [str(uuid4()) for _ in range(len(documents))]\n",
+    "\n",
+    "vector_store.add_documents(documents=documents, ids=uuids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KUIIEuYxONGG"
+   },
+   "source": [
+    "### Delete items from vector store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLFI8xEJONGG"
+   },
+   "source": [
+    "We can delete records from our vector store using the `delete` method, providing it with a list of document IDs to delete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "eboEnsbRONGG"
+   },
+   "outputs": [],
+   "source": [
+    "vector_store.delete(ids=[uuids[-1]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ggaQ5g4bONGG"
+   },
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "Once we have loaded our documents into the vector store we're most likely ready to begin querying. There are various method for doing this in LangChain.\n",
+    "\n",
+    "First, we'll see how to perform a simple vector search by querying our `vector_store` directly via the `similarity_search` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QcIS_P8CONGG",
+    "outputId": "774da46e-b919-4128-bc77-6c392e77f9f3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
+      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store.similarity_search(\"I'm building a new LangChain project!\", k=3)\n",
+    "\n",
+    "for res in results:\n",
+    "    print(f\"* {res.page_content} [{res.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "n-JlbidpONGG"
+   },
+   "source": [
+    "We can also add [metadata filtering](https://docs.pinecone.io/guides/data/understanding-metadata#metadata-query-language) to our query to limit our search based on various criteria. Let's try a simple filter to limit our search to include only records with `source==\"social\"`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "N19HBLNCONGG",
+    "outputId": "9c5e96c2-0b4e-4083-cd6a-dd6d8662df09"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
+      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store.similarity_search(\n",
+    "    \"I'm building a new LangChain project!\",\n",
+    "    k=3,\n",
+    "    filter={\"source\": \"social\"},\n",
+    ")\n",
+    "for res in results:\n",
+    "    print(f\"* {res.page_content} [{res.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yEs4xmESONGG"
+   },
+   "source": [
+    "When comparing these results, we can see that our first query returned a different record from the `\"website\"` source. In our latter, filtered, query — this is no longer the case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vWGkIfzCONGH"
+   },
+   "source": [
+    "### Similarity Search and Scores\n",
+    "\n",
+    "We can also search while returning the similarity score in a list of `(document, score)` tuples. Where the `document` is a LangChain `Document` object containing our text content and metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aRiHj8ADONGH",
+    "outputId": "5649d70a-0bd0-446d-8e60-2348170da706"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[SIM=12.959961] Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
+      "[SIM=12.959961] Building an exciting new project with LangChain - come check it out! [{'source': 'social'}]\n",
+      "[SIM=1.942383] LangGraph is the best framework for building stateful, agentic applications! [{'source': 'social'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\n",
+    "    \"I'm building a new LangChain project!\", k=3, filter={\"source\": \"social\"}\n",
+    ")\n",
+    "for doc, score in results:\n",
+    "    print(f\"[SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aqGelo6sONGH"
+   },
+   "source": [
+    "### As a Retriever\n",
+    "\n",
+    "In our chains and agents we'll often use the vector store as a `VectorStoreRetriever`. To create that, we use the `as_retriever` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Tsn9--KsONGH",
+    "outputId": "3da43258-49fb-4080-cbdd-f97101fb8099"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VectorStoreRetriever(tags=['PineconeSparseVectorStore', 'PineconeSparseEmbeddings'], vectorstore=<langchain_pinecone.vectorstores_sparse.PineconeSparseVectorStore object at 0x7c8087b24290>, search_type='similarity_score_threshold', search_kwargs={'k': 3, 'score_threshold': 0.5})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever = vector_store.as_retriever(\n",
+    "    search_type=\"similarity_score_threshold\",\n",
+    "    search_kwargs={\"k\": 3, \"score_threshold\": 0.5},\n",
+    ")\n",
+    "retriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yDqb3q-VUY2t"
+   },
+   "source": [
+    "We can now query our retriever using the `invoke` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mGV0p0TDUJyx",
+    "outputId": "89db72c3-4ff2-4900-d302-482e54549b39"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.11/dist-packages/langchain_core/vectorstores/base.py:1082: UserWarning: Relevance scores must be between 0 and 1, got [(Document(id='093fd11f-c85b-4c83-83f0-117df64ff442', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), 6.97998045), (Document(id='54f8f645-9f77-4aab-b9fa-709fd91ae3b3', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'), 6.97998045), (Document(id='f9f82811-187c-4b25-85b5-7a42b4da3bff', metadata={'source': 'social'}, page_content='LangGraph is the best framework for building stateful, agentic applications!'), 1.471191405)]\n",
+      "  self.vectorstore.similarity_search_with_relevance_scores(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='093fd11f-c85b-4c83-83f0-117df64ff442', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'),\n",
+       " Document(id='54f8f645-9f77-4aab-b9fa-709fd91ae3b3', metadata={'source': 'social'}, page_content='Building an exciting new project with LangChain - come check it out!'),\n",
+       " Document(id='f9f82811-187c-4b25-85b5-7a42b4da3bff', metadata={'source': 'social'}, page_content='LangGraph is the best framework for building stateful, agentic applications!')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever.invoke(\n",
+    "    input=\"I'm building a new LangChain project!\", filter={\"source\": \"social\"}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all features and configurations head to the API reference: \n",
+    "https://python.langchain.com/api_reference/pinecone/vectorstores_sparse/langchain_pinecone.vectorstores_sparse.PineconeSparseVectorStore.html#langchain_pinecone.vectorstores_sparse.PineconeSparseVectorStore\n",
+    "\n",
+    "Sparse Embeddings:\n",
+    "https://python.langchain.com/api_reference/pinecone/embeddings/langchain_pinecone.embeddings.PineconeSparseEmbeddings.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

docs/docs/integrations/vectorstores/sap_hanavector.ipynb

@@ -6,73 +6,46 @@
    "source": [
     "# SAP HANA Cloud Vector Engine\n",
     "\n",
-    ">[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.\n",
-    "\n",
-    "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration"
+    ">[SAP HANA Cloud Vector Engine](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-vector-engine-guide/sap-hana-cloud-sap-hana-database-vector-engine-guide) is a vector store fully integrated into the `SAP HANA Cloud` database."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setting up\n",
+    "## Setup\n",
     "\n",
-    "Installation of the HANA database driver."
+    "Install the `langchain-hana` external integration package, as well as the other packages used throughout this notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Pip install necessary package\n",
-    "%pip install --upgrade --quiet  hdbcli"
+    "%pip install -qU langchain-hana"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For `OpenAIEmbeddings` we use the OpenAI API key from the environment."
+    "### Credentials\n",
+    "\n",
+    "Ensure your SAP HANA instance is running. Load your credentials from environment variables and create a connection:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-09-09T08:02:16.802456Z",
-     "start_time": "2023-09-09T08:02:07.065604Z"
-    }
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "# Use OPENAI_API_KEY env variable\n",
-    "# os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a database connection to a HANA Cloud instance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-09-09T08:02:28.174088Z",
-     "start_time": "2023-09-09T08:02:28.162698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
+    "\n",
     "from dotenv import load_dotenv\n",
     "from hdbcli import dbapi\n",
     "\n",
@@ -88,6 +61,92 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn more about SAP HANA in [What is SAP HANA?](https://www.sap.com/products/data-cloud/hana/what-is-sap-hana.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialization\n",
+    "To initialize a `HanaDB` vector store, you need a database connection and an embedding instance. SAP HANA Cloud Vector Engine supports both external and internal embeddings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- #### Using External Embeddings\n",
+    "\n",
+    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
+    "\n",
+    "<EmbeddingTabs/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- #### Using Internal Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can compute embeddings directly in SAP HANA using its native `VECTOR_EMBEDDING()` function. To enable this, create an instance of `HanaInternalEmbeddings` with your internal model ID and pass it to `HanaDB`. Note that the `HanaInternalEmbeddings` instance is specifically designed for use with `HanaDB` and is not intended for use with other vector store implementations. For more information about internal embedding, see the [SAP HANA VECTOR_EMBEDDING Function](https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-vector-engine-guide/vector-embedding-function-vector).\n",
+    "\n",
+    "> **Caution:** Ensure NLP is enabled in your SAP HANA Cloud instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_hana import HanaInternalEmbeddings\n",
+    "\n",
+    "embeddings = HanaInternalEmbeddings(internal_embedding_model_id=\"SAP_NEB.20240715\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have your connection and embedding instance, create the vector store by passing them to `HanaDB` along with a table name for storing vectors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_hana import HanaDB\n",
+    "\n",
+    "db = HanaDB(\n",
+    "    embedding=embeddings, connection=connection, table_name=\"STATE_OF_THE_UNION\"\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -104,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-09-09T08:02:25.452472Z",
@@ -122,40 +181,16 @@
    ],
    "source": [
     "from langchain_community.document_loaders import TextLoader\n",
-    "from langchain_community.vectorstores.hanavector import HanaDB\n",
     "from langchain_core.documents import Document\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import CharacterTextSplitter\n",
     "\n",
-    "text_documents = TextLoader(\"../../how_to/state_of_the_union.txt\").load()\n",
+    "text_documents = TextLoader(\n",
+    "    \"../../how_to/state_of_the_union.txt\", encoding=\"UTF-8\"\n",
+    ").load()\n",
     "text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
     "text_chunks = text_splitter.split_documents(text_documents)\n",
-    "print(f\"Number of document chunks: {len(text_chunks)}\")\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use for accessing the vector embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-09-09T08:04:16.696625Z",
-     "start_time": "2023-09-09T08:02:31.817790Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "db = HanaDB(\n",
-    "    embedding=embeddings, connection=connection, table_name=\"STATE_OF_THE_UNION\"\n",
-    ")"
+    "print(f\"Number of document chunks: {len(text_chunks)}\")"
    ]
   },
   {
@@ -167,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -176,7 +211,7 @@
        "[]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -199,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -254,7 +289,7 @@
     }
    ],
    "source": [
-    "from langchain_community.vectorstores.utils import DistanceStrategy\n",
+    "from langchain_hana.utils import DistanceStrategy\n",
     "\n",
     "db = HanaDB(\n",
     "    embedding=embeddings,\n",
@@ -286,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-09-09T08:05:23.276819Z",
@@ -336,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +455,7 @@
        "True"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -443,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -452,7 +487,7 @@
        "[]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -471,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -480,7 +515,7 @@
        "[]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -508,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -578,13 +613,14 @@
     "| `$nin`   | Not contained in a set of given values  (not in)  |\n",
     "| `$between` | Between the range of two boundary values |\n",
     "| `$like`  | Text equality based on the \"LIKE\" semantics in SQL (using \"%\" as wildcard)  |\n",
+    "| `$contains` | Filters documents containing a specific keyword |\n",
     "| `$and`   | Logical \"and\", supporting 2 or more operands |\n",
     "| `$or`    | Logical \"or\", supporting 2 or more operands |"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -592,15 +628,15 @@
     "docs = [\n",
     "    Document(\n",
     "        page_content=\"First\",\n",
-    "        metadata={\"name\": \"adam\", \"is_active\": True, \"id\": 1, \"height\": 10.0},\n",
+    "        metadata={\"name\": \"Adam Smith\", \"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",
+    "        metadata={\"name\": \"Bob Johnson\", \"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",
+    "        metadata={\"name\": \"Jane Doe\", \"is_active\": True, \"id\": 3, \"height\": 2.4},\n",
     "    ),\n",
     "]\n",
     "\n",
@@ -632,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -640,19 +676,19 @@
      "output_type": "stream",
      "text": [
       "Filter: {'id': {'$ne': 1}}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
-      "{'name': 'jane', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
       "Filter: {'id': {'$gt': 1}}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
-      "{'name': 'jane', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
       "Filter: {'id': {'$gte': 1}}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
-      "{'name': 'jane', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n",
+      "{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
       "Filter: {'id': {'$lt': 1}}\n",
       "<empty result>\n",
       "Filter: {'id': {'$lte': 1}}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n"
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n"
      ]
     }
    ],
@@ -687,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -695,13 +731,13 @@
      "output_type": "stream",
      "text": [
       "Filter: {'id': {'$between': (1, 2)}}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
-      "Filter: {'name': {'$in': ['adam', 'bob']}}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
-      "Filter: {'name': {'$nin': ['adam', 'bob']}}\n",
-      "{'name': 'jane', 'is_active': True, 'id': 3, 'height': 2.4}\n"
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
+      "Filter: {'name': {'$in': ['Adam Smith', 'Bob Johnson']}}\n",
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
+      "Filter: {'name': {'$nin': ['Adam Smith', 'Bob Johnson']}}\n",
+      "{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}\n"
      ]
     }
    ],
@@ -710,11 +746,11 @@
     "print(f\"Filter: {advanced_filter}\")\n",
     "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))\n",
     "\n",
-    "advanced_filter = {\"name\": {\"$in\": [\"adam\", \"bob\"]}}\n",
+    "advanced_filter = {\"name\": {\"$in\": [\"Adam Smith\", \"Bob Johnson\"]}}\n",
     "print(f\"Filter: {advanced_filter}\")\n",
     "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))\n",
     "\n",
-    "advanced_filter = {\"name\": {\"$nin\": [\"adam\", \"bob\"]}}\n",
+    "advanced_filter = {\"name\": {\"$nin\": [\"Adam Smith\", \"Bob Johnson\"]}}\n",
     "print(f\"Filter: {advanced_filter}\")\n",
     "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))"
    ]
@@ -728,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -736,10 +772,10 @@
      "output_type": "stream",
      "text": [
       "Filter: {'name': {'$like': 'a%'}}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
+      "<empty result>\n",
       "Filter: {'name': {'$like': '%a%'}}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
-      "{'name': 'jane', 'is_active': True, 'id': 3, 'height': 2.4}\n"
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n",
+      "{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}\n"
      ]
     }
    ],
@@ -753,6 +789,51 @@
     "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Text filtering with `$contains`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filter: {'name': {'$contains': 'bob'}}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
+      "Filter: {'name': {'$contains': 'bo'}}\n",
+      "<empty result>\n",
+      "Filter: {'name': {'$contains': 'Adam Johnson'}}\n",
+      "<empty result>\n",
+      "Filter: {'name': {'$contains': 'Adam Smith'}}\n",
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "advanced_filter = {\"name\": {\"$contains\": \"bob\"}}\n",
+    "print(f\"Filter: {advanced_filter}\")\n",
+    "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))\n",
+    "\n",
+    "advanced_filter = {\"name\": {\"$contains\": \"bo\"}}\n",
+    "print(f\"Filter: {advanced_filter}\")\n",
+    "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))\n",
+    "\n",
+    "advanced_filter = {\"name\": {\"$contains\": \"Adam Johnson\"}}\n",
+    "print(f\"Filter: {advanced_filter}\")\n",
+    "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))\n",
+    "\n",
+    "advanced_filter = {\"name\": {\"$contains\": \"Adam Smith\"}}\n",
+    "print(f\"Filter: {advanced_filter}\")\n",
+    "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -762,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -770,14 +851,15 @@
      "output_type": "stream",
      "text": [
       "Filter: {'$or': [{'id': 1}, {'name': 'bob'}]}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n",
       "Filter: {'$and': [{'id': 1}, {'id': 2}]}\n",
       "<empty result>\n",
       "Filter: {'$or': [{'id': 1}, {'id': 2}, {'id': 3}]}\n",
-      "{'name': 'adam', 'is_active': True, 'id': 1, 'height': 10.0}\n",
-      "{'name': 'bob', 'is_active': False, 'id': 2, 'height': 5.7}\n",
-      "{'name': 'jane', 'is_active': True, 'id': 3, 'height': 2.4}\n"
+      "{'name': 'Adam Smith', 'is_active': True, 'id': 1, 'height': 10.0}\n",
+      "{'name': 'Jane Doe', 'is_active': True, 'id': 3, 'height': 2.4}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n",
+      "Filter: {'$and': [{'name': {'$contains': 'bob'}}, {'name': {'$contains': 'johnson'}}]}\n",
+      "{'name': 'Bob Johnson', 'is_active': False, 'id': 2, 'height': 5.7}\n"
      ]
     }
    ],
@@ -792,6 +874,12 @@
     "\n",
     "advanced_filter = {\"$or\": [{\"id\": 1}, {\"id\": 2}, {\"id\": 3}]}\n",
     "print(f\"Filter: {advanced_filter}\")\n",
+    "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))\n",
+    "\n",
+    "advanced_filter = {\n",
+    "    \"$and\": [{\"name\": {\"$contains\": \"bob\"}}, {\"name\": {\"$contains\": \"johnson\"}}]\n",
+    "}\n",
+    "print(f\"Filter: {advanced_filter}\")\n",
     "print_filter_result(db.similarity_search(\"just testing\", k=5, filter=advanced_filter))"
    ]
   },
@@ -804,13 +892,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
     "# Access the vector DB with a new table\n",
     "db = HanaDB(\n",
     "    connection=connection,\n",
@@ -837,7 +922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -874,6 +959,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "memory = ConversationBufferMemory(\n",
@@ -898,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -907,7 +994,7 @@
      "text": [
       "Answer from LLM:\n",
       "================\n",
-      "The United States has set up joint patrols with Mexico and Guatemala to catch more human traffickers. This collaboration is part of the efforts to address immigration issues and secure the borders in the region.\n",
+      "The United States has set up joint patrols with Mexico and Guatemala to catch more human traffickers at the border. This collaborative effort aims to improve border security and combat illegal activities such as human trafficking.\n",
       "================\n",
       "Number of used source document chunks: 5\n"
      ]
@@ -954,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -963,12 +1050,12 @@
      "text": [
       "Answer from LLM:\n",
       "================\n",
-      "Mexico and Guatemala are involved in joint patrols to catch human traffickers.\n"
+      "Countries like Mexico and Guatemala are participating in joint patrols to catch human traffickers. The United States is also working with partners in South and Central America to host more refugees and secure their borders. Additionally, the U.S. is working with twenty-seven members of the European Union, as well as countries like France, Germany, Italy, the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and Switzerland.\n"
      ]
     }
    ],
    "source": [
-    "question = \"What about other countries?\"\n",
+    "question = \"How many casualties were reported after that?\"\n",
     "\n",
     "result = qa_chain.invoke({\"question\": question})\n",
     "print(\"Answer from LLM:\")\n",
@@ -996,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1005,7 +1092,7 @@
        "[]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1038,7 +1125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1101,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1111,7 +1198,7 @@
       "None\n",
       "Some other text\n",
       "{\"start\": 400, \"end\": 450, \"doc_name\": \"other.txt\"}\n",
-      "<memory at 0x7f5edcb18d00>\n"
+      "<memory at 0x110f856c0>\n"
      ]
     }
    ],
@@ -1168,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1176,9 +1263,9 @@
      "output_type": "stream",
      "text": [
       "--------------------------------------------------------------------------------\n",
-      "Some other text\n",
+      "Some more text\n",
       "--------------------------------------------------------------------------------\n",
-      "Some more text\n"
+      "Some other text\n"
      ]
     }
    ],
@@ -1214,7 +1301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1224,7 +1311,7 @@
       "Filters on this value are very performant\n",
       "Some other text\n",
       "{\"start\": 400, \"end\": 450, \"doc_name\": \"other.txt\", \"CUSTOMTEXT\": \"Filters on this value are very performant\"}\n",
-      "<memory at 0x7f5edcb193c0>\n"
+      "<memory at 0x110f859c0>\n"
      ]
     }
    ],
@@ -1291,7 +1378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1299,9 +1386,9 @@
      "output_type": "stream",
      "text": [
       "--------------------------------------------------------------------------------\n",
-      "Some other text\n",
+      "Some more text\n",
       "--------------------------------------------------------------------------------\n",
-      "Some more text\n"
+      "Some other text\n"
      ]
     }
    ],
@@ -1330,9 +1417,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "dev311",
    "language": "python",
-   "name": "python3"
+   "name": "dev311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1344,7 +1431,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/ydb.ipynb

@@ -156,7 +156,7 @@
     "from langchain_core.documents import Document\n",
     "\n",
     "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    page_content=\"I had chocolate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
     "    metadata={\"source\": \"tweet\"},\n",
     ")\n",
     "\n",
@@ -356,7 +356,7 @@
      "output_type": "stream",
      "text": [
       "* [SIM=0.595] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n",
-      "* [SIM=0.212] I had chocalate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]\n",
+      "* [SIM=0.212] I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]\n",
       "* [SIM=0.118] Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]\n"
      ]
     }
@@ -387,7 +387,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* I had chocalate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]\n",
+      "* I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]\n",
       "* Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]\n",
       "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
       "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"

docs/docs/tutorials/classification.ipynb

@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "39f3ce3e",
    "metadata": {},
    "outputs": [],
@@ -118,15 +118,13 @@
     "    language: str = Field(description=\"The language the text is written in\")\n",
     "\n",
     "\n",
-    "# LLM\n",
-    "llm = ChatOpenAI(temperature=0, model=\"gpt-4o-mini\").with_structured_output(\n",
-    "    Classification\n",
-    ")"
+    "# Structured LLM\n",
+    "structured_llm = llm.with_structured_output(Classification)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "5509b6a6",
    "metadata": {},
    "outputs": [
@@ -144,7 +142,7 @@
    "source": [
     "inp = \"Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!\"\n",
     "prompt = tagging_prompt.invoke({\"input\": inp})\n",
-    "response = llm.invoke(prompt)\n",
+    "response = structured_llm.invoke(prompt)\n",
     "\n",
     "response"
    ]
@@ -159,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "9154474c",
    "metadata": {},
    "outputs": [
@@ -177,7 +175,7 @@
    "source": [
     "inp = \"Estoy muy enojado con vos! Te voy a dar tu merecido!\"\n",
     "prompt = tagging_prompt.invoke({\"input\": inp})\n",
-    "response = llm.invoke(prompt)\n",
+    "response = structured_llm.invoke(prompt)\n",
     "\n",
     "response.model_dump()"
    ]

docs/docs/tutorials/extraction.ipynb

@@ -145,15 +145,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a5e490f6-35ad-455e-8ae4-2bae021583ff",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Optional\n",
-    "\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from pydantic import BaseModel, Field\n",
     "\n",
     "# Define a custom prompt to provide instructions and any additional context.\n",
     "# 1) You can add examples into the prompt template to improve extraction quality\n",