core[patch]: release 0.3.44 (#30236)

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"


- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- 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/CODEOWNERS

@@ -1,2 +1,2 @@
-/.github/   @efriis @baskaryan @ccurme
-/libs/packages.yml   @efriis
+/.github/   @baskaryan @ccurme
+/libs/packages.yml   @ccurme

.github/PULL_REQUEST_TEMPLATE.md

@@ -26,4 +26,4 @@ Additional guidelines:
 - 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, efriis, eyurtsev, ccurme, vbarda, hwchase17.
+If no one reviews your PR within a few days, please @-mention one of baskaryan, eyurtsev, ccurme, vbarda, hwchase17.

.github/workflows/_release.yml

@@ -312,12 +312,88 @@ jobs:
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
+  # Test select published packages against new core
+  test-prior-published-packages-against-new-core:
+    needs:
+      - build
+      - release-notes
+      - test-pypi-publish
+      - pre-release-checks
+    if: ${{ startsWith(inputs.working-directory, 'libs/core') }}
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        partner: [openai, anthropic]
+      fail-fast: false  # Continue testing other partners if one fails
+    env:
+      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
+      AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
+      AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + uv
+        uses: "./.github/actions/uv_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: ${{ inputs.working-directory }}/dist/
+
+      - name: Test against ${{ matrix.partner }}
+        run: |
+          # Identify latest tag
+          LATEST_PACKAGE_TAG="$(
+            git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
+            | awk '{print $2}' \
+            | sed 's|refs/tags/||' \
+            | sort -Vr \
+            | head -n 1
+          )"
+          echo "Latest package tag: $LATEST_PACKAGE_TAG"
+
+          # Shallow-fetch just that single tag
+          git fetch --depth=1 origin tag "$LATEST_PACKAGE_TAG"
+
+          # Navigate to the partner directory
+          cd $GITHUB_WORKSPACE/libs/partners/${{ matrix.partner }}
+
+          # Checkout the latest package files
+          git checkout "$LATEST_PACKAGE_TAG" -- .
+
+          # Print as a sanity check
+          echo "Version number from pyproject.toml: "
+          cat pyproject.toml | grep "version = "
+
+          # Run tests
+          uv sync --group test --group test_integration
+          uv pip install ../../core/dist/*.whl
+          make integration_tests
+
   publish:
     needs:
       - build
       - release-notes
       - test-pypi-publish
       - pre-release-checks
+      - test-prior-published-packages-against-new-core
+    if: >
+      always() &&
+      needs.build.result == 'success' &&
+      needs.release-notes.result == 'success' &&
+      needs.test-pypi-publish.result == 'success' &&
+      needs.pre-release-checks.result == 'success' && (
+        (startsWith(inputs.working-directory, 'libs/core') && needs.test-prior-published-packages-against-new-core.result == 'success')
+        || (!startsWith(inputs.working-directory, 'libs/core'))
+      )
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:

Makefile

@@ -2,7 +2,6 @@
 
 .EXPORT_ALL_VARIABLES:
 UV_FROZEN = true
-UV_NO_SYNC = true
 
 ## help: Show this help info.
 help: Makefile

cookbook/elasticsearch_db_qa.ipynb

@@ -115,7 +115,7 @@
     "\n",
     "PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
     "\n",
-    "Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.\n",
+    "Unless told to do not query for all the columns from a specific index, only ask for a few relevant columns given the question.\n",
     "\n",
     "Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.\n",
     "\n",

cookbook/sql_db_qa.mdx

@@ -233,7 +233,7 @@ Question: {input}"""
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
 
-Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
 
 Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
 

docs/api_reference/_static/css/custom.css

@@ -328,7 +328,7 @@ html[data-theme=dark] .MathJax_SVG * {
 }
 
 .bd-sidebar-primary {
-    width: 22%;  /* Adjust this value to your preference */
+    width: max-content;  /* Adjust this value to your preference */
     line-height: 1.4;
 }
 

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

@@ -0,0 +1 @@
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docs/cassettes/configure_277e3232-9b77-4828-8082-b62f4d97127f.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/configure_42647fb7.msgpack.zlib

@@ -1 +1 @@
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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

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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

@@ -1 +1 @@
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

docs/docs/concepts/few_shot_prompting.mdx

@@ -30,7 +30,7 @@ At a high-level, the basic ways to generate examples are:
 - User feedback: users (or labelers) leave feedback on interactions with the application and examples are generated based on that feedback (for example, all interactions with positive feedback could be turned into examples).
 - LLM feedback: same as user feedback but the process is automated by having models evaluate themselves.
 
-Which approach is best depends on your task. For tasks where a small number core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
+Which approach is best depends on your task. For tasks where a small number of core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
 For tasks where the space of correct behaviors is broader and more nuanced, it can be useful to generate many examples in a more automated fashion so that there's a higher likelihood of there being some highly relevant examples for any runtime input.
 
 **Single-turn v.s. multi-turn examples**
@@ -39,8 +39,8 @@ Another dimension to think about when generating examples is what the example is
 
 The simplest types of examples just have a user input and an expected model output. These are single-turn examples.
 
-One more complex type if example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
-This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where its useful to show common errors and spell out exactly why they're wrong and what should be done instead.
+One more complex type of example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
+This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where it's useful to show common errors and spell out exactly why they're wrong and what should be done instead.
 
 ## 2. Number of examples
 
@@ -77,7 +77,7 @@ If we insert our examples as messages, where each example is represented as a se
 One area where formatting examples as messages can be tricky is when our example outputs have tool calls. This is because different models have different constraints on what types of message sequences are allowed when any tool calls are generated.
 - Some models require that any AIMessage with tool calls be immediately followed by ToolMessages for every tool call,
 - Some models additionally require that any ToolMessages be immediately followed by an AIMessage before the next HumanMessage,
-- Some models require that tools are passed in to the model if there are any tool calls / ToolMessages in the chat history.
+- Some models require that tools are passed into the model if there are any tool calls / ToolMessages in the chat history.
 
 These requirements are model-specific and should be checked for the model you are using. If your model requires ToolMessages after tool calls and/or AIMessages after ToolMessages and your examples only include expected tool calls and not the actual tool outputs, you can try adding dummy ToolMessages / AIMessages to the end of each example with generic contents to satisfy the API constraints.
 In these cases it's especially worth experimenting with inserting your examples as strings versus messages, as having dummy messages can adversely affect certain models.

docs/docs/concepts/streaming.mdx

@@ -91,7 +91,7 @@ For more information, please see:
 
 #### Usage with LCEL
 
-If you compose multiple Runnables using [LangChain’s Expression Language (LCEL)](/docs/concepts/lcel), the `stream()` and `astream()` methods will, by convention, stream the output of the last step in the chain. This allows the final processed result to be streamed incrementally. **LCEL** tries to optimize streaming latency in pipelines such that the streaming results from the last step are available as soon as possible.
+If you compose multiple Runnables using [LangChain’s Expression Language (LCEL)](/docs/concepts/lcel), the `stream()` and `astream()` methods will, by convention, stream the output of the last step in the chain. This allows the final processed result to be streamed incrementally. **LCEL** tries to optimize streaming latency in pipelines so that the streaming results from the last step are available as soon as possible.
 
 
 
@@ -104,7 +104,7 @@ Use the `astream_events` API to access custom data and intermediate outputs from
 While this API is available for use with [LangGraph](/docs/concepts/architecture#langgraph) as well, it is usually not necessary when working with LangGraph, as the `stream` and `astream` methods provide comprehensive streaming capabilities for LangGraph graphs.
 :::
 
-For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from te chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
+For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from the chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
 
 There are ways to do this [using callbacks](/docs/concepts/callbacks), or by constructing your chain in such a way that it passes intermediate
 values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an

docs/docs/contributing/how_to/documentation/setup.mdx

@@ -50,11 +50,6 @@ locally to ensure that it looks good and is free of errors.
 If you're unable to build it locally that's okay as well, as you will be able to
 see a preview of the documentation on the pull request page.
 
-From the **monorepo root**, run the following command to install the dependencies:
-
-```bash
-poetry install --with lint,docs --no-root
-````
 
 ### Building
 
@@ -158,14 +153,6 @@ the working directory to the `langchain-community` directory:
 cd [root]/libs/langchain-community
 ```
 
-Set up a virtual environment for the package if you haven't done so already.
-
-Install the dependencies for the package.
-
-```bash
-poetry install --with lint
-```
-
 Then you can run the following commands to lint and format the in-code documentation:
 
 ```bash

docs/docs/contributing/reference/faq.mdx

@@ -35,5 +35,5 @@ Please reference our [Review Process](review_process.mdx).
 
 ### I think my PR was closed in a way that didn't follow the review process. What should I do?
 
-Tag `@efriis` in the PR comments referencing the portion of the review
+Tag `@ccurme` in the PR comments referencing the portion of the review
 process that you believe was not followed. We'll take a look!

docs/docs/how_to/configure.ipynb

@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
+    "- [The Runnable interface](/docs/concepts/runnables/)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Binding runtime arguments](/docs/how_to/binding/)\n",
     "\n",
@@ -62,6 +62,163 @@
     "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9d25f63f-a048-42f3-ac2f-e20ba99cff16",
+   "metadata": {},
+   "source": [
+    "### Configuring fields on a chat model\n",
+    "\n",
+    "If using [init_chat_model](/docs/how_to/chat_models_universal_init/) to create a chat model, you can specify configurable fields in the constructor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "92ba5e49-b2b4-432b-b8bc-b03de46dc2bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "llm = init_chat_model(\n",
+    "    \"openai:gpt-4o-mini\",\n",
+    "    # highlight-next-line\n",
+    "    configurable_fields=(\"temperature\",),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61ef4976-9943-492b-9554-0d10e3d3ba76",
+   "metadata": {},
+   "source": [
+    "You can then set the parameter at runtime using `.with_config`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "277e3232-9b77-4828-8082-b62f4d97127f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello! How can I assist you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = llm.with_config({\"temperature\": 0}).invoke(\"Hello\")\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c5fe89-f0a6-4ff0-b419-b927e51cc9fa",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "In addition to invocation parameters like temperature, configuring fields this way extends to clients and other attributes.\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed7e600-4d5e-4875-8d37-082ec926e66f",
+   "metadata": {},
+   "source": [
+    "#### Use with tools\n",
+    "\n",
+    "This method is applicable when [binding tools](/docs/concepts/tool_calling/) as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "61a67769-4a15-49e2-a945-1f4e7ef19d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'get_weather',\n",
+       "  'args': {'location': 'San Francisco'},\n",
+       "  'id': 'call_B93EttzlGyYUhzbIIiMcl3bE',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(location: str):\n",
+    "    \"\"\"Get the weather.\"\"\"\n",
+    "    return \"It's sunny.\"\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([get_weather])\n",
+    "response = llm_with_tools.with_config({\"temperature\": 0}).invoke(\n",
+    "    \"What's the weather in SF?\"\n",
+    ")\n",
+    "response.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b71c7bf5-f351-4b90-ae86-1100d2dcdfaa",
+   "metadata": {},
+   "source": [
+    "In addition to `.with_config`, we can now include the parameter when passing a configuration directly. See example below, where we allow the underlying model temperature to be configurable inside of a [langgraph agent](/docs/tutorials/agents/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb36a46-7b67-4f11-b043-771f3005f493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install --upgrade langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "093d1c7d-1a64-4e6a-849f-075526b9b3ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent = create_react_agent(llm, [get_weather])\n",
+    "\n",
+    "response = agent.invoke(\n",
+    "    {\"messages\": \"What's the weather in Boston?\"},\n",
+    "    {\"configurable\": {\"temperature\": 0}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dc5be03-528f-4532-8cb0-1f149dddedc9",
+   "metadata": {},
+   "source": [
+    "### Configuring fields on arbitrary Runnables\n",
+    "\n",
+    "You can also use the `.configurable_fields` method on arbitrary [Runnables](/docs/concepts/runnables/), as shown below:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -604,7 +761,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/custom_tools.ipynb

@@ -22,7 +22,7 @@
     "2. LangChain [Runnables](/docs/concepts/runnables);\n",
     "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
     "\n",
-    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
+    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
     "\n",
     "In this guide we provide an overview of these methods.\n",
     "\n",
@@ -492,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "1dad8f8e",
    "metadata": {
     "execution": {
@@ -504,13 +504,14 @@
    },
    "outputs": [],
    "source": [
-    "from typing import Optional, Type\n",
+    "from typing import Optional\n",
     "\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
+    "from langchain_core.tools.base import ArgsSchema\n",
     "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
@@ -524,7 +525,7 @@
     "class CustomCalculatorTool(BaseTool):\n",
     "    name: str = \"Calculator\"\n",
     "    description: str = \"useful for when you need to answer questions about math\"\n",
-    "    args_schema: Type[BaseModel] = CalculatorInput\n",
+    "    args_schema: Optional[ArgsSchema] = CalculatorInput\n",
     "    return_direct: bool = True\n",
     "\n",
     "    def _run(\n",

docs/docs/how_to/document_loader_custom.ipynb

@@ -551,7 +551,7 @@
     "\n",
     "While a parser encapsulates the logic needed to parse binary data into documents, *blob loaders* encapsulate the logic that's necessary to load blobs from a given storage location.\n",
     "\n",
-    "A the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
+    "At the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
     "\n",
     "You can use the `FileSystemBlobLoader` to load blobs and then use the parser to parse them."
    ]

docs/docs/how_to/migrate_agent.ipynb

@@ -329,7 +329,7 @@
    "id": "fc6059fd-0df7-4b6f-a84c-b5874e983638",
    "metadata": {},
    "source": [
-    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a the graph state and output a list of messages.\n",
+    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a graph state and output a list of messages.\n",
     "We can do all types of arbitrary formatting of messages here. In this case, let's add a SystemMessage to the start of the list of messages and append another user message at the end."
    ]
   },

docs/docs/how_to/sql_large_db.ipynb

@@ -512,44 +512,6 @@
     "db.run(query)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "fcdd8432-07a4-4609-8214-b1591dd94950",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SELECT DISTINCT Genre.Name\n",
-      "FROM Genre\n",
-      "JOIN Track ON Genre.GenreId = Track.GenreId\n",
-      "JOIN Album ON Track.AlbumId = Album.AlbumId\n",
-      "JOIN Artist ON Album.ArtistId = Artist.ArtistId\n",
-      "WHERE Artist.Name = 'Elenis Moriset'\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Without retrieval\n",
-    "query = query_chain.invoke(\n",
-    "    {\"question\": \"What are all the genres of elenis moriset songs\", \"proper_nouns\": \"\"}\n",
-    ")\n",
-    "print(query)\n",
-    "db.run(query)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,

docs/docs/how_to/tool_artifacts.ipynb

@@ -87,13 +87,6 @@
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Failed to batch ingest runs: LangSmithRateLimitError('Rate limit exceeded for https://api.smith.langchain.com/runs/batch. HTTPError(\\'429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Monthly unique traces usage limit exceeded\"}\\')')\n"
-     ]
     }
    ],
    "source": [

docs/docs/how_to/trim_messages.ipynb

@@ -30,7 +30,8 @@
     "1. The resulting chat history should be **valid**. Usually this means that the following properties should be satisfied:\n",
     "   - The chat history **starts** with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/messages/#systemmessage) followed by a `HumanMessage`.\n",
     "   - The chat history **ends** with either a `HumanMessage` or a `ToolMessage`.\n",
-    "   - A `ToolMessage` can only appear after an `AIMessage` that involved a tool call. \n",
+    "   - A `ToolMessage` can only appear after an `AIMessage` that involved a tool call.\n",
+    "\n",
     "   This can be achieved by setting `start_on=\"human\"` and `ends_on=(\"human\", \"tool\")`.\n",
     "3. It includes recent messages and drops old messages in the chat history.\n",
     "   This can be achieved by setting `strategy=\"last\"`.\n",

docs/docs/integrations/chat/anthropic.ipynb

@@ -315,6 +315,59 @@
     "ai_msg.tool_calls"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6e36d25c-f358-49e5-aefa-b99fbd3fec6b",
+   "metadata": {},
+   "source": [
+    "## Extended thinking\n",
+    "\n",
+    "Claude 3.7 Sonnet supports an [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) feature, which will output the step-by-step reasoning process that led to its final answer.\n",
+    "\n",
+    "To use it, specify the `thinking` parameter when initializing `ChatAnthropic`. It can also be passed in as a kwarg during invocation.\n",
+    "\n",
+    "You will need to specify a token budget to use this feature. See usage example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a34cf93b-8522-43a6-a3f3-8a189ddf54a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  {\n",
+      "    \"signature\": \"ErUBCkYIARgCIkCx7bIPj35jGPHpoVOB2y5hvPF8MN4lVK75CYGftmVNlI4axz2+bBbSexofWsN1O/prwNv8yPXnIXQmwT6zrJsKEgwJzvks0yVRZtaGBScaDOm9xcpOxbuhku1zViIw9WDgil/KZL8DsqWrhVpC6TzM0RQNCcsHcmgmyxbgG9g8PR0eJGLxCcGoEw8zMQu1Kh1hQ1/03hZ2JCOgigpByR9aNPTwwpl64fQUe6WwIw==\",\n",
+      "    \"thinking\": \"To find the cube root of 50.653, I need to find the value of $x$ such that $x^3 = 50.653$.\\n\\nI can try to estimate this first. \\n$3^3 = 27$\\n$4^3 = 64$\\n\\nSo the cube root of 50.653 will be somewhere between 3 and 4, but closer to 4.\\n\\nLet me try to compute this more precisely. I can use the cube root function:\\n\\ncube root of 50.653 = 50.653^(1/3)\\n\\nLet me calculate this:\\n50.653^(1/3) \\u2248 3.6998\\n\\nLet me verify:\\n3.6998^3 \\u2248 50.6533\\n\\nThat's very close to 50.653, so I'm confident that the cube root of 50.653 is approximately 3.6998.\\n\\nActually, let me compute this more precisely:\\n50.653^(1/3) \\u2248 3.69981\\n\\nLet me verify once more:\\n3.69981^3 \\u2248 50.652998\\n\\nThat's extremely close to 50.653, so I'll say that the cube root of 50.653 is approximately 3.69981.\",\n",
+      "    \"type\": \"thinking\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"text\": \"The cube root of 50.653 is approximately 3.6998.\\n\\nTo verify: 3.6998\\u00b3 = 50.6530, which is very close to our original number.\",\n",
+      "    \"type\": \"text\"\n",
+      "  }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-3-7-sonnet-latest\",\n",
+    "    max_tokens=5000,\n",
+    "    thinking={\"type\": \"enabled\", \"budget_tokens\": 2000},\n",
+    ")\n",
+    "\n",
+    "response = llm.invoke(\"What is the cube root of 50.653?\")\n",
+    "print(json.dumps(response.content, indent=2))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "301d372f-4dec-43e6-b58c-eee25633e1a6",

docs/docs/integrations/chat/contextual.ipynb

@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: ContextualAI\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatContextual\n",
+    "\n",
+    "This will help you getting started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
+    "\n",
+    "To learn more about Contextual AI, please visit our [documentation](https://docs.contextual.ai/).\n",
+    "\n",
+    "This integration requires the `contextual-client` Python SDK. Learn more about it [here](https://github.com/ContextualAI/contextual-client-python).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This integration invokes Contextual AI's Grounded Language Model.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatContextual](https://github.com/ContextualAI//langchain-contextual) | [langchain-contextual](https://pypi.org/project/langchain-contextual/) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-contextual?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-contextual?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Contextual models you'll need to create a Contextual AI account, get an API key, and install the `langchain-contextual` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [app.contextual.ai](https://app.contextual.ai) to sign up to Contextual and generate an API key. Once you've done this set the CONTEXTUAL_AI_API_KEY environment variable:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"CONTEXTUAL_AI_API_KEY\"):\n",
+    "    os.environ[\"CONTEXTUAL_AI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your Contextual API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Contextual integration lives in the `langchain-contextual` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-contextual"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions.\n",
+    "\n",
+    "The chat client can be instantiated with these following additional settings:\n",
+    "\n",
+    "| Parameter | Type | Description | Default |\n",
+    "|-----------|------|-------------|---------|\n",
+    "| temperature | Optional[float] | The sampling temperature, which affects the randomness in the response. Note that higher temperature values can reduce groundedness. | 0 |\n",
+    "| top_p | Optional[float] | A parameter for nucleus sampling, an alternative to temperature which also affects the randomness of the response. Note that higher top_p values can reduce groundedness. | 0.9 |\n",
+    "| max_new_tokens | Optional[int] | The maximum number of tokens that the model can generate in the response. Minimum is 1 and maximum is 2048. | 1024 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_contextual import ChatContextual\n",
+    "\n",
+    "llm = ChatContextual(\n",
+    "    model=\"v1\",  # defaults to `v1`\n",
+    "    api_key=\"\",\n",
+    "    temperature=0,  # defaults to 0\n",
+    "    top_p=0.9,  # defaults to 0.9\n",
+    "    max_new_tokens=1024,  # defaults to 1024\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "The Contextual Grounded Language Model accepts additional `kwargs` when calling the `ChatContextual.invoke` method.\n",
+    "\n",
+    "These additional inputs are:\n",
+    "\n",
+    "| Parameter | Type | Description |\n",
+    "|-----------|------|-------------|\n",
+    "| knowledge | list[str] | Required: A list of strings of knowledge sources the grounded language model can use when generating a response. |\n",
+    "| system_prompt | Optional[str] | Optional: Instructions the model should follow when generating responses. Note that we do not guarantee that the model follows these instructions exactly. |\n",
+    "| avoid_commentary | Optional[bool] | Optional (Defaults to `False`): Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# include a system prompt (optional)\n",
+    "system_prompt = \"You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability.\"\n",
+    "\n",
+    "# provide your own knowledge from your knowledge-base here in an array of string\n",
+    "knowledge = [\n",
+    "    \"There are 2 types of dogs in the world: good dogs and best dogs.\",\n",
+    "    \"There are 2 types of cats in the world: good cats and best cats.\",\n",
+    "]\n",
+    "\n",
+    "# create your message\n",
+    "messages = [\n",
+    "    (\"human\", \"What type of cats are there in the world and what are the types?\"),\n",
+    "]\n",
+    "\n",
+    "# invoke the GLM by providing the knowledge strings, optional system prompt\n",
+    "# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument\n",
+    "ai_msg = llm.invoke(\n",
+    "    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True\n",
+    ")\n",
+    "\n",
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c35a9e0",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can chain the Contextual Model with output parsers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "545e1e16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "chain = llm | StrOutputParser\n",
+    "\n",
+    "chain.invoke(\n",
+    "    messages, knowledge=knowledge, systemp_prompt=system_prompt, avoid_commentary=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatContextual features and configurations head to the Github page: https://github.com/ContextualAI//langchain-contextual"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/deepseek.ipynb

@@ -38,6 +38,12 @@
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
     "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
     "\n",
+    ":::note\n",
+    "\n",
+    "DeepSeek-R1, specified via `model=\"deepseek-reasoner\"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model=\"deepseek-chat\"`).\n",
+    "\n",
+    ":::\n",
+    "\n",
     "## Setup\n",
     "\n",
     "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",

docs/docs/integrations/chat/groq.ipynb

@@ -85,21 +85,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "execution_count": null,
+   "id": "3f3f510e-2afe-4e76-be41-c5a9665aea63",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install -qU langchain-groq"
    ]
@@ -116,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
@@ -124,7 +113,7 @@
     "from langchain_groq import ChatGroq\n",
     "\n",
     "llm = ChatGroq(\n",
-    "    model=\"mixtral-8x7b-32768\",\n",
+    "    model=\"llama-3.1-8b-instant\",\n",
     "    temperature=0,\n",
     "    max_tokens=None,\n",
     "    timeout=None,\n",
@@ -143,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
@@ -152,10 +141,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='I enjoy programming. (The French translation is: \"J\\'aime programmer.\")\\n\\nNote: I chose to translate \"I love programming\" as \"J\\'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.', response_metadata={'token_usage': {'completion_tokens': 73, 'prompt_tokens': 31, 'total_tokens': 104, 'completion_time': 0.1140625, 'prompt_time': 0.003352463, 'queue_time': None, 'total_time': 0.117414963}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-64433c19-eadf-42fc-801e-3071e3c40160-0', usage_metadata={'input_tokens': 31, 'output_tokens': 73, 'total_tokens': 104})"
+       "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
    "metadata": {},
    "outputs": [
@@ -182,9 +171,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "I enjoy programming. (The French translation is: \"J'aime programmer.\")\n",
+      "The translation of \"I love programming\" to French is:\n",
       "\n",
-      "Note: I chose to translate \"I love programming\" as \"J'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.\n"
+      "\"J'adore le programmation.\"\n"
      ]
     }
    ],
@@ -204,17 +193,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='That\\'s great! I can help you translate English phrases related to programming into German.\\n\\n\"I love programming\" can be translated as \"Ich liebe Programmieren\" in German.\\n\\nHere are some more programming-related phrases translated into German:\\n\\n* \"Programming language\" = \"Programmiersprache\"\\n* \"Code\" = \"Code\"\\n* \"Variable\" = \"Variable\"\\n* \"Function\" = \"Funktion\"\\n* \"Array\" = \"Array\"\\n* \"Object-oriented programming\" = \"Objektorientierte Programmierung\"\\n* \"Algorithm\" = \"Algorithmus\"\\n* \"Data structure\" = \"Datenstruktur\"\\n* \"Debugging\" = \"Fehlersuche\"\\n* \"Compile\" = \"Kompilieren\"\\n* \"Link\" = \"Verknüpfen\"\\n* \"Run\" = \"Ausführen\"\\n* \"Test\" = \"Testen\"\\n* \"Deploy\" = \"Bereitstellen\"\\n* \"Version control\" = \"Versionskontrolle\"\\n* \"Open source\" = \"Open Source\"\\n* \"Software development\" = \"Softwareentwicklung\"\\n* \"Agile methodology\" = \"Agile Methodik\"\\n* \"DevOps\" = \"DevOps\"\\n* \"Cloud computing\" = \"Cloud Computing\"\\n\\nI hope this helps! Let me know if you have any other questions or if you need further translations.', response_metadata={'token_usage': {'completion_tokens': 331, 'prompt_tokens': 25, 'total_tokens': 356, 'completion_time': 0.520006542, 'prompt_time': 0.00250165, 'queue_time': None, 'total_time': 0.522508192}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-74207fb7-85d3-417d-b2b9-621116b75d41-0', usage_metadata={'input_tokens': 25, 'output_tokens': 331, 'total_tokens': 356})"
+       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -269,7 +258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/tongyi.ipynb

@@ -26,22 +26,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Install the package\n",
     "%pip install --upgrade --quiet  dashscope"
@@ -49,8 +36,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-05T01:11:20.457141Z",
+     "start_time": "2025-03-05T01:11:18.810160Z"
+    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -66,8 +57,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-05T01:11:24.270318Z",
+     "start_time": "2025-03-05T01:11:24.268064Z"
+    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -266,6 +261,52 @@
     "ai_message"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Partial Mode\n",
+    "Enable the large model to continue generating content from the initial text you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-05T01:31:29.155824Z",
+     "start_time": "2025-03-05T01:31:27.239667Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' has cast off its heavy cloak of snow, donning instead a vibrant garment of fresh greens and floral hues; it is as if the world has woken from a long slumber, stretching and reveling in the warm caress of the sun. Everywhere I look, there is a symphony of life: birdsong fills the air, bees dance from flower to flower, and a gentle breeze carries the sweet fragrance of blossoms. It is in this season that my heart finds particular joy, for it whispers promises of renewal and growth, reminding me that even after the coldest winters, there will always be a spring to follow.', additional_kwargs={}, response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'stop', 'request_id': '447283e9-ee31-9d82-8734-af572921cb05', 'token_usage': {'input_tokens': 40, 'output_tokens': 127, 'prompt_tokens_details': {'cached_tokens': 0}, 'total_tokens': 167}}, id='run-6a35a91c-cc12-4afe-b56f-fd26d9035357-0')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
+    "from langchain_core.messages import AIMessage, HumanMessage\n",
+    "\n",
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"\"\"Please continue the sentence \"Spring has arrived, and the earth\" to express the beauty of spring and the author's joy.\"\"\"\n",
+    "    ),\n",
+    "    AIMessage(\n",
+    "        content=\"Spring has arrived, and the earth\", additional_kwargs={\"partial\": True}\n",
+    "    ),\n",
+    "]\n",
+    "chatLLM = ChatTongyi()\n",
+    "ai_message = chatLLM.invoke(messages)\n",
+    "ai_message"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/docs/integrations/chat/writer.ipynb

@@ -1,362 +1,231 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "85e07aae70a15572",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Writer\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "cb4dd00a-8893-4a45-96f7-9a9fc341cd61",
+   "id": "e815de6298bf07ca",
    "metadata": {},
    "source": [
-    "# ChatWriter\n",
+    "# Chat Writer\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Writer [chat models](/docs/concepts/chat_models).\n",
+    "This notebook provides a quick overview for getting started with Writer [chat](/docs/concepts/chat_models/).\n",
+    "\n",
+    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home).\n",
     "\n",
-    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home/models).\n",
     "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "617a6e98205ab7c8",
-   "metadata": {},
-   "source": [
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |:----------:| :---: | :---: |\n",
-    "| ChatWriter | langchain-community | ❌ | ❌ |     ❌      | ❌ | ❌ |\n",
-    "\n",
+    "| Class                                                                                                                    | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [ChatWriter](https://github.com/writer/langchain-writer/blob/main/langchain_writer/chat_models.py#L308) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | Structured output | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async |         [Token usage](/docs/how_to/chat_token_usage_tracking/)          | Logprobs |\n",
     "| :---: |:-----------------:| :---: | :---: |  :---: | :---: | :---: | :---: |:--------------------------------:|:--------:|\n",
-    "| ✅ |         ❌         | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ |                ✅                 |    ❌     |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Writer models you'll need to create a Writer account, get an API key, and install the `writer-sdk` and `langchain-community` packages.\n",
-    "\n",
+    "| ✅ |         ❌         | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ |                ✅                 |    ❌     |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fd9903e685808d9",
+   "metadata": {},
+   "source": [
     "### Credentials\n",
     "\n",
-    "Head to [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to sign up to OpenAI and generate an API key. Once you've done this set the WRITER_API_KEY environment variable:"
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) and follow this [Quickstart](https://dev.writer.com/api-guides/quickstart) to obtain an API key. Then, set the WRITER_API_KEY environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "e817fe2e-4f1d-4533-b19e-2400b1cf6ce8",
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:26.800627Z",
-     "start_time": "2024-11-14T09:27:59.652281Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
-   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if not os.environ.get(\"WRITER_API_KEY\"):\n",
-    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key:\")"
-   ]
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "c59722a9-6dbb-45f7-ae59-5be50ca5733d",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
    "metadata": {},
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain Writer integration lives in the `langchain-community` package:"
+    "`ChatWriter` is available from the `langchain-writer` package. Install it with:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "2113471c-75d7-45df-b784-d78da4ef7aba",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:32.415354Z",
-     "start_time": "2024-11-14T09:46:26.826112Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
    "source": [
-    "%pip install -qU langchain-community writer-sdk"
-   ]
+    "%pip install -qU langchain-writer"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "1098bc9d-ce83-462b-8c19-f85bf3a159dc",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
    "metadata": {},
    "source": [
-    "## Instantiation\n",
+    "### Instantiation\n",
     "\n",
-    "Now we can instantiate our model object and generate chat completions:"
+    "Now we can instantiate our model object in order to generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "522686de",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:33.504711Z",
-     "start_time": "2024-11-14T09:46:32.574505Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
    "source": [
-    "from langchain_community.chat_models.writer import ChatWriter\n",
+    "from langchain_writer import ChatWriter\n",
     "\n",
     "llm = ChatWriter(\n",
     "    model=\"palmyra-x-004\",\n",
-    "    temperature=0.7,\n",
-    "    max_tokens=1000,\n",
-    "    # other params...\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "6511982a-734a-4193-a47d-254f8dcaff5e",
+   "id": "2b4f3e15",
    "metadata": {},
    "source": [
-    "## Invocation"
+    "## Usage\n",
+    "\n",
+    "To use the model, you pass in a list of messages and call the `invoke` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
+   "id": "62e0dbc3",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.856174Z",
-     "start_time": "2024-11-14T09:46:33.520062Z"
-    },
     "tags": []
    },
-   "outputs": [],
    "source": [
     "messages = [\n",
     "    (\n",
     "        \"system\",\n",
-    "        \"You are a helpful assistant that writes poems about the Python programming language.\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
     "    ),\n",
-    "    (\"human\", \"Write a poem about Python.\"),\n",
+    "    (\"human\", \"I love programming.\"),\n",
     "]\n",
-    "ai_msg = llm.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2cd224b8-4499-41fb-a604-d53a7ff17b2e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.866651Z",
-     "start_time": "2024-11-14T09:46:38.863817Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In realms of code, where logic weaves and flows,\n",
-      "A language rises, Python by its name,\n",
-      "With syntax clear, where elegance it shows,\n",
-      "A serpent, wise, that time and space can tame.\n",
-      "\n",
-      "Born from the mind of Guido, pure and bright,\n",
-      "Its beauty lies in simplicity and grace,\n",
-      "A tool of power, yet gentle in its might,\n",
-      "In every programmer's heart, a cherished place.\n",
-      "\n",
-      "It dances through the data, vast and deep,\n",
-      "With libraries that span the digital realm,\n",
-      "From machine learning's secrets to keep,\n",
-      "To web development, it wields the helm.\n",
-      "\n",
-      "In the hands of the novice and the sage,\n",
-      "Python spins the threads of digital dreams,\n",
-      "A language that can turn the age,\n",
-      "With a gentle learning curve, its appeal gleams.\n",
-      "\n",
-      "It's more than code, a community it builds,\n",
-      "Where knowledge freely flows, and all are heard,\n",
-      "In Python's world, the future unfolds,\n",
-      "A language of the people, for the world.\n",
-      "\n",
-      "So here's to Python, in its gentle might,\n",
-      "A master of the modern coding art,\n",
-      "May it continue to light our path each night,\n",
-      "In the vast, evolving world of code, its heart.\n"
-     ]
-    }
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "35b3a5b3dabef65",
+   "id": "5cf7293d",
    "metadata": {},
    "source": [
-    "## Streaming"
+    "Then, you can access the content of the message:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "2725770182bf96dc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.914883Z",
-     "start_time": "2024-11-14T09:46:38.912564Z"
-    }
-   },
-   "outputs": [],
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
    "source": [
-    "ai_stream = llm.stream(messages)"
+    "print(ai_msg.content)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4391289ce0a80e19",
+   "metadata": {},
+   "source": [
+    "## Streaming\n",
+    "\n",
+    "You can also stream the response. First, create a stream:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a48410d9488162e3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:43.226449Z",
-     "start_time": "2024-11-14T09:46:38.955512Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In realms of code where logic weaves,\n",
-      "A language rises, Python, it breezes,\n",
-      "With syntax clear and simple to read,\n",
-      "Through its elegance, our spirits are fed.\n",
-      "\n",
-      "Like rivers flowing, smooth and serene,\n",
-      "Its structure harmonious, a coder's dream,\n",
-      "Indentations guide the flow of control,\n",
-      "In Python's world, confusion takes no toll.\n",
-      "\n",
-      "A vast library, a treasure trove so bright,\n",
-      "For web and data, it offers its might,\n",
-      "With modules and packages, a rich array,\n",
-      "Python empowers us to code in play.\n",
-      "\n",
-      "From AI to scripts, in flexibility it thrives,\n",
-      "A language of the future, as many now derive,\n",
-      "Its community, a beacon of support and cheer,\n",
-      "With Python, the possibilities are vast, far and near.\n",
-      "\n",
-      "So here's to Python, in its gentle grace,\n",
-      "A tool that enhances, a language that embraces,\n",
-      "The art of coding, with a fluent, flowing pen,\n",
-      "In the Python world, we code, and we begin."
-     ]
-    }
+   "id": "4a0f2112b3a4c79e",
+   "metadata": {},
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming. Sing a song about it\"),\n",
+    "]\n",
+    "ai_stream = llm.stream(messages)\n",
+    "ai_stream"
    ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23cc74b6",
+   "metadata": {},
+   "source": [
+    "Then, iterate over the stream to get the chunks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8c4b7b9b9308c757",
+   "metadata": {},
    "source": [
     "for chunk in ai_stream:\n",
     "    print(chunk.content, end=\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fbb043e6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:50.721645Z",
-     "start_time": "2024-11-14T09:46:43.234590Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessageChunk(content='In the realm of code, where logic weaves and flows,  \\nA language rises, like a phoenix from the code\\'s throes.  \\nJava, the name, a cup of coffee\\'s steam,  \\nBrewed in the minds, where digital dreams gleam.\\n\\nWith syntax clear, like morning\\'s misty hue,  \\nIn classes and objects, it spins a tale so true.  \\nA platform agnostic, with a byte to spare,  \\nAcross the devices, it journeys everywhere.\\n\\nInheritance and polymorphism, its power\\'s core,  \\nLike ancient runes, in every line they bore.  \\nEncapsulation, a shield, with data it does hide,  \\nIn the vast jungle of code, it stands as a guide.\\n\\nFrom applets small, to vast, server-side apps,  \\nIts threads run swift, through the computing traps.  \\nA language of the people, by the people, for the people’s use,  \\nBuilt on the principle, \"write once, run anywhere, with no excuse.\"\\n\\nIn the heart of Android, it beats, a steady drum,  \\nCrafting experiences, in every smartphone\\'s hum.  \\nIn the cloud, in the enterprise, its presence is vast,  \\nA cornerstone of computing, built to last.\\n\\nOh Java, thy elegance, thy robust design,  \\nA language that stands, in any computing line.  \\nWith every update, with every new release,  \\nThy community grows, with a vibrant, diverse peace.\\n\\nSo here\\'s to Java, the versatile, the grand,  \\nA language that shapes the digital land.  \\nMay it continue to evolve, to grow, to inspire,  \\nIn the endless quest of turning thoughts into digital fire.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 345, 'prompt_tokens': 33, 'total_tokens': 378, 'completion_tokens_details': None, 'prompt_token_details': None}, 'model_name': 'palmyra-x-004', 'system_fingerprint': 'v1', 'finish_reason': 'stop'}, id='run-a5b4be59-0eb0-41bd-80f7-72477861b0bd-0')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that writes poems about the {input_language} programming language.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"Java\",\n",
-    "        \"input\": \"Write a poem about Java.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "0b1b52a5-b58d-40c9-bcdd-88eb8fb351e2",
+   "id": "e632bf7d0873f933",
    "metadata": {},
    "source": [
     "## Tool calling\n",
     "\n",
-    "Writer supports [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool.\n",
+    "Writer models like Palmyra X 004 support [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments. The model will return a JSON object with a tool to invoke and the inputs to that tool.\n",
     "\n",
-    "### ChatWriter.bind_tools()\n",
+    "### Binding tools\n",
     "\n",
-    "With `ChatWriter.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to tool schemas, which looks like:\n",
+    "With `ChatWriter.bind_tools`, you can easily pass in Pydantic classes, dictionary schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to tool schemas, which look like this:\n",
     "```\n",
     "{\n",
     "    \"name\": \"...\",\n",
@@ -364,20 +233,15 @@
     "    \"parameters\": {...}  # JSONSchema\n",
     "}\n",
     "```\n",
-    "and passed in every model invocation."
+    "These are passed in every model invocation.\n",
+    "\n",
+    "For example, to use a tool that gets the weather in a given location, you can define a Pydantic class and pass it to `ChatWriter.bind_tools`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "b7ea7690-ec7a-4337-b392-e87d1f39a6ec",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:50.891937Z",
-     "start_time": "2024-11-14T09:46:50.733463Z"
-    }
-   },
-   "outputs": [],
+   "id": "47e2f0faceca533",
+   "metadata": {},
    "source": [
     "from pydantic import BaseModel, Field\n",
     "\n",
@@ -388,86 +252,175 @@
     "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
     "\n",
     "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1d1ab955-6a68-42f8-bb5d-86eb1111478a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:51.725422Z",
-     "start_time": "2024-11-14T09:46:50.904699Z"
-    }
-   },
+    "llm.bind_tools([GetWeather])"
+   ],
    "outputs": [],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in New York City\",\n",
-    ")"
-   ]
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "768d1ae4-4b1a-48eb-a329-c8d5051067a3",
+   "id": "68e22d3b",
    "metadata": {},
    "source": [
-    "### AIMessage.tool_calls\n",
-    "Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
+    "Then, you can invoke the model with the tool:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "166cb7ce-831d-4a7c-9721-abc107f11084",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:51.744202Z",
-     "start_time": "2024-11-14T09:46:51.738431Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'GetWeather',\n",
-       "  'args': {'location': 'New York City, NY'},\n",
-       "  'id': 'chatcmpl-tool-fe70912c800d40fc8700d604d4823001',\n",
-       "  'type': 'tool_call'}]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "id": "765527dd533ec967",
+   "metadata": {},
+   "source": [
+    "ai_msg = llm.invoke(\n",
+    "    \"what is the weather like in New York City\",\n",
+    ")\n",
+    "ai_msg"
    ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57544bdf",
+   "metadata": {},
+   "source": [
+    "Finally, you can access the tool calls and proceed to execute your functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f361c4769e772fe",
+   "metadata": {},
    "source": [
     "print(ai_msg.tool_calls)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "e082c9ac-c7c7-4aff-a8ec-8e220262a59c",
+   "id": "3baf53021834d2ff",
    "metadata": {},
    "source": [
-    "For more on binding tools and tool call outputs, head to the [tool calling](/docs/how_to/function_calling) docs."
+    "### A note on tool binding\n",
+    "\n",
+    "The `ChatWriter.bind_tools()` method does not create new instance with bound tools, but stores the received `tools` and `tool_choice` in the initial class instance attributes to pass them as parameters during the Palmyra LLM call while using `ChatWriter` invocation. This approach allows the support of different tool types, e.g. `function` and `graph`. `Graph` is one of the remotely called Writer Palmyra tools. For further information visit our [docs](https://dev.writer.com/api-guides/knowledge-graph#knowledge-graph). \n",
+    "\n",
+    "For more information about tool usage in LangChain, visit the [LangChain tool calling documentation](https://python.langchain.com/docs/concepts/tool_calling/)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a796d728-971b-408b-88d5-440015bbb941",
+   "id": "a4674b1b82ce9d1f",
+   "metadata": {},
+   "source": [
+    "## Batching\n",
+    "\n",
+    "You can also batch requests and set the `max_concurrency`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c8a217f6190747fe",
+   "metadata": {},
+   "source": [
+    "ai_batch = llm.batch(\n",
+    "    [\n",
+    "        \"How to cook pancakes?\",\n",
+    "        \"How to compose poem?\",\n",
+    "        \"How to run faster?\",\n",
+    "    ],\n",
+    "    config={\"max_concurrency\": 3},\n",
+    ")\n",
+    "ai_batch"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eb81e1d",
+   "metadata": {},
+   "source": [
+    "Then, iterate over the batch to get the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b6a228d448f3df23",
+   "metadata": {},
+   "source": [
+    "for batch in ai_batch:\n",
+    "    print(batch.content)\n",
+    "    print(\"-\" * 100)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a9ab241fe09a71",
+   "metadata": {},
+   "source": [
+    "## Asynchronous usage\n",
+    "\n",
+    "All features above (invocation, streaming, batching, tools calling) also support asynchronous usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Prompt templates\n",
+    "\n",
+    "[Prompt templates](https://python.langchain.com/docs/concepts/prompt_templates/) help to translate user input and parameters into instructions for a language model. You can use `ChatWriter` with a prompt templates like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
    "metadata": {},
    "source": [
     "## API reference\n",
+    "For detailed documentation of all ChatWriter features and configurations head to the [API reference](https://python.langchain.com/api_reference/writer/chat_models/langchain_writer.chat_models.ChatWriter.html#langchain_writer.chat_models.ChatWriter).\n",
     "\n",
-    "For detailed documentation of all Writer features, head to our [API reference](https://dev.writer.com/api-guides/api-reference/completion-api/chat-completion)."
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home)."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -481,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/parsers/writer_pdf_parser.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "db23d51760310705",
+   "metadata": {},
+   "source": [
+    "# Writer PDF Parser\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with the Writer `PDFParser` [document loader](/docs/concepts/document_loaders/).\n",
+    "\n",
+    "Writer's [PDF Parser](https://dev.writer.com/api-guides/api-reference/tool-api/pdf-parser#parse-pdf) converts PDF documents into other formats like text or Markdown. This is particularly useful when you need to extract and process text content from PDF files for further analysis or integration into your workflow. In `langchain-writer`, we provide usage of Writer's PDF Parser as a LangChain document parser.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "| Class                                                                                                                              | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [PDFParser](https://github.com/writer/langchain-writer/blob/main/langchain_writer/pdf_parser.py#L55) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f08d23df5dc127",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The `PDFParser` is available in the `langchain-writer` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a8d653f15b7ee32d",
+   "metadata": {},
+   "source": [
+    "%pip install --quiet -U langchain-writer"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b9709c26797edf",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to generate an API key (you can follow this [Quickstart](https://dev.writer.com/api-guides/quickstart)). Then, set the WRITER_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2983e19c9d555e58",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92a22c77f03d43dc",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability. If you wish to do so, you can set the `LANGCHAIN_TRACING_V2` and `LANGCHAIN_API_KEY` environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "98d8422ecee77403",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ab78950a3da8ba",
+   "metadata": {},
+   "source": [
+    "### Instantiation\n",
+    "\n",
+    "Next, instantiate an instance of the Writer PDF Parser with the desired output format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "787b3ba8af32533f",
+   "metadata": {},
+   "source": [
+    "from langchain_writer.pdf_parser import PDFParser\n",
+    "\n",
+    "parser = PDFParser(format=\"markdown\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d91c6f752fd31cee",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "There are two ways to use the PDF Parser, either synchronously or asynchronously. In either case, the PDF Parser will return a list of `Document` objects, each containing the parsed content of a page from the PDF file.\n",
+    "\n",
+    "### Synchronous usage\n",
+    "\n",
+    "To invoke the PDF Parser synchronously, pass a `Blob` object to the `parse` method referencing the PDF file you want to parse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d1a24b81a8a96f09",
+   "metadata": {},
+   "source": [
+    "from langchain_core.documents.base import Blob\n",
+    "\n",
+    "file = Blob.from_path(\"../example_data/layout-parser-paper.pdf\")\n",
+    "\n",
+    "parsed_pages = parser.parse(blob=file)\n",
+    "parsed_pages"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89c048c7d23807a",
+   "metadata": {},
+   "source": [
+    "### Asynchronous usage\n",
+    "\n",
+    "To invoke the PDF Parser asynchronously, pass a `Blob` object to the `aparse` method referencing the PDF file you want to parse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e2f7fd52b7188c6c",
+   "metadata": {},
+   "source": [
+    "parsed_pages_async = await parser.aparse(blob=file)\n",
+    "parsed_pages_async"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab25a3bed8437a05",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `PDFParser` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/writer/pdf_parser/langchain_writer.pdf_parser.PDFParser.html#langchain_writer.pdf_parser.PDFParser).\n",
+    "\n",
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/pymupdf4llm.ipynb

@@ -0,0 +1,721 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: PyMuPDF4LLM\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyMuPDF4LLMLoader\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with PyMuPDF4LLM [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all PyMuPDF4LLMLoader features and configurations head to the [GitHub repository](https://github.com/lakinduboteju/langchain-pymupdf4llm).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support |\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyMuPDF4LLMLoader](https://github.com/lakinduboteju/langchain-pymupdf4llm) | [langchain_pymupdf4llm](https://pypi.org/project/langchain-pymupdf4llm) | ✅ | ❌ | ❌ |\n",
+    "\n",
+    "### Loader features\n",
+    "\n",
+    "| Source | Document Lazy Loading | Native Async Support | Extract Images | Extract Tables |\n",
+    "| :---: | :---: | :---: | :---: | :---: |\n",
+    "| PyMuPDF4LLMLoader | ✅ | ❌ | ✅ | ✅ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access PyMuPDF4LLM document loader you'll need to install the `langchain-pymupdf4llm` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are required to use PyMuPDF4LLMLoader."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **langchain-pymupdf4llm**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU langchain_community langchain-pymupdf4llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_pymupdf4llm import PyMuPDF4LLMLoader\n",
+    "\n",
+    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+    "loader = PyMuPDF4LLMLoader(file_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'trapped': '', 'modDate': 'D:20210622012710Z', 'creationDate': 'D:20210622012710Z', 'page': 0}, page_content='```\\nLayoutParser: A Unified Toolkit for Deep\\n\\n## Learning Based Document Image Analysis\\n\\n```\\n\\nZejiang Shen[1] (<28>), Ruochen Zhang[2], Melissa Dell[3], Benjamin Charles Germain\\nLee[4], Jacob Carlson[3], and Weining Li[5]\\n\\n1 Allen Institute for AI\\n```\\n              shannons@allenai.org\\n\\n```\\n2 Brown University\\n```\\n             ruochen zhang@brown.edu\\n\\n```\\n3 Harvard University\\n_{melissadell,jacob carlson}@fas.harvard.edu_\\n4 University of Washington\\n```\\n              bcgl@cs.washington.edu\\n\\n```\\n5 University of Waterloo\\n```\\n              w422li@uwaterloo.ca\\n\\n```\\n\\n**Abstract. Recent advances in document image analysis (DIA) have been**\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of important innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applications. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digitization pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\n[The library is publicly available at https://layout-parser.github.io.](https://layout-parser.github.io)\\n\\n**Keywords: Document Image Analysis · Deep Learning · Layout Analysis**\\n\\n    - Character Recognition · Open Source library · Toolkit.\\n\\n### 1 Introduction\\n\\n\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\n\\n')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        pages = []\n",
+    "len(pages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Markdown, display\n",
+    "\n",
+    "part = pages[0].page_content[778:1189]\n",
+    "print(part)\n",
+    "# Markdown rendering\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint.pp(pages[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata attribute contains at least the following keys:\n",
+    "- source\n",
+    "- page (if in mode *page*)\n",
+    "- total_page\n",
+    "- creationdate\n",
+    "- creator\n",
+    "- producer\n",
+    "\n",
+    "Additional metadata are specific to each parser.\n",
+    "These pieces of information can be helpful (to categorize your PDFs for example)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splitting mode & custom pages delimiter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When loading the PDF file you can split it in two different ways:\n",
+    "- By page\n",
+    "- As a single text flow\n",
+    "\n",
+    "By default PyMuPDF4LLMLoader will split the PDF by page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the PDF by page. Each page is extracted as a langchain Document object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16\n",
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(len(docs))\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this mode the pdf is split by pages and the resulting Documents metadata contains the `page` (page number). But in some cases we could want to process the pdf as a single text flow (so we don't cut some paragraphs in half). In this case you can use the *single* mode :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the whole PDF as a single langchain Document object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"single\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(len(docs))\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Logically, in this mode, the `page` (page_number) metadata disappears. Here's how to clearly identify where pages end in the text flow :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add a custom *pages_delimiter* to identify where are ends of pages in *single* mode:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"single\",\n",
+    "    pages_delimiter=\"\\n-------THIS IS A CUSTOM END OF PAGE-------\\n\\n\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[0].page_content[10663:11317]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default `pages_delimiter` is \\n-----\\n\\n.\n",
+    "But this could simply be \\n, or \\f to clearly indicate a page change, or \\<!-- PAGE BREAK --> for seamless injection in a Markdown viewer without a visual effect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract images from the PDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can extract images from your PDFs (in text form) with a choice of three different solutions:\n",
+    "- rapidOCR (lightweight Optical Character Recognition tool)\n",
+    "- Tesseract (OCR tool with high precision)\n",
+    "- Multimodal language model\n",
+    "\n",
+    "The result is inserted at the end of text of the page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with rapidOCR:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU rapidocr-onnxruntime pillow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import RapidOCRBlobParser\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=RapidOCRBlobParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[5].page_content[1863:]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Be careful, RapidOCR is designed to work with Chinese and English, not other languages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with Tesseract:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU pytesseract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import TesseractBlobParser\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=TesseractBlobParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(docs[5].page_content[1863:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with multimodal model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU langchain_openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "\n",
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API key =\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import LLMImageBlobParser\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=LLMImageBlobParser(\n",
+    "        model=ChatOpenAI(model=\"gpt-4o-mini\", max_tokens=1024)\n",
+    "    ),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(docs[5].page_content[1863:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract tables from the PDF\n",
+    "\n",
+    "With PyMUPDF4LLM you can extract tables from your PDFs in *markdown* format :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    # \"lines_strict\" is the default strategy and\n",
+    "    # is the most accurate for tables with column and row lines,\n",
+    "    # but may not work well with all documents.\n",
+    "    # \"lines\" is a less strict strategy that may work better with\n",
+    "    # some documents.\n",
+    "    # \"text\" is the least strict strategy and may work better\n",
+    "    # with documents that do not have tables with lines.\n",
+    "    table_strategy=\"lines\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[4].page_content[3210:]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Files\n",
+    "\n",
+    "Many document loaders involve parsing files. The difference between such loaders usually stems from how the file is parsed, rather than how the file is loaded. For example, you can use `open` to read the binary content of either a PDF or a markdown file, but you need different parsing logic to convert that binary data into text.\n",
+    "\n",
+    "As a result, it can be helpful to decouple the parsing logic from the loading logic, which makes it easier to re-use a given parser regardless of how the data was loaded.\n",
+    "You can use this strategy to analyze different files, with the same parsing parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import FileSystemBlobLoader\n",
+    "from langchain_community.document_loaders.generic import GenericLoader\n",
+    "from langchain_pymupdf4llm import PyMuPDF4LLMParser\n",
+    "\n",
+    "loader = GenericLoader(\n",
+    "    blob_loader=FileSystemBlobLoader(\n",
+    "        path=\"./example_data/\",\n",
+    "        glob=\"*.pdf\",\n",
+    "    ),\n",
+    "    blob_parser=PyMuPDF4LLMParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[0].page_content[:562]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PyMuPDF4LLMLoader features and configurations head to the GitHub repository: https://github.com/lakinduboteju/langchain-pymupdf4llm"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/document_transformers/google_cloud_vertexai_rerank.ipynb

@@ -546,7 +546,7 @@
     "id": "ud_cnGszb1i9"
    },
    "source": [
-    "Let's inspect a couple of reranked documents. We observe that the retriever still returns the relevant Langchain type [documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) but as part of the metadata field, we also recieve the `relevance_score` from the Ranking API."
+    "Let's inspect a couple of reranked documents. We observe that the retriever still returns the relevant Langchain type [documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) but as part of the metadata field, we also receive the `relevance_score` from the Ranking API."
    ]
   },
   {

docs/docs/integrations/llms/lmformatenforcer_experimental.ipynb

@@ -221,7 +221,7 @@
    "source": [
     "## JSONFormer LLM Wrapper\n",
     "\n",
-    "Let's try that again, now providing a the Action input's JSON Schema to the model."
+    "Let's try that again, now providing the Action input's JSON Schema to the model."
    ]
   },
   {

docs/docs/integrations/providers/ads4gpts.mdx

@@ -0,0 +1,82 @@
+# ADS4GPTs
+
+> [ADS4GPTs](https://www.ads4gpts.com/) is building the open monetization backbone of the AI-Native internet. It helps AI applications monetize through advertising with a UX and Privacy first approach. 
+
+## Installation and Setup
+
+### Using pip
+You can install the package directly from PyPI:
+
+```bash
+pip install ads4gpts-langchain
+```
+
+### From Source
+Alternatively, install from source:
+
+```bash
+git clone https://github.com/ADS4GPTs/ads4gpts.git
+cd ads4gpts/libs/python-sdk/ads4gpts-langchain
+pip install .
+```
+
+## Prerequisites
+
+- Python 3.11+
+- ADS4GPTs API Key ([Obtain API Key](https://www.ads4gpts.com))
+
+## Environment Variables
+Set the following environment variables for API authentication:
+
+```bash
+export ADS4GPTS_API_KEY='your-ads4gpts-api-key'
+```
+
+Alternatively, API keys can be passed directly when initializing classes or stored in a `.env` file.
+
+## Tools
+
+ADS4GPTs provides two main tools for monetization:
+
+### Ads4gptsInlineSponsoredResponseTool
+This tool fetches native, sponsored responses that can be seamlessly integrated within your AI application's outputs.
+
+```python
+from ads4gpts_langchain import Ads4gptsInlineSponsoredResponseTool
+```
+
+### Ads4gptsSuggestedPromptTool
+Generates sponsored prompt suggestions to enhance user engagement and provide monetization opportunities.
+
+```python
+from ads4gpts_langchain import Ads4gptsSuggestedPromptTool
+```
+### Ads4gptsInlineConversationalTool
+Delivers conversational sponsored content that naturally fits within chat interfaces and dialogs.
+
+```python
+from ads4gpts_langchain import Ads4gptsInlineConversationalTool
+```
+
+### Ads4gptsInlineBannerTool
+Provides inline banner advertisements that can be displayed within your AI application's response.
+
+```python
+from ads4gpts_langchain import Ads4gptsInlineBannerTool
+```
+
+### Ads4gptsSuggestedBannerTool
+Generates banner advertisement suggestions that can be presented to users as recommended content.
+
+```python
+from ads4gpts_langchain import Ads4gptsSuggestedBannerTool
+```
+
+## Toolkit
+
+The `Ads4gptsToolkit` combines these tools for convenient access in LangChain applications.
+
+```python
+from ads4gpts_langchain import Ads4gptsToolkit
+```
+

docs/docs/integrations/providers/apify.mdx

@@ -29,7 +29,7 @@ You can use the `ApifyActorsTool` to use Apify Actors with agents.
 from langchain_apify import ApifyActorsTool
 ```
 
-See [this notebook](/docs/integrations/tools/apify_actors) for example usage.
+See [this notebook](/docs/integrations/tools/apify_actors) for example usage and a full example of a tool-calling agent with LangGraph in the [Apify LangGraph agent Actor template](https://apify.com/templates/python-langgraph).
 
 For more information on how to use this tool, visit [the Apify integration documentation](https://docs.apify.com/platform/integrations/langgraph).
 

docs/docs/integrations/providers/contextual.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contextual AI\n",
+    "\n",
+    "Contextual AI is a platform that offers state-of-the-art Retrieval-Augmented Generation (RAG) technology for enterprise applications. Our platformant models helps innovative teams build production-ready AI applications that can process millions of pages of documents with exceptional accuracy.\n",
+    "\n",
+    "## Grounded Language Model (GLM)\n",
+    "\n",
+    "The Grounded Language Model (GLM) is specifically engineered to minimize hallucinations in RAG and agentic applications. The GLM achieves:\n",
+    "\n",
+    "- State-of-the-art performance on the FACTS benchmark\n",
+    "- Responses strictly grounded in provided knowledge sources\n",
+    "\n",
+    "## Using Contextual AI with LangChain\n",
+    "\n",
+    "See details [here](/docs/integrations/chat/contextual).\n",
+    "\n",
+    "This integration allows you to easily incorporate Contextual AI's GLM into your LangChain workflows. Whether you're building applications for regulated industries or security-conscious environments, Contextual AI provides the grounded and reliable responses your use cases demand.\n",
+    "\n",
+    "Get started with a free trial today and experience the most grounded language model for enterprise AI applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to the information available, there are two types of cats in the world:\n",
+      "\n",
+      "1. Good cats\n",
+      "2. Best cats\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "from langchain_contextual import ChatContextual\n",
+    "\n",
+    "# Set credentials\n",
+    "if not os.getenv(\"CONTEXTUAL_AI_API_KEY\"):\n",
+    "    os.environ[\"CONTEXTUAL_AI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your Contextual API key: \"\n",
+    "    )\n",
+    "\n",
+    "# intialize Contextual llm\n",
+    "llm = ChatContextual(\n",
+    "    model=\"v1\",\n",
+    "    api_key=\"\",\n",
+    ")\n",
+    "# include a system prompt (optional)\n",
+    "system_prompt = \"You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability.\"\n",
+    "\n",
+    "# provide your own knowledge from your knowledge-base here in an array of string\n",
+    "knowledge = [\n",
+    "    \"There are 2 types of dogs in the world: good dogs and best dogs.\",\n",
+    "    \"There are 2 types of cats in the world: good cats and best cats.\",\n",
+    "]\n",
+    "\n",
+    "# create your message\n",
+    "messages = [\n",
+    "    (\"human\", \"What type of cats are there in the world and what are the types?\"),\n",
+    "]\n",
+    "\n",
+    "# invoke the GLM by providing the knowledge strings, optional system prompt\n",
+    "# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument\n",
+    "ai_msg = llm.invoke(\n",
+    "    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True\n",
+    ")\n",
+    "\n",
+    "print(ai_msg.content)"
+   ]
+  }
+ ],
+ "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/gitlab.mdx

@@ -27,5 +27,5 @@ from langchain_community.agent_toolkits.gitlab.toolkit import GitLabToolkit
 Tool for interacting with the GitLab API.
 
 ```python
-from langchain_community.tools.github.tool import GitHubAction
+from langchain_community.tools.gitlab.tool import GitLabAction
 ```

docs/docs/integrations/providers/kinetica.mdx

@@ -20,7 +20,7 @@ from langchain_community.chat_models.kinetica import ChatKinetica
 The Kinetca vectorstore wrapper leverages Kinetica's native support for [vector
 similarity search](https://docs.kinetica.com/7.2/vector_search/).
 
-See [Kinetica Vectorsore API](/docs/integrations/vectorstores/kinetica) for usage.
+See [Kinetica Vectorstore API](/docs/integrations/vectorstores/kinetica) for usage.
 
 ```python
 from langchain_community.vectorstores import Kinetica
@@ -28,8 +28,8 @@ from langchain_community.vectorstores import Kinetica
 
 ## Document Loader
 
-The Kinetica Document loader can be used to load LangChain Documents from the
-Kinetica database.
+The Kinetica Document loader can be used to load LangChain [Documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) from the
+[Kinetica](https://www.kinetica.com/) database.
 
 See [Kinetica Document Loader](/docs/integrations/document_loaders/kinetica) for usage
 

docs/docs/integrations/providers/opengradient.ipynb

@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: OpenGradient\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# OpenGradient\n",
+    "[OpenGradient](https://www.opengradient.ai/) is a decentralized AI computing network enabling globally accessible, permissionless, and verifiable ML model inference.\n",
+    "\n",
+    "The OpenGradient langchain package currently offers a toolkit that allows developers to build their own custom ML inference tools for models on the OpenGradient network. This was previously a challenge because of the context-window polluting nature of large model parameters -- imagine having to give your agent a 200x200 array of floating-point data!\n",
+    "\n",
+    "The toolkit solves this problem by encapsulating all data processing logic within the tool definition itself. This approach keeps the agent's context window clean while giving developers complete flexibility to implement custom data processing and live-data retrieval for their ML models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup\n",
+    "Ensure that you have an OpenGradient API key in order to access the OpenGradient network. If you already have an API key, simply set the environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!export OPENGRADIENT_PRIVATE_KEY=\"your-api-key\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you need to set up a new API key, download the opengradient SDK and follow the instructions to initialize a new configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install opengradient\n",
+    "!opengradient config init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have set up your API key, install the langchain-opengradient package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "powershell"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install -U langchain-opengradient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OpenGradient Toolkit\n",
+    "The OpenGradientToolkit empowers developers to create specialized tools based on [ML models](https://hub.opengradient.ai/models) and [workflows](https://docs.opengradient.ai/developers/sdk/ml_workflows.html) deployed on the OpenGradient decentralized network. This integration enables LangChain agents to access powerful ML capabilities while maintaining efficient context usage.\n",
+    "\n",
+    "### Key Benefits\n",
+    "* 🔄 Real-time data integration - Process live data feeds within your tools\n",
+    "\n",
+    "* 🎯 Dynamic processing - Custom data pipelines that adapt to specific agent inputs\n",
+    "\n",
+    "* 🧠 Context efficiency - Handle complex ML operations without flooding your context window\n",
+    "\n",
+    "* 🔌 Seamless deployment - Easy integration with models already on the OpenGradient network\n",
+    "\n",
+    "* 🔧 Full customization - Create and deploy your own specific models through the [OpenGradient SDK](https://docs.opengradient.ai/developers/sdk/model_management.html), then build custom tools from them\n",
+    "\n",
+    "* 🔐 Verifiable inference - All inferences run on the decentralized OpenGradient network, allowing users to choose various flavors of security such as ZKML and TEE for trustless, verifiable model execution\n",
+    "\n",
+    "For detailed examples and implementation guides, check out our [comprehensive tutorial](/docs/integrations/tools/opengradient_toolkit.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/permit.mdx

@@ -0,0 +1,31 @@
+# Permit
+
+[Permit.io](https://permit.io/) offers fine-grained access control and policy
+enforcement. With LangChain, you can integrate Permit checks to ensure only authorized
+users can access or retrieve data in your LLM applications.
+
+## Installation and Setup
+
+```bash
+pip install langchain-permit
+pip install permit
+```
+
+Set environment variables for your Permit PDP and credentials:
+
+```python
+export PERMIT_API_KEY="your_permit_api_key"
+export PERMIT_PDP_URL="http://localhost:7766"   # or your real PDP endpoint
+```
+
+Make sure your PDP is running and configured. See
+[Permit Docs](https://docs.permit.io/sdk/python/quickstart-python/#2-setup-your-pdp-policy-decision-point-container)
+for policy setup.
+
+## Tools
+
+See detail on available tools [here](/docs/integrations/tools/permit).
+
+## Retrievers
+
+See detail on available retrievers [here](/docs/integrations/retrievers/permit).

docs/docs/integrations/providers/prolog.md

@@ -4,7 +4,7 @@ SWI-Prolog offers a comprehensive free Prolog environment.
 
 ## Installation and Setup
 
-Install lanchain-prolog using pip:
+Once SWI-Prolog has been installed, install lanchain-prolog using pip:
 ```bash
 pip install langchain-prolog
 ```

docs/docs/integrations/providers/pymupdf4llm.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyMuPDF4LLM\n",
+    "\n",
+    "[PyMuPDF4LLM](https://pymupdf.readthedocs.io/en/latest/pymupdf4llm) is aimed to make it easier to extract PDF content in Markdown format, needed for LLM & RAG applications.\n",
+    "\n",
+    "[langchain-pymupdf4llm](https://github.com/lakinduboteju/langchain-pymupdf4llm) integrates PyMuPDF4LLM to LangChain as a Document Loader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-pymupdf4llm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_pymupdf4llm import PyMuPDF4LLMLoader, PyMuPDF4LLMParser"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/tableau.mdx

@@ -0,0 +1,15 @@
+# Tableau
+
+[Tableau](https://www.tableau.com/) is an analytics platform that enables anyone to
+see and understand data. 
+
+
+## Installation and Setup
+
+```bash
+pip install langchain-tableau
+```
+
+## Tools
+
+See detail on available tools [here](/docs/integrations/tools/tableau).

docs/docs/integrations/providers/taiga.mdx

@@ -0,0 +1,49 @@
+# Taiga
+
+> [Taiga](https://docs.taiga.io/) is an open-source project management platform designed for agile teams, offering features like Kanban, Scrum, and issue tracking.
+
+## Installation and Setup
+
+Install the `langchain-taiga` package:
+
+```bash
+pip install langchain-taiga
+```
+
+You must provide a logins via environment variable so the tools can authenticate.
+
+```bash
+export TAIGA_URL="https://taiga.xyz.org/"
+export TAIGA_API_URL="https://taiga.xyz.org/"
+export TAIGA_USERNAME="username"
+export TAIGA_PASSWORD="pw"
+export OPENAI_API_KEY="OPENAI_API_KEY"
+```
+
+
+---
+
+## Tools
+
+See a [usage example](/docs/integrations/tools/taiga)
+
+---
+
+## Toolkit
+
+`TaigaToolkit` groups multiple Taiga-related tools into a single interface.
+
+```python
+from langchain_taiga.toolkits import TaigaToolkit
+
+toolkit = TaigaToolkit()
+tools = toolkit.get_tools()
+
+```
+
+---
+
+## Future Integrations
+
+
+Check the [Taiga Developer Docs](https://docs.taiga.io/) for more information, and watch for updates or advanced usage examples in the [langchain_taiga GitHub repo](https://github.com/Shikenso-Analytics/langchain-taiga).

docs/docs/integrations/providers/valthera.mdx

@@ -0,0 +1,63 @@
+# Valthera
+
+> [Valthera](https://github.com/valthera/valthera) is an open-source framework that empowers LLM Agents to drive meaningful, context-aware user engagement. It evaluates user motivation and ability in real time, ensuring that notifications and actions are triggered only when users are most receptive.
+> 
+> **langchain-valthera** integrates Valthera with LangChain, enabling developers to build smarter, behavior-driven engagement systems that deliver personalized interactions.
+
+## Installation and Setup
+
+### Install langchain-valthera
+
+Install the LangChain Valthera package via pip:
+
+```bash
+pip install -U langchain-valthera
+```
+
+Import the ValtheraTool:
+
+```python
+from langchain_valthera.tools import ValtheraTool
+```
+
+### Example: Initializing the ValtheraTool for LangChain
+
+This example shows how to initialize the ValtheraTool using a `DataAggregator` and configuration for motivation and ability scoring.
+
+```python
+import os
+from langchain_openai import ChatOpenAI
+from valthera.aggregator import DataAggregator
+from mocks import hubspot, posthog, snowflake  # Replace these with your actual connector implementations
+from langchain_valthera.tools import ValtheraTool
+
+# Initialize the DataAggregator with your data connectors
+data_aggregator = DataAggregator(
+    connectors={
+        "hubspot": hubspot(),
+        "posthog": posthog(),
+        "app_db": snowflake()
+    }
+)
+
+# Initialize the ValtheraTool with your scoring configurations
+valthera_tool = ValtheraTool(
+    data_aggregator=data_aggregator,
+    motivation_config=[
+        {"key": "hubspot_lead_score", "weight": 0.30, "transform": lambda x: min(x, 100) / 100.0},
+        {"key": "posthog_events_count_past_30days", "weight": 0.30, "transform": lambda x: min(x, 50) / 50.0},
+        {"key": "hubspot_marketing_emails_opened", "weight": 0.20, "transform": lambda x: min(x / 10.0, 1.0)},
+        {"key": "posthog_session_count", "weight": 0.20, "transform": lambda x: min(x / 5.0, 1.0)}
+    ],
+    ability_config=[
+        {"key": "posthog_onboarding_steps_completed", "weight": 0.30, "transform": lambda x: min(x / 5.0, 1.0)},
+        {"key": "posthog_session_count", "weight": 0.30, "transform": lambda x: min(x / 10.0, 1.0)},
+        {"key": "behavior_complexity", "weight": 0.40, "transform": lambda x: 1 - (min(x, 5) / 5.0)}
+    ]
+)
+
+print("✅ ValtheraTool successfully initialized for LangChain integration!")
+```
+
+
+The langchain-valthera integration allows you to assess user behavior and decide on the best course of action for engagement, ensuring that interactions are both timely and relevant within your LangChain applications.

docs/docs/integrations/providers/writer.mdx

@@ -1,16 +1,55 @@
-# Writer
+---
+keywords: [writer]
+---
 
-This page covers how to use the Writer ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
+# Writer, Inc.
+
+All functionality related to Writer
+
+
+>This page covers how to use the [Writer](https://writer.com/) ecosystem within LangChain. For further information see Writer [docs](https://dev.writer.com/home/introduction).
+>[Palmyra](https://writer.com/blog/palmyra/) is a Large Language Model (LLM) developed by `Writer, Inc`.
+>
+>The [Writer API](https://dev.writer.com/api-guides/introduction) is powered by a diverse set of Palmyra sub-models with different capabilities and price points.
 
 ## Installation and Setup
-- Get an Writer api key and set it as an environment variable (`WRITER_API_KEY`)
 
-## Wrappers
+Install the integration package with
+```bash
+pip install langchain-writer
+```
 
-### LLM
+Get an Writer API key and set it as an environment variable (`WRITER_API_KEY`)
+
+## Chat model
 
-There exists an Writer LLM wrapper, which you can access with 
 ```python
-from langchain_community.llms import Writer
-```
+from langchain_writer import ChatWriter
+```
+
+## PDF Parser
+
+
+```python
+from langchain_writer.pdf_parser import PDFParser
+```
+
+## Text splitter
+
+```python
+from langchain_writer.text_splitter import WriterTextSplitter
+```
+
+## Tools calling
+
+### Functions
+
+Support of basic function calls defined via dicts, Pydantic, python functions etc.
+
+### Graphs
+
+```python
+from langchain_writer.tools import GraphTool
+```
+
+Writer-specific remotely invoking tool

docs/docs/integrations/retrievers/permit.ipynb

@@ -0,0 +1,330 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Permit\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# PermitRetriever\n",
+    "\n",
+    "Permit is an access control platform that provides fine-grained, real-time permission management using various models such as RBAC, ABAC, and ReBAC. It enables organizations to enforce dynamic policies across their applications, ensuring that only authorized users can access specific resources.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "This notebook illustrates how to integrate [Permit.io](https://permit.io/) permissions into LangChain retrievers.\n",
+    "\n",
+    "We provide two custom retrievers:\n",
+    "\n",
+    "- PermitSelfQueryRetriever – Uses a self-query approach to parse the user’s natural-language prompt, fetch the user’s permitted resource IDs from Permit, and apply that filter automatically in a vector store search. \n",
+    " \n",
+    "- PermitEnsembleRetriever – Combines multiple underlying retrievers (e.g., BM25 + Vector) via LangChain’s EnsembleRetriever, then filters the merged results with Permit.io.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Install the package with the command:\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-permit\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "```bash\n",
+    "pip install langchain-permit\n",
+    "```\n",
+    "\n",
+    "#### Environment Variables\n",
+    "\n",
+    "```bash\n",
+    "PERMIT_API_KEY=your_api_key\n",
+    "PERMIT_PDP_URL= # or your real deployment\n",
+    "OPENAI_API_KEY=sk-...\n",
+    "```\n",
+    "- A running Permit PDP. See [Permit docs](https://docs.permit.io/) for details on setting up your policy and container.\n",
+    "- A vector store or multiple retrievers that we can wrap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-permit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "### PermitSelfQueryRetriever\n",
+    "\n",
+    "#### Basic Explanation\n",
+    "\n",
+    "1. Retrieves permitted document IDs from Permit.  \n",
+    "\n",
+    "2. Uses an LLM to parse your query and build a “structured filter,” ensuring only docs with those permitted IDs are considered.\n",
+    "\n",
+    "#### Basic Usage\n",
+    "\n",
+    "```python\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_permit.retrievers import PermitSelfQueryRetriever\n",
+    "\n",
+    "# Step 1: Create / load some documents and build a vector store\n",
+    "docs = [...]\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "vectorstore = FAISS.from_documents(docs, embeddings)\n",
+    "\n",
+    "# Step 2: Initialize the retriever\n",
+    "retriever = PermitSelfQueryRetriever(\n",
+    "    api_key=\"...\",\n",
+    "    pdp_url=\"...\",\n",
+    "    user={\"key\": \"user-123\"},\n",
+    "    resource_type=\"document\",\n",
+    "    action=\"read\",\n",
+    "    llm=...,                # Typically a ChatOpenAI or other LLM\n",
+    "    vectorstore=vectorstore,\n",
+    "    enable_limit=True,      # optional\n",
+    ")\n",
+    "\n",
+    "# Step 3: Query\n",
+    "query = \"Give me docs about cats\"\n",
+    "results = retriever.get_relevant_documents(query)\n",
+    "for doc in results:\n",
+    "    print(doc.metadata.get(\"id\"), doc.page_content)\n",
+    "```\n",
+    "\n",
+    "### PermitEnsembleRetriever\n",
+    "\n",
+    "#### Basic Explanation\n",
+    "\n",
+    "1. Uses LangChain’s EnsembleRetriever to gather documents from multiple sub-retrievers (e.g., vector-based, BM25, etc.).\n",
+    "2. After retrieving documents, it calls filter_objects on Permit to eliminate any docs the user isn’t allowed to see.\n",
+    "\n",
+    "#### Basic Usage\n",
+    "\n",
+    "```python\n",
+    "from langchain_community.retrievers import BM25Retriever\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_permit.retrievers import PermitEnsembleRetriever\n",
+    "\n",
+    "# Suppose we have two child retrievers: bm25_retriever, vector_retriever\n",
+    "...\n",
+    "ensemble_retriever = PermitEnsembleRetriever(\n",
+    "    api_key=\"...\",\n",
+    "    pdp_url=\"...\",\n",
+    "    user=\"user_abc\",\n",
+    "    action=\"read\",\n",
+    "    resource_type=\"document\",\n",
+    "    retrievers=[bm25_retriever, vector_retriever],\n",
+    "    weights=None\n",
+    ")\n",
+    "\n",
+    "docs = ensemble_retriever.get_relevant_documents(\"Query about cats\")\n",
+    "for doc in docs:\n",
+    "    print(doc.metadata.get(\"id\"), doc.page_content)\n",
+    "```\n",
+    "\n",
+    "### Demo Scripts\n",
+    "\n",
+    "For more complete demos, check out the `/langchain_permit/examples/demo_scripts` folder:\n",
+    "\n",
+    "1. demo_self_query.py – Demonstrates PermitSelfQueryRetriever.\n",
+    "2. demo_ensemble.py – Demonstrates PermitEnsembleRetriever.\n",
+    "\n",
+    "Each script shows how to build or load documents, configure Permit, and run queries.\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "With these custom retrievers, you can seamlessly integrate Permit.io’s permission checks into LangChain’s retrieval workflow. You can keep your application’s vector search logic while ensuring only authorized documents are returned.\n",
+    "\n",
+    "For more details on setting up Permit policies, see the official Permit docs. If you want to combine these with other tools (like JWT validation or a broader RAG pipeline), check out our docs/tools.ipynb in the examples folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_permit import PermitRetriever\n",
+    "\n",
+    "retriever = PermitRetriever(\n",
+    "    # ...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+   "metadata": {},
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c70ae8ab",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"...\"\n",
+    "\n",
+    "retriever.invoke(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+   "metadata": {},
+   "source": [
+    "## Use within a chain\n",
+    "\n",
+    "Like other retrievers, PermitRetriever can be incorporated into LLM applications via [chains](https://docs.permit.io/).\n",
+    "\n",
+    "We will need a LLM or chat model:\n",
+    "\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" />"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25b647a3-f8f2-4541-a289-7a241e43f9df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"\"\"Answer the question based only on the context provided.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "Question: {question}\"\"\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "chain = (\n",
+    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d47c37dd-5c11-416c-a3b6-bec413cd70e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain.invoke(\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PermitRetriever features and configurations head to the [Repo](https://github.com/permitio/langchain-permit/tree/master/langchain_permit/examples/demo_scripts)."
+   ]
+  }
+ ],
+ "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/splitters/writer_text_splitter.ipynb

@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "db23d51760310705",
+   "metadata": {},
+   "source": [
+    "# Writer Text Splitter\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Writer's [text splitter](/docs/concepts/text_splitters/).\n",
+    "\n",
+    "Writer's [context-aware splitting endpoint](https://dev.writer.com/api-guides/tools#context-aware-text-splitting) provides intelligent text splitting capabilities for long documents (up to 4000 words). Unlike simple character-based splitting, it preserves the semantic meaning and context between chunks, making it ideal for processing long-form content while maintaining coherence. In `langchain-writer`, we provide usage of Writer's context-aware splitting endpoint as a LangChain text splitter.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "| Class                                                                                                                                    | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [WriterTextSplitter](https://github.com/writer/langchain-writer/blob/main/langchain_writer/text_splitter.py#L11) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f08d23df5dc127",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The `WriterTextSplitter` is available in the `langchain-writer` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a8d653f15b7ee32d",
+   "metadata": {},
+   "source": "%pip install --quiet -U langchain-writer",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b9709c26797edf",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to generate an API key (you can follow this [Quickstart](https://dev.writer.com/api-guides/quickstart)). Then, set the WRITER_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2983e19c9d555e58",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92a22c77f03d43dc",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability. If you wish to do so, you can set the `LANGCHAIN_TRACING_V2` and `LANGCHAIN_API_KEY` environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "98d8422ecee77403",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ab78950a3da8ba",
+   "metadata": {},
+   "source": [
+    "### Instantiation\n",
+    "\n",
+    "Instantiate an instance of `WriterTextSplitter` with the `strategy` parameter set to one of the following:\n",
+    "\n",
+    "- `llm_split`: Uses language model for precise semantic splitting\n",
+    "- `fast_split`: Uses heuristic-based approach for quick splitting\n",
+    "- `hybrid_split`: Combines both approaches\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "787b3ba8af32533f",
+   "metadata": {},
+   "source": [
+    "from langchain_writer.text_splitter import WriterTextSplitter\n",
+    "\n",
+    "splitter = WriterTextSplitter(strategy=\"fast_split\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d91c6f752fd31cee",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "The `WriterTextSplitter` can be used synchronously or asynchronously.\n",
+    "\n",
+    "### Synchronous usage\n",
+    "To use the `WriterTextSplitter` synchronously, call the `split_text` method with the text you want to split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d1a24b81a8a96f09",
+   "metadata": {},
+   "source": [
+    "text = \"\"\"Reeeeeeeeeeeeeeeeeeeeeaally long text you want to divide into smaller chunks. For example you can add a poem multiple times:\n",
+    "Two roads diverged in a yellow wood,\n",
+    "And sorry I could not travel both\n",
+    "And be one traveler, long I stood\n",
+    "And looked down one as far as I could\n",
+    "To where it bent in the undergrowth;\n",
+    "\n",
+    "Then took the other, as just as fair,\n",
+    "And having perhaps the better claim,\n",
+    "Because it was grassy and wanted wear;\n",
+    "Though as for that the passing there\n",
+    "Had worn them really about the same,\n",
+    "\n",
+    "And both that morning equally lay\n",
+    "In leaves no step had trodden black.\n",
+    "Oh, I kept the first for another day!\n",
+    "Yet knowing how way leads on to way,\n",
+    "I doubted if I should ever come back.\n",
+    "\n",
+    "I shall be telling this with a sigh\n",
+    "Somewhere ages and ages hence:\n",
+    "Two roads diverged in a wood, and I—\n",
+    "I took the one less traveled by,\n",
+    "And that has made all the difference.\n",
+    "\n",
+    "Two roads diverged in a yellow wood,\n",
+    "And sorry I could not travel both\n",
+    "And be one traveler, long I stood\n",
+    "And looked down one as far as I could\n",
+    "To where it bent in the undergrowth;\n",
+    "\n",
+    "Then took the other, as just as fair,\n",
+    "And having perhaps the better claim,\n",
+    "Because it was grassy and wanted wear;\n",
+    "Though as for that the passing there\n",
+    "Had worn them really about the same,\n",
+    "\n",
+    "And both that morning equally lay\n",
+    "In leaves no step had trodden black.\n",
+    "Oh, I kept the first for another day!\n",
+    "Yet knowing how way leads on to way,\n",
+    "I doubted if I should ever come back.\n",
+    "\n",
+    "I shall be telling this with a sigh\n",
+    "Somewhere ages and ages hence:\n",
+    "Two roads diverged in a wood, and I—\n",
+    "I took the one less traveled by,\n",
+    "And that has made all the difference.\n",
+    "\n",
+    "Two roads diverged in a yellow wood,\n",
+    "And sorry I could not travel both\n",
+    "And be one traveler, long I stood\n",
+    "And looked down one as far as I could\n",
+    "To where it bent in the undergrowth;\n",
+    "\n",
+    "Then took the other, as just as fair,\n",
+    "And having perhaps the better claim,\n",
+    "Because it was grassy and wanted wear;\n",
+    "Though as for that the passing there\n",
+    "Had worn them really about the same,\n",
+    "\n",
+    "And both that morning equally lay\n",
+    "In leaves no step had trodden black.\n",
+    "Oh, I kept the first for another day!\n",
+    "Yet knowing how way leads on to way,\n",
+    "I doubted if I should ever come back.\n",
+    "\n",
+    "I shall be telling this with a sigh\n",
+    "Somewhere ages and ages hence:\n",
+    "Two roads diverged in a wood, and I—\n",
+    "I took the one less traveled by,\n",
+    "And that has made all the difference.\n",
+    "\"\"\"\n",
+    "\n",
+    "chunks = splitter.split_text(text)\n",
+    "chunks"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6d09fcf",
+   "metadata": {},
+   "source": [
+    "You can print the length of the chunks to see how many chunks were created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a470daa875d99006",
+   "metadata": {},
+   "source": [
+    "print(len(chunks))"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89c048c7d23807a",
+   "metadata": {},
+   "source": [
+    "### Asynchronous usage\n",
+    "To use the `WriterTextSplitter` asynchronously, call the `asplit_text` method with the text you want to split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e2f7fd52b7188c6c",
+   "metadata": {},
+   "source": [
+    "async_chunks = await splitter.asplit_text(text)\n",
+    "async_chunks"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb669ce7",
+   "metadata": {},
+   "source": [
+    "Print the length of the chunks to see how many chunks were created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a1439db14e687fa4",
+   "metadata": {},
+   "source": [
+    "print(len(async_chunks))"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab25a3bed8437a05",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "For detailed documentation of all `WriterTextSplitter` features and configurations head to the [API reference](https://python.langchain.com/api_reference/writer/text_splitter/langchain_writer.text_splitter.WriterTextSplitter.html#langchain_writer.text_splitter.WriterTextSplitter).\n",
+    "\n",
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/tools/ads4gpts.ipynb

@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ADS4GPTs\n",
+    "\n",
+    "Integrate AI native advertising into your Agentic application.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook outlines how to use the ADS4GPTs Tools and Toolkit in LangChain directly. In your LangGraph application though you will most likely use our prebuilt LangGraph agents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install ADS4GPTs Package\n",
+    "Install the ADS4GPTs package using pip."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install ADS4GPTs Package\n",
+    "# Install the ADS4GPTs package using pip\n",
+    "!pip install ads4gpts-langchain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up the environment variables for API authentication ([Obtain API Key](https://www.ads4gpts.com))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup Environment Variables\n",
+    "# Prompt the user to enter their ADS4GPTs API key securely\n",
+    "if not os.environ.get(\"ADS4GPTS_API_KEY\"):\n",
+    "    os.environ[\"ADS4GPTS_API_KEY\"] = getpass(\"Enter your ADS4GPTS API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the necessary libraries, including ADS4GPTs tools and toolkit.\n",
+    "\n",
+    "Initialize the ADS4GPTs tools such as Ads4gptsInlineSponsoredResponseTool. We are going to work with one tool because the process is the same for every other tool we provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import Required Libraries\n",
+    "\n",
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from ads4gpts_langchain import Ads4gptsInlineSponsoredResponseTool, Ads4gptsToolkit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize ADS4GPTs Tools\n",
+    "# Initialize the Ads4gptsInlineSponsoredResponseTool\n",
+    "inline_sponsored_response_tool = Ads4gptsInlineSponsoredResponseTool(\n",
+    "    ads4gpts_api_key=os.environ[\"ADS4GPTS_API_KEY\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Toolkit Instantiation\n",
+    "Initialize the Ads4gptsToolkit with the required parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized tool: Ads4gptsInlineSponsoredResponseTool\n",
+      "Initialized tool: Ads4gptsSuggestedPromptTool\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Toolkit Initialization\n",
+    "# Initialize the Ads4gptsToolkit with the required parameters\n",
+    "toolkit = Ads4gptsToolkit(\n",
+    "    ads4gpts_api_key=os.environ[\"ADS4GPTS_API_KEY\"],\n",
+    ")\n",
+    "\n",
+    "# Retrieve tools from the toolkit\n",
+    "tools = toolkit.get_tools()\n",
+    "\n",
+    "# Print the initialized tools\n",
+    "for tool in tools:\n",
+    "    print(f\"Initialized tool: {tool.__class__.__name__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the ADS4GPTs tools with sample inputs and display the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inline Sponsored Response Result: {'ad_text': '<- Promoted Content ->\\n\\nLearn the sartorial ways and get your handmade tailored suit by the masters themselves with Bespoke Tailors. [Subscribe now](https://youtube.com/@bespoketailorsdubai?si=9iH587ujoWKkueFa)\\n\\n<->'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run ADS4GPTs Tools\n",
+    "# Sample input data for the tools\n",
+    "sample_input = {\n",
+    "    \"id\": \"test_id\",\n",
+    "    \"user_gender\": \"female\",\n",
+    "    \"user_age\": \"25-34\",\n",
+    "    \"user_persona\": \"test_persona\",\n",
+    "    \"ad_recommendation\": \"test_recommendation\",\n",
+    "    \"undesired_ads\": \"test_undesired_ads\",\n",
+    "    \"context\": \"test_context\",\n",
+    "    \"num_ads\": 1,\n",
+    "    \"style\": \"neutral\",\n",
+    "}\n",
+    "\n",
+    "# Run Ads4gptsInlineSponsoredResponseTool\n",
+    "inline_sponsored_response_result = inline_sponsored_response_tool._run(\n",
+    "    **sample_input, ad_format=\"INLINE_SPONSORED_RESPONSE\"\n",
+    ")\n",
+    "print(\"Inline Sponsored Response Result:\", inline_sponsored_response_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Async Run ADS4GPTs Tools\n",
+    "Run the ADS4GPTs tools asynchronously with sample inputs and display the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Async Inline Sponsored Response Result: {'ad_text': '<- Promoted Content ->\\n\\nGet the best tailoring content from Jonathan Farley. Learn to tie 100 knots and more! [Subscribe now](https://www.youtube.com/channel/UCx5hk4LN3p02jcUt3j_cexQ)\\n\\n<->'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "\n",
+    "# Define an async function to run the tools asynchronously\n",
+    "async def run_ads4gpts_tools_async():\n",
+    "    # Run Ads4gptsInlineSponsoredResponseTool asynchronously\n",
+    "    inline_sponsored_response_result = await inline_sponsored_response_tool._arun(\n",
+    "        **sample_input, ad_format=\"INLINE_SPONSORED_RESPONSE\"\n",
+    "    )\n",
+    "    print(\"Async Inline Sponsored Response Result:\", inline_sponsored_response_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Toolkit Invocation\n",
+    "Use the Ads4gptsToolkit to get and run tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result from Ads4gptsInlineSponsoredResponseTool: {'ad_text': '<- Promoted Content ->\\n\\nLearn the sartorial ways and get your handmade tailored suit by the masters themselves with Bespoke Tailors. [Subscribe now](https://youtube.com/@bespoketailorsdubai?si=9iH587ujoWKkueFa)\\n\\n<->'}\n",
+      "Async result from Ads4gptsInlineSponsoredResponseTool: {'ad_text': '<- Promoted Content ->\\n\\nGet the best tailoring content from Jonathan Farley. Learn to tie 100 knots and more! [Subscribe now](https://www.youtube.com/channel/UCx5hk4LN3p02jcUt3j_cexQ)\\n\\n<->'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample input data for the tools\n",
+    "sample_input = {\n",
+    "    \"id\": \"test_id\",\n",
+    "    \"user_gender\": \"female\",\n",
+    "    \"user_age\": \"25-34\",\n",
+    "    \"user_persona\": \"test_persona\",\n",
+    "    \"ad_recommendation\": \"test_recommendation\",\n",
+    "    \"undesired_ads\": \"test_undesired_ads\",\n",
+    "    \"context\": \"test_context\",\n",
+    "    \"num_ads\": 1,\n",
+    "    \"style\": \"neutral\",\n",
+    "}\n",
+    "\n",
+    "# Run one tool and print the result\n",
+    "tool = tools[0]\n",
+    "result = tool._run(**sample_input)\n",
+    "print(f\"Result from {tool.__class__.__name__}:\", result)\n",
+    "\n",
+    "\n",
+    "# Define an async function to run the tools asynchronously\n",
+    "async def run_toolkit_tools_async():\n",
+    "    result = await tool._arun(**sample_input)\n",
+    "    print(f\"Async result from {tool.__class__.__name__}:\", result)\n",
+    "\n",
+    "\n",
+    "# Execute the async function\n",
+    "await run_toolkit_tools_async()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your OPENAI_API_KEY API key: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tool call: content='' additional_kwargs={'tool_calls': [{'id': 'call_XLR5UjF8JhylVHvrk9mTjhj8', 'function': {'arguments': '{\"id\":\"unique_user_id_001\",\"user_gender\":\"male\",\"user_age\":\"18-24\",\"ad_recommendation\":\"Stylish and trendy clothing suitable for young men going out with friends.\",\"undesired_ads\":\"formal wear, women\\'s clothing, children\\'s clothing\",\"context\":\"A young man looking for clothing to go out with friends\",\"num_ads\":1,\"style\":\"youthful and trendy\",\"ad_format\":\"INLINE_SPONSORED_RESPONSE\"}', 'name': 'ads4gpts_inline_sponsored_response'}, 'type': 'function'}], 'refusal': None} response_metadata={'token_usage': {'completion_tokens': 106, 'prompt_tokens': 1070, 'total_tokens': 1176, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 1024}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb9dce56a8', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-e3e64b4b-4505-4a71-bf02-a8d77bb68eee-0' tool_calls=[{'name': 'ads4gpts_inline_sponsored_response', 'args': {'id': 'unique_user_id_001', 'user_gender': 'male', 'user_age': '18-24', 'ad_recommendation': 'Stylish and trendy clothing suitable for young men going out with friends.', 'undesired_ads': \"formal wear, women's clothing, children's clothing\", 'context': 'A young man looking for clothing to go out with friends', 'num_ads': 1, 'style': 'youthful and trendy', 'ad_format': 'INLINE_SPONSORED_RESPONSE'}, 'id': 'call_XLR5UjF8JhylVHvrk9mTjhj8', 'type': 'tool_call'}] usage_metadata={'input_tokens': 1070, 'output_tokens': 106, 'total_tokens': 1176, 'input_token_details': {'audio': 0, 'cache_read': 1024}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "openai_model = ChatOpenAI(model=\"gpt-4o\", openai_api_key=os.environ[\"OPENAI_API_KEY\"])\n",
+    "model = openai_model.bind_tools(tools)\n",
+    "model_response = model.invoke(\n",
+    "    \"Get me an ad for clothing. I am a young man looking to go out with friends.\"\n",
+    ")\n",
+    "print(\"Tool call:\", model_response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can learn more about ADS4GPTs and the tools at our [GitHub](https://github.com/ADS4GPTs/ads4gpts/tree/main)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ads4gpts-langraph-agent",
+   "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/tools/apify_actors.ipynb

@@ -1,256 +1,258 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_9MNj58sIkGN"
-   },
-   "source": [
-    "# Apify Actor\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    ">[Apify Actors](https://docs.apify.com/platform/actors) are cloud programs designed for a wide range of web scraping, crawling, and data extraction tasks. These actors facilitate automated data gathering from the web, enabling users to extract, process, and store information efficiently. Actors can be used to perform tasks like scraping e-commerce sites for product details, monitoring price changes, or gathering search engine results. They integrate seamlessly with [Apify Datasets](https://docs.apify.com/platform/storage/dataset), allowing the structured data collected by actors to be stored, managed, and exported in formats like JSON, CSV, or Excel for further analysis or use.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "OHLF9t9v9HCb"
-   },
-   "source": [
-    "## Setup\n",
-    "\n",
-    "This integration lives in the [langchain-apify](https://pypi.org/project/langchain-apify/) package. The package can be installed using pip.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "4DdGmBn5IbXz"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install langchain-apify"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rEAwonXqwggR"
-   },
-   "source": [
-    "### Prerequisites\n",
-    "\n",
-    "- **Apify account**: Register your free Apify account [here](https://console.apify.com/sign-up).\n",
-    "- **Apify API token**: Learn how to get your API token in the [Apify documentation](https://docs.apify.com/platform/integrations/api)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "9nJOl4MBMkcR"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"APIFY_API_TOKEN\"] = \"your-apify-api-token\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UfoQxAlCxR9q"
-   },
-   "source": [
-    "## Instantiation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qG9KtXtLM8i7"
-   },
-   "source": [
-    "Here we instantiate the `ApifyActorsTool` to be able to call [RAG Web Browser](https://apify.com/apify/rag-web-browser) Apify Actor. This Actor provides web browsing functionality for AI and LLM applications, similar to the web browsing feature in ChatGPT. Any Actor from the [Apify Store](https://apify.com/store) can be used in this way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "id": "cyxeTlPnM4Ya"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_apify import ApifyActorsTool\n",
-    "\n",
-    "tool = ApifyActorsTool(\"apify/rag-web-browser\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fGDLvDCqyKWO"
-   },
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "The `ApifyActorsTool` takes a single argument, which is `run_input` - a dictionary that is passed as a run input to the Actor. Run input schema documentation can be found in the input section of the Actor details page. See [RAG Web Browser input schema](https://apify.com/apify/rag-web-browser/input-schema).\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "nTWy6Hx1yk04"
-   },
-   "outputs": [],
-   "source": [
-    "tool.invoke({\"run_input\": {\"query\": \"what is apify?\", \"maxResults\": 2}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kQsa27hoO58S"
-   },
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can provide the created tool to an [agent](https://python.langchain.com/docs/tutorials/agents/). When asked to search for information, the agent will call the Apify Actor, which will search the web, and then retrieve the search results.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "YySvLskW72Y8"
-   },
-   "outputs": [],
-   "source": [
-    "%pip install langgraph langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "id": "QEDz07btO5Gi"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import ToolMessage\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
-    "tools = [tool]\n",
-    "graph = create_react_agent(model, tools=tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "XS1GEyNkQxGu",
-    "outputId": "195273d7-034c-425b-f3f9-95c0a9fb0c9e"
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "search for what is Apify\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  apify_actor_apify_rag-web-browser (call_27mjHLzDzwa5ZaHWCMH510lm)\n",
-      " Call ID: call_27mjHLzDzwa5ZaHWCMH510lm\n",
-      "  Args:\n",
-      "    run_input: {\"run_input\":{\"query\":\"Apify\",\"maxResults\":3,\"outputFormats\":[\"markdown\"]}}\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "Apify is a comprehensive platform for web scraping, browser automation, and data extraction. It offers a wide array of tools and services that cater to developers and businesses looking to extract data from websites efficiently and effectively. Here's an overview of Apify:\n",
-      "\n",
-      "1. **Ecosystem and Tools**:\n",
-      "   - Apify provides an ecosystem where developers can build, deploy, and publish data extraction and web automation tools called Actors.\n",
-      "   - The platform supports various use cases such as extracting data from social media platforms, conducting automated browser-based tasks, and more.\n",
-      "\n",
-      "2. **Offerings**:\n",
-      "   - Apify offers over 3,000 ready-made scraping tools and code templates.\n",
-      "   - Users can also build custom solutions or hire Apify's professional services for more tailored data extraction needs.\n",
-      "\n",
-      "3. **Technology and Integration**:\n",
-      "   - The platform supports integration with popular tools and services like Zapier, GitHub, Google Sheets, Pinecone, and more.\n",
-      "   - Apify supports open-source tools and technologies such as JavaScript, Python, Puppeteer, Playwright, Selenium, and its own Crawlee library for web crawling and browser automation.\n",
-      "\n",
-      "4. **Community and Learning**:\n",
-      "   - Apify hosts a community on Discord where developers can get help and share expertise.\n",
-      "   - It offers educational resources through the Web Scraping Academy to help users become proficient in data scraping and automation.\n",
-      "\n",
-      "5. **Enterprise Solutions**:\n",
-      "   - Apify provides enterprise-grade web data extraction solutions with high reliability, 99.95% uptime, and compliance with SOC2, GDPR, and CCPA standards.\n",
-      "\n",
-      "For more information, you can visit [Apify's official website](https://apify.com/) or their [GitHub page](https://github.com/apify) which contains their code repositories and further details about their projects.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_9MNj58sIkGN"
+      },
+      "source": [
+        "# Apify Actor\n",
+        "\n",
+        ">[Apify Actors](https://docs.apify.com/platform/actors) are cloud programs designed for a wide range of web scraping, crawling, and data extraction tasks. These actors facilitate automated data gathering from the web, enabling users to extract, process, and store information efficiently. Actors can be used to perform tasks like scraping e-commerce sites for product details, monitoring price changes, or gathering search engine results. They integrate seamlessly with [Apify Datasets](https://docs.apify.com/platform/storage/dataset), allowing the structured data collected by actors to be stored, managed, and exported in formats like JSON, CSV, or Excel for further analysis or use.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "This notebook walks you through using [Apify Actors](https://docs.apify.com/platform/actors) with LangChain to automate web scraping and data extraction. The `langchain-apify` package integrates Apify's cloud-based tools with LangChain agents, enabling efficient data collection and processing for AI applications.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OHLF9t9v9HCb"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "This integration lives in the [langchain-apify](https://pypi.org/project/langchain-apify/) package. The package can be installed using pip.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4DdGmBn5IbXz"
+      },
+      "outputs": [],
+      "source": [
+        "%pip install langchain-apify"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rEAwonXqwggR"
+      },
+      "source": [
+        "### Prerequisites\n",
+        "\n",
+        "- **Apify account**: Register your free Apify account [here](https://console.apify.com/sign-up).\n",
+        "- **Apify API token**: Learn how to get your API token in the [Apify documentation](https://docs.apify.com/platform/integrations/api)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9nJOl4MBMkcR"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"APIFY_API_TOKEN\"] = \"your-apify-api-token\"\n",
+        "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UfoQxAlCxR9q"
+      },
+      "source": [
+        "## Instantiation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qG9KtXtLM8i7"
+      },
+      "source": [
+        "Here we instantiate the `ApifyActorsTool` to be able to call [RAG Web Browser](https://apify.com/apify/rag-web-browser) Apify Actor. This Actor provides web browsing functionality for AI and LLM applications, similar to the web browsing feature in ChatGPT. Any Actor from the [Apify Store](https://apify.com/store) can be used in this way."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cyxeTlPnM4Ya"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_apify import ApifyActorsTool\n",
+        "\n",
+        "tool = ApifyActorsTool(\"apify/rag-web-browser\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fGDLvDCqyKWO"
+      },
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "The `ApifyActorsTool` takes a single argument, which is `run_input` - a dictionary that is passed as a run input to the Actor. Run input schema documentation can be found in the input section of the Actor details page. See [RAG Web Browser input schema](https://apify.com/apify/rag-web-browser/input-schema).\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nTWy6Hx1yk04"
+      },
+      "outputs": [],
+      "source": [
+        "tool.invoke({\"run_input\": {\"query\": \"what is apify?\", \"maxResults\": 2}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kQsa27hoO58S"
+      },
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can provide the created tool to an [agent](https://python.langchain.com/docs/tutorials/agents/). When asked to search for information, the agent will call the Apify Actor, which will search the web, and then retrieve the search results.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YySvLskW72Y8"
+      },
+      "outputs": [],
+      "source": [
+        "%pip install langgraph langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QEDz07btO5Gi"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_core.messages import ToolMessage\n",
+        "from langchain_openai import ChatOpenAI\n",
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "model = ChatOpenAI(model=\"gpt-4o\")\n",
+        "tools = [tool]\n",
+        "graph = create_react_agent(model, tools=tools)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XS1GEyNkQxGu",
+        "outputId": "195273d7-034c-425b-f3f9-95c0a9fb0c9e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "search for what is Apify\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  apify_actor_apify_rag-web-browser (call_27mjHLzDzwa5ZaHWCMH510lm)\n",
+            " Call ID: call_27mjHLzDzwa5ZaHWCMH510lm\n",
+            "  Args:\n",
+            "    run_input: {\"run_input\":{\"query\":\"Apify\",\"maxResults\":3,\"outputFormats\":[\"markdown\"]}}\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "Apify is a comprehensive platform for web scraping, browser automation, and data extraction. It offers a wide array of tools and services that cater to developers and businesses looking to extract data from websites efficiently and effectively. Here's an overview of Apify:\n",
+            "\n",
+            "1. **Ecosystem and Tools**:\n",
+            "   - Apify provides an ecosystem where developers can build, deploy, and publish data extraction and web automation tools called Actors.\n",
+            "   - The platform supports various use cases such as extracting data from social media platforms, conducting automated browser-based tasks, and more.\n",
+            "\n",
+            "2. **Offerings**:\n",
+            "   - Apify offers over 3,000 ready-made scraping tools and code templates.\n",
+            "   - Users can also build custom solutions or hire Apify's professional services for more tailored data extraction needs.\n",
+            "\n",
+            "3. **Technology and Integration**:\n",
+            "   - The platform supports integration with popular tools and services like Zapier, GitHub, Google Sheets, Pinecone, and more.\n",
+            "   - Apify supports open-source tools and technologies such as JavaScript, Python, Puppeteer, Playwright, Selenium, and its own Crawlee library for web crawling and browser automation.\n",
+            "\n",
+            "4. **Community and Learning**:\n",
+            "   - Apify hosts a community on Discord where developers can get help and share expertise.\n",
+            "   - It offers educational resources through the Web Scraping Academy to help users become proficient in data scraping and automation.\n",
+            "\n",
+            "5. **Enterprise Solutions**:\n",
+            "   - Apify provides enterprise-grade web data extraction solutions with high reliability, 99.95% uptime, and compliance with SOC2, GDPR, and CCPA standards.\n",
+            "\n",
+            "For more information, you can visit [Apify's official website](https://apify.com/) or their [GitHub page](https://github.com/apify) which contains their code repositories and further details about their projects.\n"
+          ]
+        }
+      ],
+      "source": [
+        "inputs = {\"messages\": [(\"user\", \"search for what is Apify\")]}\n",
+        "for s in graph.stream(inputs, stream_mode=\"values\"):\n",
+        "    message = s[\"messages\"][-1]\n",
+        "    # skip tool messages\n",
+        "    if isinstance(message, ToolMessage):\n",
+        "        continue\n",
+        "    message.pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WYXuQIQx8AvG"
+      },
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For more information on how to use this integration, see the [git repository](https://github.com/apify/langchain-apify) or the [Apify integration documentation](https://docs.apify.com/platform/integrations/langgraph)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f1NnMik78oib"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-   ],
-   "source": [
-    "inputs = {\"messages\": [(\"user\", \"search for what is Apify\")]}\n",
-    "for s in graph.stream(inputs, stream_mode=\"values\"):\n",
-    "    message = s[\"messages\"][-1]\n",
-    "    # skip tool messages\n",
-    "    if isinstance(message, ToolMessage):\n",
-    "        continue\n",
-    "    message.pretty_print()"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WYXuQIQx8AvG"
-   },
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For more information on how to use this integration, see the [git repository](https://github.com/apify/langchain-apify) or the [Apify integration documentation](https://docs.apify.com/platform/integrations/langgraph)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "f1NnMik78oib"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/docs/integrations/tools/opengradient_toolkit.ipynb

@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: OpenGradient\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# OpenGradientToolkit\n",
+    "\n",
+    "This notebook shows how to build tools using the OpenGradient toolkit. This toolkit gives users the ability to create custom tools based on models and workflows on the [OpenGradient network](https://www.opengradient.ai/).\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Ensure that you have an OpenGradient API key in order to access the OpenGradient network. If you already have an API key, simply set the environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f7303e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!export OPENGRADIENT_PRIVATE_KEY=\"your-api-key\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a7af45e",
+   "metadata": {},
+   "source": [
+    "If you need to set up a new API key, download the opengradient SDK and follow the instructions to initialize a new configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7777f1e",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install opengradient\n",
+    "!opengradient config init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This toolkit lives in the `langchain-opengradient` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-opengradient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our toolkit with the API key from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_opengradient import OpenGradientToolkit\n",
+    "\n",
+    "toolkit = OpenGradientToolkit(\n",
+    "    # Not required if you have already set the environment variable OPENGRADIENT_PRIVATE_KEY\n",
+    "    private_key=\"your-api-key\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad986625",
+   "metadata": {},
+   "source": [
+    "## Build your own tools\n",
+    "The OpenGradientToolkit offers two main methods for creating custom tools:\n",
+    "\n",
+    "### 1. Create a tool to run ML models\n",
+    "You can create tools that leverage ML models deployed on the [OpenGradient model hub](https://hub.opengradient.ai/). User-created models can be uploaded, inferenced, and shared to the model hub through the [OpenGradient SDK](https://docs.opengradient.ai/developers/sdk/model_management.html).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7a03746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import opengradient as og\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "# Example 1: Simple tool with no input schema\n",
+    "def price_data_provider():\n",
+    "    \"\"\"Function that provides input data to the model.\"\"\"\n",
+    "    return {\n",
+    "        \"open_high_low_close\": [\n",
+    "            [2535.79, 2535.79, 2505.37, 2515.36],\n",
+    "            [2515.37, 2516.37, 2497.27, 2506.94],\n",
+    "            [2506.94, 2515, 2506.35, 2508.77],\n",
+    "            [2508.77, 2519, 2507.55, 2518.79],\n",
+    "            [2518.79, 2522.1, 2513.79, 2517.92],\n",
+    "            [2517.92, 2521.4, 2514.65, 2518.13],\n",
+    "            [2518.13, 2525.4, 2517.2, 2522.6],\n",
+    "            [2522.59, 2528.81, 2519.49, 2526.12],\n",
+    "            [2526.12, 2530, 2524.11, 2529.99],\n",
+    "            [2529.99, 2530.66, 2525.29, 2526],\n",
+    "        ]\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def format_volatility(inference_result):\n",
+    "    \"\"\"Function that formats the model output.\"\"\"\n",
+    "    return format(float(inference_result.model_output[\"Y\"].item()), \".3%\")\n",
+    "\n",
+    "\n",
+    "# Create the tool\n",
+    "volatility_tool = toolkit.create_run_model_tool(\n",
+    "    model_cid=\"QmRhcpDXfYCKsimTmJYrAVM4Bbvck59Zb2onj3MHv9Kw5N\",\n",
+    "    tool_name=\"eth_volatility\",\n",
+    "    model_input_provider=price_data_provider,\n",
+    "    model_output_formatter=format_volatility,\n",
+    "    tool_description=\"Generates volatility measurement for ETH/USDT trading pair\",\n",
+    "    inference_mode=og.InferenceMode.VANILLA,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Example 2: Tool with input schema from the agent\n",
+    "class TokenInputSchema(BaseModel):\n",
+    "    token: str = Field(description=\"Token name (ethereum or bitcoin)\")\n",
+    "\n",
+    "\n",
+    "def token_data_provider(**inputs):\n",
+    "    \"\"\"Dynamic function that changes behavior based on agent input.\"\"\"\n",
+    "    token = inputs.get(\"token\")\n",
+    "    if token == \"bitcoin\":\n",
+    "        return {\"price_series\": [100001.1, 100013.2, 100149.2, 99998.1]}\n",
+    "    else:  # ethereum\n",
+    "        return {\"price_series\": [2010.1, 2012.3, 2020.1, 2019.2]}\n",
+    "\n",
+    "\n",
+    "# Create the tool with schema\n",
+    "token_tool = toolkit.create_run_model_tool(\n",
+    "    model_cid=\"QmZdSfHWGJyzBiB2K98egzu3MypPcv4R1ASypUxwZ1MFUG\",\n",
+    "    tool_name=\"token_volatility\",\n",
+    "    model_input_provider=token_data_provider,\n",
+    "    model_output_formatter=lambda x: format(float(x.model_output[\"std\"].item()), \".3%\"),\n",
+    "    tool_input_schema=TokenInputSchema,\n",
+    "    tool_description=\"Measures return volatility for a specified token\",\n",
+    ")\n",
+    "\n",
+    "# Add tools to the toolkit\n",
+    "toolkit.add_tool(volatility_tool)\n",
+    "toolkit.add_tool(token_tool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45627b99",
+   "metadata": {},
+   "source": [
+    "### 2. Create a tool to read workflow results\n",
+    "\n",
+    "Read workflows are scheduled inferences that regularly run models stored on smart-contracts with live oracle data. More information on these can be [found here](https://docs.opengradient.ai/developers/sdk/ml_workflows.html).\n",
+    "\n",
+    "You can create tools that read results from workflow smart contracts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "197cccbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a tool to read from a workflow\n",
+    "forecast_tool = toolkit.create_read_workflow_tool(\n",
+    "    workflow_contract_address=\"0x58826c6dc9A608238d9d57a65bDd50EcaE27FE99\",\n",
+    "    tool_name=\"ETH_Price_Forecast\",\n",
+    "    tool_description=\"Reads latest forecast for ETH price from deployed workflow\",\n",
+    "    output_formatter=lambda x: f\"Price change forecast: {format(float(x.numbers['regression_output'].item()), '.2%')}\",\n",
+    ")\n",
+    "\n",
+    "# Add the tool to the toolkit\n",
+    "toolkit.add_tool(forecast_tool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "\n",
+    "Use the built in `get_tools()` method to view a list of the available tools within the OpenGradient toolkit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = toolkit.get_tools()\n",
+    "\n",
+    "# View tools\n",
+    "for tool in tools:\n",
+    "    print(tool)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "Here's how to use your OpenGradient tools with a LangChain agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310bf18e-6c9a-4072-b86e-47bc1fcca29d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "# Initialize LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-4o\")\n",
+    "\n",
+    "# Create tools from the toolkit\n",
+    "tools = toolkit.get_tools()\n",
+    "\n",
+    "# Create agent\n",
+    "agent_executor = create_react_agent(llm, tools)\n",
+    "\n",
+    "# Example query for the agent\n",
+    "example_query = \"What's the current volatility of ETH?\"\n",
+    "\n",
+    "# Execute the agent\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bb2b716",
+   "metadata": {},
+   "source": [
+    "Here's a sample output of everything put together:\n",
+    "\n",
+    "```\n",
+    "================================ Human Message =================================\n",
+    "\n",
+    "What's the current volatility of ETH?\n",
+    "================================== Ai Message ==================================\n",
+    "Tool Calls:\n",
+    "  eth_volatility (chatcmpl-tool-d66ab9ee8f2c40e5a2634d90c7aeb17d)\n",
+    " Call ID: chatcmpl-tool-d66ab9ee8f2c40e5a2634d90c7aeb17d\n",
+    "  Args:\n",
+    "================================= Tool Message =================================\n",
+    "Name: eth_volatility\n",
+    "\n",
+    "0.038%\n",
+    "================================== Ai Message ==================================\n",
+    "\n",
+    "The current volatility of the ETH/USDT trading pair is 0.038%.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "268bc64a",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "See the [Github page](https://github.com/OpenGradient/og-langchain) for more detail."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/tools/permit.ipynb

@@ -0,0 +1,283 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "10238e62-3465-4973-9279-606cbb7ccf16",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Permit\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6f91f20",
+   "metadata": {},
+   "source": [
+    "# Permit\n",
+    "\n",
+    "Permit is an access control platform that provides fine-grained, real-time permission management using various models such as RBAC, ABAC, and ReBAC. It enables organizations to enforce dynamic policies across their applications, ensuring that only authorized users can access specific resources.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This package provides two Langchain tools for JWT validation and permission checking using Permit:\n",
+    "\n",
+    "* LangchainJWTValidationTool: Validates JWT tokens against a JWKS endpoint\n",
+    "\n",
+    "* LangchainPermissionsCheckTool: Checks user permissions using Permit\n",
+    "\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Set up the following environment variables:\n",
+    "\n",
+    "```bash\n",
+    "PERMIT_API_KEY=your_permit_api_key\n",
+    "JWKS_URL=your_jwks_endpoint_url\n",
+    "PERMIT_PDP_URL=your_permit_pdp_url  # Usually http://localhost:7766 for local development or your real deployment\n",
+    "```\n",
+    "\n",
+    "Make sure your PDP (Policy Decision Point) is running at PERMIT_PDP_URL.\n",
+    "See [Permit docs](https://docs.permit.io/concepts/pdp/overview/) for details on policy setup and how to launch the PDP container."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b15e9266",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "```bash\n",
+    "PERMIT_API_KEY=\n",
+    "JWKS_URL=your_jwks_endpoint_url # or your deployed url\n",
+    "PERMIT_PDP_URL=your_pdp_url # or your deployed url\n",
+    "TEST_JWT_TOKEN= # for quick test purposes\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "### JWT Validation Tool\n",
+    "The JWT Validation tool verifies JWT tokens against a JWKS (JSON Web Key Set) endpoint.\n",
+    "\n",
+    "```python\n",
+    "from langchain_permit.tools import LangchainJWTValidationTool\n",
+    "\n",
+    "# Initialize the tool\n",
+    "jwt_validator = LangchainJWTValidationTool(\n",
+    "    jwks_url=#your url endpoint\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "### Configuration Options\n",
+    "You can initialize the tool with either:\n",
+    "\n",
+    "* A JWKS URL\n",
+    "* Direct JWKS JSON data\n",
+    "* Environment variable (JWKS_URL)\n",
+    "\n",
+    "```python\n",
+    "# Using direct JWKS JSON\n",
+    "jwt_validator = LangchainJWTValidationTool(\n",
+    "    jwks_json={\n",
+    "        \"keys\": [\n",
+    "            {\n",
+    "                \"kid\": \"key-id\",\n",
+    "                \"kty\": \"RSA\",\n",
+    "                ...\n",
+    "            }\n",
+    "        ]\n",
+    "    }\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "### Permissions Check Tool\n",
+    "The Permissions Check tool integrates with Permit.io to verify user permissions against resources.\n",
+    "\n",
+    "```python\n",
+    "from permit import Permit\n",
+    "from langchain_permit.tools import LangchainPermissionsCheckTool\n",
+    "\n",
+    "# Initialize Permit client\n",
+    "permit_client = Permit(\n",
+    "    token=\"your_permit_api_key\",\n",
+    "    pdp=# Your PDP URL\n",
+    ")\n",
+    "\n",
+    "# Initialize the tool\n",
+    "permissions_checker = LangchainPermissionsCheckTool(\n",
+    "    permit=permit_client\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "This documentation demonstrates the key features and usage patterns of both tools."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74147a1a",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "### [Invoke directly with args](https://docs.permit.io/)\n",
+    "\n",
+    "### JWT Validation Tool\n",
+    "\n",
+    "```python\n",
+    "# Validate a token\n",
+    "async def validate_token():\n",
+    "    claims = await jwt_validator._arun(\n",
+    "        \"...\"  # Your JWT token\n",
+    "    )\n",
+    "    print(\"Validated Claims:\", claims)\n",
+    "```\n",
+    "\n",
+    "### Permissions Check Tool\n",
+    "\n",
+    "```python\n",
+    "# Check permissions\n",
+    "async def check_user_permission():\n",
+    "    result = await permissions_checker._arun(\n",
+    "        user={\n",
+    "            \"key\": \"user-123\",\n",
+    "            \"firstName\": \"John\"\n",
+    "        },\n",
+    "        action=\"read\",\n",
+    "        resource={\n",
+    "            \"type\": \"Document\",\n",
+    "            \"tenant\": \"default\"\n",
+    "        }\n",
+    "    )\n",
+    "    print(\"Permission granted:\", result)\n",
+    "```\n",
+    "\n",
+    "#### Input Formats\n",
+    "The permissions checker accepts different input formats:\n",
+    "\n",
+    "1. Simple string for user (converts to user key):\n",
+    "\n",
+    "```python\n",
+    "result = await permissions_checker._arun(\n",
+    "    user=\"user-123\",\n",
+    "    action=\"read\",\n",
+    "    resource=\"Document\"\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "2. Full user object:\n",
+    "\n",
+    "```python\n",
+    "result = await permissions_checker._arun(\n",
+    "    user={\n",
+    "        \"key\": \"user-123\",\n",
+    "        \"firstName\": \"John\",\n",
+    "        \"lastName\": \"Doe\",\n",
+    "        \"email\": \"john@example.com\",\n",
+    "        \"attributes\": {\"department\": \"IT\"}\n",
+    "    },\n",
+    "    action=\"read\",\n",
+    "    resource={\n",
+    "        \"type\": \"Document\",\n",
+    "        \"key\": \"doc-123\",\n",
+    "        \"tenant\": \"techcorp\",\n",
+    "        \"attributes\": {\"confidentiality\": \"high\"}\n",
+    "    }\n",
+    ")\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e73897",
+   "metadata": {},
+   "source": [
+    "### [Invoke with ToolCall](https://docs.permit.io/)\n",
+    "\n",
+    "(TODO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "- TODO: Add user question and run cells\n",
+    "\n",
+    "We can use our tool in a chain by first binding it to a [tool-calling model](https://docs.permit.io/) and then calling it:\n",
+    "\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" />\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1306ac4",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "### Additional Demo Scripts\n",
+    "\n",
+    "For fully runnable demos, check out the `/langchain_permit/examples/demo_scripts` folder in this [repository](https://github.com/permitio/langchain-permit). You’ll find:\n",
+    "\n",
+    "* demo_jwt_validation.py – A quick script showing how to validate JWTs using LangchainJWTValidationTool.\n",
+    "\n",
+    "* demo_permissions_check.py – A script that performs Permit.io permission checks using LangchainPermissionsCheckTool.\n",
+    "\n",
+    "Just run `python demo_jwt_validation.py` or `python demo_permissions_check.py` (after setting your environment variables) to see these tools in action."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ac8146c",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all Permit features and configurations head to the API reference: https://docs.permit.io/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv-311",
+   "language": "python",
+   "name": "poetry-venv-311"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/tools/prolog_tool.ipynb

@@ -86,33 +86,14 @@
    "execution_count": 3,
    "id": "68709aa2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#!pip install python-dotenv\n",
     "\n",
     "from dotenv import find_dotenv, load_dotenv\n",
     "\n",
-    "load_dotenv(find_dotenv(), override=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c171c1ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "load_dotenv(find_dotenv(), override=True)\n",
+    "\n",
     "#!pip install langchain-openai\n",
     "\n",
     "from langchain_core.messages import HumanMessage\n",
@@ -124,19 +105,37 @@
    "id": "9d23b2c5",
    "metadata": {},
    "source": [
-    "To use the tool, bind it to the LLM model and query the model:"
+    "To use the tool, bind it to the LLM model:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "3ce4479d",
    "metadata": {},
    "outputs": [],
    "source": [
     "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "llm_with_tools = llm.bind_tools([prolog_tool])\n",
-    "messages = [HumanMessage(\"Who are John's children?\")]\n",
+    "llm_with_tools = llm.bind_tools([prolog_tool])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74767c7c",
+   "metadata": {},
+   "source": [
+    "and then query the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "affb960e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"Who are John's children?\"\n",
+    "messages = [HumanMessage(query)]\n",
     "response = llm_with_tools.invoke(messages)"
    ]
   },
@@ -159,7 +158,7 @@
       "text/plain": [
        "{'name': 'family_query',\n",
        " 'args': {'men': 'john', 'women': None, 'child': None},\n",
-       " 'id': 'call_3VazWUstCGlY8zczi05TaduU',\n",
+       " 'id': 'call_gH8rWamYXITrkfvRP2s5pkbF',\n",
        " 'type': 'tool_call'}"
       ]
      },
@@ -208,7 +207,7 @@
     {
      "data": {
       "text/plain": [
-       "ToolMessage(content='[{\"Women\": \"bianca\", \"Child\": \"mary\"}, {\"Women\": \"bianca\", \"Child\": \"michael\"}]', name='family_query', tool_call_id='call_3VazWUstCGlY8zczi05TaduU')"
+       "ToolMessage(content='[{\"Women\": \"bianca\", \"Child\": \"mary\"}, {\"Women\": \"bianca\", \"Child\": \"michael\"}]', name='family_query', tool_call_id='call_gH8rWamYXITrkfvRP2s5pkbF')"
       ]
      },
      "execution_count": 8,
@@ -239,7 +238,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "John has two children: Mary and Michael. Their mother is Bianca.\n"
+      "John has two children: Mary and Michael, with Bianca as their mother.\n"
      ]
     }
    ],
@@ -269,28 +268,15 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b7bbfd79",
+   "id": "8957d420",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "#!pip install langgraph\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        (\"placeholder\", \"{agent_scratchpad}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "tools = [prolog_tool]\n",
-    "\n",
-    "agent = create_tool_calling_agent(llm, tools, prompt)\n",
-    "\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools)"
+    "agent_executor = create_react_agent(llm, [prolog_tool])"
    ]
   },
   {
@@ -310,7 +296,7 @@
    },
    "outputs": [],
    "source": [
-    "answer = agent_executor.invoke({\"input\": \"Who are John's children?\"})"
+    "messages = agent_executor.invoke({\"messages\": [(\"human\", query)]})"
    ]
   },
   {
@@ -318,7 +304,7 @@
    "id": "92e71ffa",
    "metadata": {},
    "source": [
-    "Then the agent recieves the tool response as part of the `\"agent_scratchpad`\" placeholder and generates the answer:"
+    "Then the agent receives​ the tool response and generates the answer:"
    ]
   },
   {
@@ -331,12 +317,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
       "John has two children: Mary and Michael, with Bianca as their mother.\n"
      ]
     }
    ],
    "source": [
-    "print(answer[\"output\"])"
+    "messages[\"messages\"][-1].pretty_print()"
    ]
   },
   {
@@ -346,17 +334,23 @@
    "source": [
     "## API reference\n",
     "\n",
-    "(TODO: update with API reference once built.)\n",
-    "\n",
-    "See https://github.com/apisani1/langchain-prolog/tree/main for detail."
+    "See https://langchain-prolog.readthedocs.io/en/latest/modules.html for detail."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a95afa82",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9 (test-env)",
+   "display_name": "Python 3.10 (test-again)",
    "language": "python",
-   "name": "test-env"
+   "name": "test-again"
   },
   "language_info": {
    "codemirror_mode": {
@@ -368,7 +362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.21"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/tableau.ipynb

@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f302499-eb05-4296-8716-950babc0f10e",
+   "metadata": {},
+   "source": [
+    "# Tableau\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with [Tableau](https://help.tableau.com/current/api/vizql-data-service/en-us/index.html). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d57b913-819e-4676-9f6e-3afe0a80030e",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "\n",
+    "Tableau's VizQL Data Service (aka VDS) provides developers with programmatic access to their Tableau Published Data Sources, allowing them to extend their business semantics for any custom workload or application, including AI Agents. The simple_datasource_qa tool adds VDS to the Langchain framework. This notebook shows you how you can use it to build agents that answer analytical questions grounded on your enterprise semantic models. \n",
+    "\n",
+    "Follow the [tableau-langchain](https://github.com/Tab-SE/tableau_langchain) project for more tools coming soon!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "311bce64",
+   "metadata": {},
+   "source": [
+    "#### Setup\n",
+    "Make sure you are running and have access to:\n",
+    "1. python version 3.12.2 or higher\n",
+    "2. A Tableau Cloud or Server environment with at least 1 published data source\n",
+    "\n",
+    "Get started by installing and/or importing the required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b178e95-ffae-4f04-ad77-1fdc2ab05edf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8605e87a-2253-4c89-992a-ecdbec955ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c13dca76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: regex>=2022.1.18 in /Users/joe.constantino/.pyenv/versions/3.12.2/lib/python3.12/site-packages (from tiktoken<1,>=0.7->langchain-openai->langchain-tableau) (2024.11.6)\r\n",
+      "Requirement already satisfied: httpcore==1.* in /Users/joe.constantino/.pyenv/versions/3.12.2/lib/python3.12/site-packages (from httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph->langchain-tableau) (1.0.7)\r\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /Users/joe.constantino/.pyenv/versions/3.12.2/lib/python3.12/site-packages (from httpcore==1.*->httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph->langchain-tableau) (0.14.0)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# %pip install langchain-tableau --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbaa05f4",
+   "metadata": {},
+   "source": [
+    "Note you may need to restart your kernal to use updated packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80473fcc",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "You can declare your environment variables explicitly, as shown in several cases in this doc. However, if these parameters are not provided, the simple_datasource_qa tool will attempt to automatically read them from environment variables.\n",
+    "\n",
+    "For the Data Source that you choose to query, make sure you've updated the VizqlDataApiAccess permission in Tableau to allow the VDS API to access that Data Source via REST. More info [here](https://help.tableau.com/current/server/en-us/permissions_capabilities.htm#data-sources\n",
+    "). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310d21b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# langchain package imports\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# langchain_tableau and langgraph imports\n",
+    "from langchain_tableau.tools.simple_datasource_qa import initialize_simple_datasource_qa\n",
+    "from langgraph.prebuilt import create_react_agent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "596d6718-f2e1-44bb-b614-65447862661c",
+   "metadata": {},
+   "source": [
+    "## Authentication Variables\n",
+    "You can declare your environment variables explicitly, as shown in several cases in this cookbook. However, if these parameters are not provided, the simple_datasource_qa tool will attempt to automatically read them from environment variables.\n",
+    "\n",
+    "For the Data Source that you choose, make sure you've updated the VizqlDataApiAccess permission in Tableau to allow the VDS API to access that Data Source via REST. More info [here](https://help.tableau.com/current/server/en-us/permissions_capabilities.htm#data-sources\n",
+    "). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ccfb4159-34ac-4816-a8f0-795c5442c0b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "tableau_server = \"https://stage-dataplane2.tableau.sfdc-shbmgi.svc.sfdcfc.net/\"  # replace with your Tableau server name\n",
+    "tableau_site = \"vizqldataservicestage02\"  # replace with your Tableau site\n",
+    "tableau_jwt_client_id = os.getenv(\n",
+    "    \"TABLEAU_JWT_CLIENT_ID\"\n",
+    ")  # a JWT client ID (obtained through Tableau's admin UI)\n",
+    "tableau_jwt_secret_id = os.getenv(\n",
+    "    \"TABLEAU_JWT_SECRET_ID\"\n",
+    ")  # a JWT secret ID (obtained through Tableau's admin UI)\n",
+    "tableau_jwt_secret = os.getenv(\n",
+    "    \"TABLEAU_JWT_SECRET\"\n",
+    ")  # a JWT secret ID (obtained through Tableau's admin UI)\n",
+    "tableau_api_version = \"3.21\"  # the current Tableau REST API Version\n",
+    "tableau_user = \"joe.constantino@salesforce.com\"  # replace with the username querying the target Tableau Data Source\n",
+    "\n",
+    "# For this cookbook we are connecting to the Superstore dataset that comes by default with every Tableau server\n",
+    "datasource_luid = (\n",
+    "    \"0965e61b-a072-43cf-994c-8c6cf526940d\"  # the target data source for this Tool\n",
+    ")\n",
+    "\n",
+    "# Add variables to control LLM models for the Agent and Tools\n",
+    "os.environ[\"OPENAI_API_KEY\"]  # set an your model API key as an environment variable\n",
+    "tooling_llm_model = \"gpt-4o\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64d08107",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "The initialize_simple_datasource_qa initializes the Langgraph tool called [simple_datasource_qa](https://github.com/Tab-SE/tableau_langchain/blob/3ff9047414479cd55d797c18a78f834d57860761/pip_package/langchain_tableau/tools/simple_datasource_qa.py#L101), which can be used for analytical questions and answers on a Tableau Data Source.\n",
+    "\n",
+    "This initializer function:\n",
+    "1. Authenticates to Tableau using Tableau's connected-app framework for JWT-based authentication. All the required variables must be defined at runtime or as environment variables.\n",
+    "2. Asynchronously queries for the field metadata of the target datasource specified in the datasource_luid variable.\n",
+    "3. Grounds on the metadata of the target datasource to transform natural language questions into the json-formatted query payload required to make VDS query-datasource requests.\n",
+    "4. Executes a POST request to VDS.\n",
+    "5. Formats and returns the results in a structured response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72ee3eca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initalize simple_datasource_qa for querying Tableau Datasources through VDS\n",
+    "analyze_datasource = initialize_simple_datasource_qa(\n",
+    "    domain=tableau_server,\n",
+    "    site=tableau_site,\n",
+    "    jwt_client_id=tableau_jwt_client_id,\n",
+    "    jwt_secret_id=tableau_jwt_secret_id,\n",
+    "    jwt_secret=tableau_jwt_secret,\n",
+    "    tableau_api_version=tableau_api_version,\n",
+    "    tableau_user=tableau_user,\n",
+    "    datasource_luid=datasource_luid,\n",
+    "    tooling_llm_model=tooling_llm_model,\n",
+    ")\n",
+    "\n",
+    "# load the List of Tools to be used by the Agent. In this case we will just load our data source Q&A tool.\n",
+    "tools = [analyze_datasource]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ac5daa0-4336-48d0-9c26-20bf2c252bad",
+   "metadata": {},
+   "source": [
+    "## Invocation - Langgraph Example\n",
+    "First, we'll initlialize the LLM of our choice. Then, we define an agent using a langgraph agent constructor class and invoke it with a query related to the target data source. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "06a1d3f7-79a8-452e-b37e-9070d15445b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "Here are the results for the states with the highest sales and profits based on the data queried:\n",
+       "\n",
+       "### States with the Most Sales\n",
+       "1. **California**: $457,687.63\n",
+       "2. **New York**: $310,876.27\n",
+       "3. **Texas**: $170,188.05\n",
+       "4. **Washington**: $138,641.27\n",
+       "5. **Pennsylvania**: $116,511.91\n",
+       "\n",
+       "### States with the Most Profit\n",
+       "1. **California**: $76,381.39\n",
+       "2. **New York**: $74,038.55\n",
+       "3. **Washington**: $33,402.65\n",
+       "4. **Michigan**: $24,463.19\n",
+       "5. **Virginia**: $18,597.95\n",
+       "\n",
+       "### Comparison\n",
+       "- **California** and **New York** are the only states that appear in both lists, indicating they are the top sellers and also generate the most profit.\n",
+       "- **Texas**, while having the third highest sales, does not rank in the top five for profit, showing a potential issue with profitability despite high sales.\n",
+       "\n",
+       "This analysis suggests that high sales do not always correlate with high profits, as seen with Texas."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Markdown, display\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "\n",
+    "tableauAgent = create_react_agent(model, tools)\n",
+    "\n",
+    "# Run the agent\n",
+    "messages = tableauAgent.invoke(\n",
+    "    {\n",
+    "        \"messages\": [\n",
+    "            (\n",
+    "                \"human\",\n",
+    "                \"which states sell the most? Are those the same states with the most profits?\",\n",
+    "            )\n",
+    "        ]\n",
+    "    }\n",
+    ")\n",
+    "messages\n",
+    "# display(Markdown(messages['messages'][4].content)) #display a nicely formatted answer for successful generations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6b20093",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "TODO."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12ab3d7b",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "TODO."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (package_test_env)",
+   "language": "python",
+   "name": "package_test_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/tools/taiga.ipynb

@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "10238e62-3465-4973-9279-606cbb7ccf16",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Taiga\n",
+    "---"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Taiga\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Taiga tooling in [langchain_taiga](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/docs/tools.ipynb). For more details on each tool and configuration, see the docstrings in your repository or relevant doc pages.\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class                                                                                                | Package                                                                    | Serializable | JS support |                                        Package latest                                        |\n",
+    "|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------| :---:        |:------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|\n",
+    "| `create_entity_tool`, `search_entities_tool`, `get_entity_by_ref_tool`, `update_entity_by_ref_tool` , `add_comment_by_ref_tool`, `add_attachment_by_ref_tool` | [langchain-taiga](https://github.com/Shikenso-Analytics/langchain-taiga)   | N/A          |                                      TBD                                       | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-taiga?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Tool features\n",
+    "\n",
+    "- **`create_entity_tool`**: Creates user stories, tasks and issues in Taiga.\n",
+    "- **`search_entities_tool`**: Searches for user stories, tasks and issues in Taiga.\n",
+    "- **`get_entity_by_ref_tool`**: Gets a user story, task or issue by reference.\n",
+    "- **`update_entity_by_ref_tool`**: Updates a user story, task or issue by reference.\n",
+    "- **`add_comment_by_ref_tool`**: Adds a comment to a user story, task or issue.\n",
+    "- **`add_attachment_by_ref_tool`**: Adds an attachment to a user story, task or issue.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "The integration lives in the `langchain-taiga` package."
+   ],
+   "id": "41616bfd02d989a6"
+  },
+  {
+   "cell_type": "code",
+   "id": "f85b4089",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-28T12:43:23.290414Z",
+     "start_time": "2025-02-28T12:43:23.162563Z"
+    }
+   },
+   "source": "%pip install --quiet -U langchain-taiga",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/henlein/Workspace/PyCharm/langchain/.venv/bin/python: No module named pip\r\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b15e9266",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "This integration requires you to set `TAIGA_URL`, `TAIGA_API_URL`, `TAIGA_USERNAME`, `TAIGA_PASSWORD` and `OPENAI_API_KEY` as environment variables to authenticate with Taiga.\n",
+    "\n",
+    "```bash\n",
+    "export TAIGA_URL=\"https://taiga.xyz.org/\"\n",
+    "export TAIGA_API_URL=\"https://taiga.xyz.org/\"\n",
+    "export TAIGA_USERNAME=\"username\"\n",
+    "export TAIGA_PASSWORD=\"pw\"\n",
+    "export OPENAI_API_KEY=\"OPENAI_API_KEY\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-28T12:43:23.295879Z",
+     "start_time": "2025-02-28T12:43:23.293809Z"
+    }
+   },
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ],
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Below is an example showing how to instantiate the Taiga tools in `langchain_taiga`. Adjust as needed for your specific usage."
+   ],
+   "id": "d6eab61edeeb40a5"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from langchain_taiga.tools.discord_read_messages import create_entity_tool\n",
+    "from langchain_taiga.tools.discord_send_messages import search_entities_tool\n",
+    "\n",
+    "create_tool = create_entity_tool\n",
+    "search_tool = search_entities_tool"
+   ],
+   "id": "8ae97a3413cd040e"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74147a1a",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "### Direct invocation with args\n",
+    "\n",
+    "Below is a simple example of calling the tool with keyword arguments in a dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "65310a8b-eb0c-4d9e-a618-4f4abe2414fc",
+   "metadata": {},
+   "source": [
+    "from langchain_taiga.tools.taiga_tools import (\n",
+    "    add_attachment_by_ref_tool,\n",
+    "    add_comment_by_ref_tool,\n",
+    "    create_entity_tool,\n",
+    "    get_entity_by_ref_tool,\n",
+    "    search_entities_tool,\n",
+    "    update_entity_by_ref_tool,\n",
+    ")\n",
+    "\n",
+    "response = create_entity_tool.invoke(\n",
+    "    {\n",
+    "        \"project_slug\": \"slug\",\n",
+    "        \"entity_type\": \"us\",\n",
+    "        \"subject\": \"subject\",\n",
+    "        \"status\": \"new\",\n",
+    "        \"description\": \"desc\",\n",
+    "        \"parent_ref\": 5,\n",
+    "        \"assign_to\": \"user\",\n",
+    "        \"due_date\": \"2022-01-01\",\n",
+    "        \"tags\": [\"tag1\", \"tag2\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "response = search_entities_tool.invoke(\n",
+    "    {\"project_slug\": \"slug\", \"query\": \"query\", \"entity_type\": \"task\"}\n",
+    ")\n",
+    "\n",
+    "response = get_entity_by_ref_tool.invoke(\n",
+    "    {\"entity_type\": \"user_story\", \"project_id\": 1, \"ref\": \"1\"}\n",
+    ")\n",
+    "\n",
+    "response = update_entity_by_ref_tool.invoke(\n",
+    "    {\"project_slug\": \"slug\", \"entity_ref\": 555, \"entity_type\": \"us\"}\n",
+    ")\n",
+    "\n",
+    "\n",
+    "response = add_comment_by_ref_tool.invoke(\n",
+    "    {\"project_slug\": \"slug\", \"entity_ref\": 3, \"entity_type\": \"us\", \"comment\": \"new\"}\n",
+    ")\n",
+    "\n",
+    "response = add_attachment_by_ref_tool.invoke(\n",
+    "    {\n",
+    "        \"project_slug\": \"slug\",\n",
+    "        \"entity_ref\": 3,\n",
+    "        \"entity_type\": \"us\",\n",
+    "        \"attachment_url\": \"url\",\n",
+    "        \"content_type\": \"png\",\n",
+    "        \"description\": \"desc\",\n",
+    "    }\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e73897",
+   "metadata": {},
+   "source": [
+    "### Invocation with ToolCall\n",
+    "\n",
+    "If you have a model-generated `ToolCall`, pass it to `tool.invoke()` in the format shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f90e33a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
+    "model_generated_tool_call = {\n",
+    "    \"args\": {\"project_slug\": \"slug\", \"query\": \"query\", \"entity_type\": \"task\"},\n",
+    "    \"id\": \"1\",\n",
+    "    \"name\": search_entities_tool.name,\n",
+    "    \"type\": \"tool_call\",\n",
+    "}\n",
+    "tool.invoke(model_generated_tool_call)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "Below is a more complete example showing how you might integrate the `create_entity_tool` and `search_entities_tool` tools in a chain or agent with an LLM. This example assumes you have a function (like `create_react_agent`) that sets up a LangChain-style agent capable of calling tools when appropriate.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "# Example: Using Taiga Tools in an Agent\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "from langchain_taiga.tools.taiga_tools import create_entity_tool, search_entities_tool\n",
+    "\n",
+    "# 1. Instantiate or configure your language model\n",
+    "# (Replace with your actual LLM, e.g., ChatOpenAI(temperature=0))\n",
+    "llm = ...\n",
+    "\n",
+    "# 2. Build an agent that has access to these tools\n",
+    "agent_executor = create_react_agent(llm, [create_entity_tool, search_entities_tool])\n",
+    "\n",
+    "# 4. Formulate a user query that may invoke one or both tools\n",
+    "example_query = \"Please create a new user story with the subject 'subject' in slug project: 'slug'\"\n",
+    "\n",
+    "# 5. Execute the agent in streaming mode (or however your code is structured)\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "\n",
+    "# 6. Print out the model's responses (and any tool outputs) as they arrive\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()\n",
+    "```\n"
+   ],
+   "id": "8cafefef7c8bd43e"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## API reference\n",
+    "\n",
+    "See the docstrings in:\n",
+    "- [taiga_tools.py](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/langchain_taiga/tools/taiga_tools.py)\n",
+    "- [toolkits.py](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/langchain_taiga/toolkits.py)\n",
+    "\n",
+    "for usage details, parameters, and advanced configurations."
+   ],
+   "id": "4ac8146c"
+  }
+ ],
+ "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/tools/valthera.ipynb

@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Valthera\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Valthera\n",
+    "\n",
+    "Enable AI agents to engage users when they're most likely to respond.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Valthera is an open-source framework that enables LLM Agents to engage users in a more meaningful way. It is built on BJ Fogg's Behavior Model (B=MAT) and leverages data from multiple sources (such as HubSpot, PostHog, and Snowflake) to assess a user's **motivation** and **ability** before triggering an action.\n",
+    "\n",
+    "In this guide, you'll learn:\n",
+    "\n",
+    "- **Core Concepts:** Overview of the components (Data Aggregator, Scorer, Reasoning Engine, and Trigger Generator).\n",
+    "- **System Architecture:** How data flows through the system and how decisions are made.\n",
+    "- **Customization:** How to extend connectors, scoring metrics, and decision rules to fit your needs.\n",
+    "\n",
+    "Let's dive in!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "This section covers installation of dependencies and setting up custom data connectors for Valthera."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install openai langchain langchain_openai valthera langchain_valthera langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Any, Dict, List\n",
+    "\n",
+    "from valthera.connectors.base import BaseConnector\n",
+    "\n",
+    "\n",
+    "class MockHubSpotConnector(BaseConnector):\n",
+    "    \"\"\"\n",
+    "    Simulates data retrieval from HubSpot. Provides information such as lead score,\n",
+    "    lifecycle stage, and marketing metrics.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def get_user_data(self, user_id: str) -> Dict[str, Any]:\n",
+    "        \"\"\"\n",
+    "        Retrieve mock HubSpot data for a given user.\n",
+    "\n",
+    "        Args:\n",
+    "            user_id: The unique identifier for the user\n",
+    "\n",
+    "        Returns:\n",
+    "            A dictionary containing HubSpot user data\n",
+    "        \"\"\"\n",
+    "        return {\n",
+    "            \"hubspot_contact_id\": \"999-ZZZ\",\n",
+    "            \"lifecycle_stage\": \"opportunity\",\n",
+    "            \"lead_status\": \"engaged\",\n",
+    "            \"hubspot_lead_score\": 100,\n",
+    "            \"company_name\": \"MaxMotivation Corp.\",\n",
+    "            \"last_contacted_date\": \"2023-09-20\",\n",
+    "            \"hubspot_marketing_emails_opened\": 20,\n",
+    "            \"marketing_emails_clicked\": 10,\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "class MockPostHogConnector(BaseConnector):\n",
+    "    \"\"\"\n",
+    "    Simulates data retrieval from PostHog. Provides session data and engagement events.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def get_user_data(self, user_id: str) -> Dict[str, Any]:\n",
+    "        \"\"\"\n",
+    "        Retrieve mock PostHog data for a given user.\n",
+    "\n",
+    "        Args:\n",
+    "            user_id: The unique identifier for the user\n",
+    "\n",
+    "        Returns:\n",
+    "            A dictionary containing PostHog user data\n",
+    "        \"\"\"\n",
+    "        return {\n",
+    "            \"distinct_ids\": [user_id, f\"email_{user_id}\"],\n",
+    "            \"last_event_timestamp\": \"2023-09-20T12:34:56Z\",\n",
+    "            \"feature_flags\": [\"beta_dashboard\", \"early_access\"],\n",
+    "            \"posthog_session_count\": 30,\n",
+    "            \"avg_session_duration_sec\": 400,\n",
+    "            \"recent_event_types\": [\"pageview\", \"button_click\", \"premium_feature_used\"],\n",
+    "            \"posthog_events_count_past_30days\": 80,\n",
+    "            \"posthog_onboarding_steps_completed\": 5,\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "class MockSnowflakeConnector(BaseConnector):\n",
+    "    \"\"\"\n",
+    "    Simulates retrieval of additional user profile data from Snowflake.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def get_user_data(self, user_id: str) -> Dict[str, Any]:\n",
+    "        \"\"\"\n",
+    "        Retrieve mock Snowflake data for a given user.\n",
+    "\n",
+    "        Args:\n",
+    "            user_id: The unique identifier for the user\n",
+    "\n",
+    "        Returns:\n",
+    "            A dictionary containing Snowflake user data\n",
+    "        \"\"\"\n",
+    "        return {\n",
+    "            \"user_id\": user_id,\n",
+    "            \"email\": f\"{user_id}@example.com\",\n",
+    "            \"subscription_status\": \"paid\",\n",
+    "            \"plan_tier\": \"premium\",\n",
+    "            \"account_creation_date\": \"2023-01-01\",\n",
+    "            \"preferred_language\": \"en\",\n",
+    "            \"last_login_datetime\": \"2023-09-20T12:00:00Z\",\n",
+    "            \"behavior_complexity\": 3,\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "In this section, we instantiate the core components. First, we create a Data Aggregator to combine data from the custom connectors. Then, we configure the scoring metrics for motivation and ability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from valthera.aggregator import DataAggregator\n",
+    "\n",
+    "# Constants for configuration\n",
+    "LEAD_SCORE_MAX = 100\n",
+    "EVENTS_COUNT_MAX = 50\n",
+    "EMAILS_OPENED_FACTOR = 10.0\n",
+    "SESSION_COUNT_FACTOR_1 = 5.0\n",
+    "ONBOARDING_STEPS_FACTOR = 5.0\n",
+    "SESSION_COUNT_FACTOR_2 = 10.0\n",
+    "BEHAVIOR_COMPLEXITY_MAX = 5.0\n",
+    "\n",
+    "# Initialize data aggregator\n",
+    "data_aggregator = DataAggregator(\n",
+    "    connectors={\n",
+    "        \"hubspot\": MockHubSpotConnector(),\n",
+    "        \"posthog\": MockPostHogConnector(),\n",
+    "        \"snowflake\": MockSnowflakeConnector(),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# You can now fetch unified user data by calling data_aggregator.get_user_context(user_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Callable, Union\n",
+    "\n",
+    "from valthera.scorer import ValtheraScorer\n",
+    "\n",
+    "\n",
+    "# Define transform functions with proper type annotations\n",
+    "def transform_lead_score(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform lead score to a value between 0 and 1.\"\"\"\n",
+    "    return min(x, LEAD_SCORE_MAX) / LEAD_SCORE_MAX\n",
+    "\n",
+    "\n",
+    "def transform_events_count(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform events count to a value between 0 and 1.\"\"\"\n",
+    "    return min(x, EVENTS_COUNT_MAX) / EVENTS_COUNT_MAX\n",
+    "\n",
+    "\n",
+    "def transform_emails_opened(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform emails opened to a value between 0 and 1.\"\"\"\n",
+    "    return min(x / EMAILS_OPENED_FACTOR, 1.0)\n",
+    "\n",
+    "\n",
+    "def transform_session_count_1(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform session count for motivation to a value between 0 and 1.\"\"\"\n",
+    "    return min(x / SESSION_COUNT_FACTOR_1, 1.0)\n",
+    "\n",
+    "\n",
+    "def transform_onboarding_steps(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform onboarding steps to a value between 0 and 1.\"\"\"\n",
+    "    return min(x / ONBOARDING_STEPS_FACTOR, 1.0)\n",
+    "\n",
+    "\n",
+    "def transform_session_count_2(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform session count for ability to a value between 0 and 1.\"\"\"\n",
+    "    return min(x / SESSION_COUNT_FACTOR_2, 1.0)\n",
+    "\n",
+    "\n",
+    "def transform_behavior_complexity(x: Union[int, float]) -> float:\n",
+    "    \"\"\"Transform behavior complexity to a value between 0 and 1.\"\"\"\n",
+    "    return 1 - (min(x, BEHAVIOR_COMPLEXITY_MAX) / BEHAVIOR_COMPLEXITY_MAX)\n",
+    "\n",
+    "\n",
+    "# Scoring configuration for user motivation\n",
+    "motivation_config = [\n",
+    "    {\"key\": \"hubspot_lead_score\", \"weight\": 0.30, \"transform\": transform_lead_score},\n",
+    "    {\n",
+    "        \"key\": \"posthog_events_count_past_30days\",\n",
+    "        \"weight\": 0.30,\n",
+    "        \"transform\": transform_events_count,\n",
+    "    },\n",
+    "    {\n",
+    "        \"key\": \"hubspot_marketing_emails_opened\",\n",
+    "        \"weight\": 0.20,\n",
+    "        \"transform\": transform_emails_opened,\n",
+    "    },\n",
+    "    {\n",
+    "        \"key\": \"posthog_session_count\",\n",
+    "        \"weight\": 0.20,\n",
+    "        \"transform\": transform_session_count_1,\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Scoring configuration for user ability\n",
+    "ability_config = [\n",
+    "    {\n",
+    "        \"key\": \"posthog_onboarding_steps_completed\",\n",
+    "        \"weight\": 0.30,\n",
+    "        \"transform\": transform_onboarding_steps,\n",
+    "    },\n",
+    "    {\n",
+    "        \"key\": \"posthog_session_count\",\n",
+    "        \"weight\": 0.30,\n",
+    "        \"transform\": transform_session_count_2,\n",
+    "    },\n",
+    "    {\n",
+    "        \"key\": \"behavior_complexity\",\n",
+    "        \"weight\": 0.40,\n",
+    "        \"transform\": transform_behavior_complexity,\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "# Instantiate the scorer\n",
+    "scorer = ValtheraScorer(motivation_config, ability_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "Next, we set up the Reasoning Engine and Trigger Generator, then bring all components together by instantiating the Valthera Tool. Finally, we execute the agent workflow to process an input message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from valthera.reasoning_engine import ReasoningEngine\n",
+    "\n",
+    "# Define threshold as constant\n",
+    "SCORE_THRESHOLD = 0.75\n",
+    "\n",
+    "\n",
+    "# Function to safely get API key\n",
+    "def get_openai_api_key() -> str:\n",
+    "    \"\"\"Get OpenAI API key with error handling.\"\"\"\n",
+    "    api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
+    "    if not api_key:\n",
+    "        raise ValueError(\"OPENAI_API_KEY not found in environment variables\")\n",
+    "    return api_key\n",
+    "\n",
+    "\n",
+    "# Decision rules using constant\n",
+    "decision_rules = [\n",
+    "    {\n",
+    "        \"condition\": f\"motivation >= {SCORE_THRESHOLD} and ability >= {SCORE_THRESHOLD}\",\n",
+    "        \"action\": \"trigger\",\n",
+    "        \"description\": \"Both scores are high enough.\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"condition\": f\"motivation < {SCORE_THRESHOLD}\",\n",
+    "        \"action\": \"improve_motivation\",\n",
+    "        \"description\": \"User motivation is low.\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"condition\": f\"ability < {SCORE_THRESHOLD}\",\n",
+    "        \"action\": \"improve_ability\",\n",
+    "        \"description\": \"User ability is low.\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"condition\": \"otherwise\",\n",
+    "        \"action\": \"defer\",\n",
+    "        \"description\": \"No action needed at this time.\",\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "try:\n",
+    "    api_key = get_openai_api_key()\n",
+    "    reasoning_engine = ReasoningEngine(\n",
+    "        llm=ChatOpenAI(\n",
+    "            model_name=\"gpt-4-turbo\", temperature=0.0, openai_api_key=api_key\n",
+    "        ),\n",
+    "        decision_rules=decision_rules,\n",
+    "    )\n",
+    "except ValueError as e:\n",
+    "    print(f\"Error initializing reasoning engine: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from valthera.trigger_generator import TriggerGenerator\n",
+    "\n",
+    "try:\n",
+    "    api_key = get_openai_api_key()  # Reuse the function for consistency\n",
+    "    trigger_generator = TriggerGenerator(\n",
+    "        llm=ChatOpenAI(\n",
+    "            model_name=\"gpt-4-turbo\", temperature=0.7, openai_api_key=api_key\n",
+    "        )\n",
+    "    )\n",
+    "except ValueError as e:\n",
+    "    print(f\"Error initializing trigger generator: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_valthera.tools import ValtheraTool\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "try:\n",
+    "    api_key = get_openai_api_key()\n",
+    "\n",
+    "    # Initialize Valthera tool\n",
+    "    valthera_tool = ValtheraTool(\n",
+    "        data_aggregator=data_aggregator,\n",
+    "        motivation_config=motivation_config,\n",
+    "        ability_config=ability_config,\n",
+    "        reasoning_engine=reasoning_engine,\n",
+    "        trigger_generator=trigger_generator,\n",
+    "    )\n",
+    "\n",
+    "    # Create agent with LLM\n",
+    "    llm = ChatOpenAI(model_name=\"gpt-4-turbo\", temperature=0.0, openai_api_key=api_key)\n",
+    "    tools = [valthera_tool]\n",
+    "    graph = create_react_agent(llm, tools=tools)\n",
+    "\n",
+    "    # Define input message for testing\n",
+    "    inputs = {\n",
+    "        \"messages\": [(\"user\", \"Evaluate behavior for user_12345: Finish Onboarding\")]\n",
+    "    }\n",
+    "\n",
+    "    # Process the input and display responses\n",
+    "    print(\"Running Valthera agent workflow...\")\n",
+    "    for response in graph.stream(inputs, stream_mode=\"values\"):\n",
+    "        print(response)\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"Error running Valthera workflow: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "This integration does not currently support chaining operations. Future releases may include chaining support."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "Below is an overview of the key APIs provided by the Valthera integration:\n",
+    "\n",
+    "- **Data Aggregator:** Use `data_aggregator.get_user_context(user_id)` to fetch aggregated user data.\n",
+    "- **Scorer:** The `ValtheraScorer` computes motivation and ability scores based on the provided configurations.\n",
+    "- **Reasoning Engine:** The `ReasoningEngine` evaluates decision rules to determine the appropriate action (trigger, improve motivation, improve ability, or defer).\n",
+    "- **Trigger Generator:** Generates personalized trigger messages using the LLM.\n",
+    "- **Valthera Tool:** Integrates all the components to process inputs and execute the agent workflow.\n",
+    "\n",
+    "For detailed usage, refer to the inline documentation in the source code."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "title": "Valthera Developer Guide"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/tools/writer.ipynb

@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a6f91f20",
+   "metadata": {},
+   "source": [
+    "# Writer Tools\n",
+    "\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Writer [tools](https://python.langchain.com/docs/concepts/tools/). For detailed documentation of all Writer features and configurations head to the [Writer docs](https://dev.writer.com/home).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class                                                                                                      | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-----------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [GraphTool](https://github.com/writer/langchain-writer/blob/main/langchain_writer/tools.py#L9) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Features\n",
+    "\n",
+    "We provide usage of two types of tools for use with `ChatWriter`: `function` and `graph`.\n",
+    "\n",
+    "#### Function\n",
+    "\n",
+    "Functions are the most common type of tool, which allows the LLM to call external APIs, fetch data from databases, and generally perform any external action you want to do. Visit our [tool calling docs](https://dev.writer.com/api-guides/tool-calling#tool-calling) for additional information.\n",
+    "\n",
+    "#### Graph\n",
+    "\n",
+    "The `Graph` tool is Writer's graph-based retrieval-augmented generation (RAG) called Knowledge Graph. This tool enables developers to simply pass the graph ID to the model and it will return the answer to the question in the prompt. To learn more, see our [Knowledge Graph API docs](https://dev.writer.com/api-guides/knowledge-graph)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40136062a4c267f3",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to generate an API key (you can follow this [Quickstart](https://dev.writer.com/api-guides/quickstart)). Then, set the WRITER_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "80d4e1a791aaa8",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "You can bind graph or function tools to `ChatWriter`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "570e4abffc12774",
+   "metadata": {},
+   "source": [
+    "### Graph Tools\n",
+    "\n",
+    "To bind graph tools, first create and initialize a `GraphTool` instance with the `graph_ids` you want to use as sources:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "6faaae25509f0f28",
+   "metadata": {},
+   "source": [
+    "from langchain_writer.chat_models import ChatWriter\n",
+    "from langchain_writer.tools import GraphTool\n",
+    "\n",
+    "chat = ChatWriter()\n",
+    "\n",
+    "graph_id = getpass.getpass(\"Enter Writer Knowledge Graph ID: \")\n",
+    "graph_tool = GraphTool(graph_ids=[graph_id])"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50ea16fc3a382cf",
+   "metadata": {},
+   "source": "## Instantiation"
+  },
+  {
+   "cell_type": "code",
+   "id": "e98d7deedb0e5c6f",
+   "metadata": {},
+   "source": [
+    "from typing import Optional\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_supercopa_trophies_count(club_name: str) -> Optional[int]:\n",
+    "    \"\"\"Returns information about supercopa trophies count.\n",
+    "\n",
+    "    Args:\n",
+    "        club_name: Club you want to investigate info of supercopa trophies about\n",
+    "\n",
+    "    Returns:\n",
+    "        Number of supercopa trophies or None if there is no info about requested club\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if club_name == \"Barcelona\":\n",
+    "        return 15\n",
+    "    elif club_name == \"Real Madrid\":\n",
+    "        return 13\n",
+    "    elif club_name == \"Atletico Madrid\":\n",
+    "        return 2\n",
+    "    else:\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "class GetWeather(BaseModel):\n",
+    "    \"\"\"Get the current weather in a given location\"\"\"\n",
+    "\n",
+    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+    "\n",
+    "\n",
+    "get_product_info = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_product_info\",\n",
+    "        \"description\": \"Get information about a product by its id\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"product_id\": {\n",
+    "                    \"type\": \"number\",\n",
+    "                    \"description\": \"The unique identifier of the product to retrieve information for\",\n",
+    "                }\n",
+    "            },\n",
+    "            \"required\": [\"product_id\"],\n",
+    "        },\n",
+    "    },\n",
+    "}"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1753ca46",
+   "metadata": {},
+   "source": [
+    "### Binding tools\n",
+    "Then, you can simply bind all tools to the `ChatWriter` instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a4833f2597a87777",
+   "metadata": {},
+   "source": [
+    "chat.bind_tools(\n",
+    "    [graph_tool, get_supercopa_trophies_count, GetWeather, get_product_info]\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a300614e244f4aaf",
+   "metadata": {},
+   "source": [
+    "All tools are stored in the `tools` attribute of the `ChatWriter` instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ccb61b945a56672b",
+   "metadata": {},
+   "source": [
+    "chat.tools"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52dfec8a",
+   "metadata": {},
+   "source": [
+    "The tool choice mode is stored at the `tool_choice` attribute, which is `auto` by default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "381f0d4b9a8357a4",
+   "metadata": {},
+   "source": [
+    "chat.tool_choice"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74147a1a",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "The model will automatically choose the tool during invocation with all modes (streaming/non-streaming, sync/async)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "74df06b58b5dc2e9",
+   "metadata": {},
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        \"Use knowledge graph tool to compose this answer. Tell me what th first line of documents stored in your KG. Also I want to know: how many SuperCopa trophies have Barcelona won?\"\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "response = chat.invoke(messages)\n",
+    "messages.append(response)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b015fdb2",
+   "metadata": {},
+   "source": [
+    "In the case of function tools, you will receive an assistant message with the tool call request."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e271e0fc677446b2",
+   "metadata": {},
+   "source": [
+    "print(response.tool_calls)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aeff6a17ce3176b1",
+   "metadata": {},
+   "source": [
+    "Then you can manually handle tool call request, send to model and receive final response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "156b58108aa9b367",
+   "metadata": {},
+   "source": [
+    "for tool_call in response.tool_calls:\n",
+    "    selected_tool = {\n",
+    "        \"get_supercopa_trophies_count\": get_supercopa_trophies_count,\n",
+    "    }[tool_call[\"name\"].lower()]\n",
+    "    tool_msg = selected_tool.invoke(tool_call)\n",
+    "    messages.append(tool_msg)\n",
+    "\n",
+    "response = chat.invoke(messages)\n",
+    "print(response.content)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff9347b2",
+   "metadata": {},
+   "source": [
+    "With a `GraphTool`, the model will call it remotely and return usage info in the `additional_kwargs` under the `graph_data` key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "4b3c6f05096fc9e3",
+   "metadata": {},
+   "source": [
+    "print(response.additional_kwargs[\"graph_data\"])"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f001d2ca",
+   "metadata": {},
+   "source": [
+    "The `content` attribute contains the final response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "eb6e0da74b10b8fc",
+   "metadata": {},
+   "source": [
+    "print(response.content)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "602631cd878e5dbe",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "Due to specificity of Writer Graph tool (you don't need to call it manually, Writer server will call it by himself and return RAG based generation) it's impossible to invoke it separately, so GraphTool can't be used as part of chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ac8146c",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "For detailed documentation of all `GraphTool` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/writer/tools/langchain_writer.tools.GraphTool.html#langchain_writer.tools.GraphTool)."
+   ]
+  }
+ ],
+ "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/tutorials/qa_chat_history.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of \"memory\" of past questions and answers, and some logic for incorporating those into its current thinking.\n",
     "\n",
-    "This is a the second part of a multi-part tutorial:\n",
+    "This is the second part of a multi-part tutorial:\n",
     "\n",
     "- [Part 1](/docs/tutorials/rag) introduces RAG and walks through a minimal implementation.\n",
     "- [Part 2](/docs/tutorials/qa_chat_history) (this guide) extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes.\n",

docs/scripts/check_templates.py

@@ -59,7 +59,10 @@ def check_header_order(path: Path) -> None:
     if doc_dir not in INFO_BY_DIR:
         # Skip if not a directory we care about
         return
-    headers = _get_headers(doc_dir)
+    if "toolkit" in path.name:
+        headers = _get_headers("toolkits")
+    else:
+        headers = _get_headers(doc_dir)
     issue_number = INFO_BY_DIR[doc_dir].get("issue_number", "nonexistent")
 
     print(f"Checking {doc_dir} page {path}")

docs/src/theme/ChatModelTabs.js

@@ -211,6 +211,13 @@ ${llmVarName} = ChatWatsonx(
       apiKeyName: "DATABRICKS_TOKEN",
       packageName: "databricks-langchain",
     },
+    {
+      value: "xai",
+      label: "xAI",
+      model: "grok-2",
+      apiKeyName: "XAI_API_KEY",
+      packageName: "langchain-xai",
+    },
   ].map((item) => ({
     ...item,
     ...overrideParams?.[item.value],

docs/src/theme/FeatureTables.js

@@ -888,6 +888,13 @@ const FEATURE_TABLES = {
                 api: "Package",
                 apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html"
             },
+            {
+                name: "PyMuPDF4LLM",
+                link: "pymupdf4llm",
+                source: "Load PDF content to Markdown using PyMuPDF4LLM",
+                api: "Package",
+                apiLink: "https://github.com/lakinduboteju/langchain-pymupdf4llm"
+            },
             {
                 name: "PDFMiner",
                 link: "pdfminer",

docs/vercel_overrides.txt

@@ -1 +1,2 @@
-httpx
+httpx
+grpcio

libs/cli/Makefile

@@ -48,4 +48,5 @@ _e2e_test:
 			poetry run pip install -e ../../../standard-tests && \
 			make format lint tests && \
 			poetry install --with test_integration && \
+			poetry run pip install -e ../../../core && \
 			make integration_test 

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

@@ -181,11 +181,11 @@
    "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
    "metadata": {},
    "source": [
-    "## Chaining\n",
+    "## Use within an agent\n",
     "\n",
     "- TODO: Add user question and run cells\n",
     "\n",
-    "We can use our tool in a chain by first binding it to a [tool-calling model](/docs/how_to/tool_calling/) and then calling it:\n",
+    "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
@@ -208,6 +208,19 @@
     "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bea35fa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "tools = [tool]\n",
+    "agent = create_react_agent(llm, tools)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -215,32 +228,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnableConfig, chain\n",
+    "example_query = \"...\"\n",
     "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"human\", \"{user_input}\"),\n",
-    "        (\"placeholder\", \"{messages}\"),\n",
-    "    ]\n",
+    "events = agent.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
     ")\n",
-    "\n",
-    "# specifying tool_choice will force the model to call this tool.\n",
-    "llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)\n",
-    "\n",
-    "llm_chain = prompt | llm_with_tools\n",
-    "\n",
-    "\n",
-    "@chain\n",
-    "def tool_chain(user_input: str, config: RunnableConfig):\n",
-    "    input_ = {\"user_input\": user_input}\n",
-    "    ai_msg = llm_chain.invoke(input_, config=config)\n",
-    "    tool_msgs = tool.batch(ai_msg.tool_calls, config=config)\n",
-    "    return llm_chain.invoke({**input_, \"messages\": [ai_msg, *tool_msgs]}, config=config)\n",
-    "\n",
-    "\n",
-    "tool_chain.invoke(\"...\")"
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
    ]
   },
   {

libs/cli/pyproject.toml

@@ -15,7 +15,7 @@ dependencies = [
     "gritql<1.0.0,>=0.2.0",
 ]
 name = "langchain-cli"
-version = "0.0.35"
+version = "0.0.36"
 description = "CLI for interacting with LangChain"
 readme = "README.md"
 
@@ -31,11 +31,12 @@ langchain-cli = "langchain_cli.cli:app"
 [dependency-groups]
 dev = ["pytest<8.0.0,>=7.4.2", "pytest-watch<5.0.0,>=4.2.0"]
 lint = ["ruff<1.0,>=0.5", "mypy<2.0.0,>=1.13.0"]
-test = ["langchain"]
+test = ["langchain-core", "langchain"]
 typing = ["langchain"]
 test_integration = []
 
 [tool.uv.sources]
+langchain-core = { path = "../core", editable = true }
 langchain = { path = "../langchain", editable = true }
 
 [tool.ruff.lint]

libs/cli/uv.lock

@@ -6,120 +6,6 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
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-
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-version = "3.11.11"
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-    { name = "aiosignal" },
-    { name = "async-timeout", marker = "python_full_version < '3.11'" },
-    { name = "attrs" },
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+version = "0.3.20"
 source = { editable = "../langchain" }
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     { name = "langchain-aws", marker = "extra == 'aws'" },
@@ -657,14 +445,12 @@ requires-dist = [
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     { name = "langchain-text-splitters", editable = "../text-splitters" },
     { name = "langchain-together", marker = "extra == 'together'" },
+    { name = "langchain-xai", marker = "extra == 'xai'" },
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-    { name = "tenacity", specifier = ">=8.1.0,!=8.4.0,<10" },
 ]
 
 [package.metadata.requires-dev]
@@ -682,7 +468,7 @@ lint = [
     { name = "ruff", specifier = ">=0.9.2,<1.0.0" },
 ]
 test = [
-    { name = "blockbuster", specifier = ">=1.5.14,<1.6" },
+    { name = "blockbuster", specifier = ">=1.5.18,<1.6" },
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     { name = "duckdb-engine", specifier = ">=0.9.2,<1.0.0" },
@@ -692,6 +478,7 @@ test = [
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     { name = "langchain-text-splitters", editable = "../text-splitters" },
     { name = "lark", specifier = ">=1.1.5,<2.0.0" },
+    { name = "numpy", specifier = ">=1.26.4,<3" },
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     { name = "pandas", specifier = ">=2.0.0,<3.0.0" },
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@@ -722,6 +509,7 @@ typing = [
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     { name = "mypy", specifier = ">=1.10,<2.0" },
     { name = "mypy-protobuf", specifier = ">=3.0.0,<4.0.0" },
+    { name = "numpy", specifier = ">=1.26.4,<3" },
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     { name = "types-pytz", specifier = ">=2023.3.0.0,<2024.0.0.0" },
     { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
@@ -754,6 +542,7 @@ lint = [
 ]
 test = [
     { name = "langchain" },
+    { name = "langchain-core" },
 ]
 typing = [
     { name = "langchain" },
@@ -778,13 +567,16 @@ lint = [
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     { name = "ruff", specifier = ">=0.5,<1.0" },
 ]
-test = [{ name = "langchain", editable = "../langchain" }]
+test = [
+    { name = "langchain", editable = "../langchain" },
+    { name = "langchain-core", editable = "../core" },
+]
 test-integration = []
 typing = [{ name = "langchain", editable = "../langchain" }]
 
 [[package]]
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-version = "0.3.35"
+version = "0.3.41"
 source = { editable = "../core" }
 dependencies = [
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@@ -816,7 +608,7 @@ dev = [
 ]
 lint = [{ name = "ruff", specifier = ">=0.9.2,<1.0.0" }]
 test = [
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+    { name = "blockbuster", specifier = "~=1.5.18" },
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     { name = "grandalf", specifier = ">=0.8,<1.0" },
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-]
-
 [[package]]
 name = "zstandard"
 version = "0.23.0"

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

@@ -43,27 +43,27 @@ class JiraToolkit(BaseToolkit):
         operations: List[Dict] = [
             {
                 "mode": "jql",
-                "name": "JQL Query",
+                "name": "jql_query",
                 "description": JIRA_JQL_PROMPT,
             },
             {
                 "mode": "get_projects",
-                "name": "Get Projects",
+                "name": "get_projects",
                 "description": JIRA_GET_ALL_PROJECTS_PROMPT,
             },
             {
                 "mode": "create_issue",
-                "name": "Create Issue",
+                "name": "create_issue",
                 "description": JIRA_ISSUE_CREATE_PROMPT,
             },
             {
                 "mode": "other",
-                "name": "Catch all Jira API call",
+                "name": "catch_all_jira_api",
                 "description": JIRA_CATCH_ALL_PROMPT,
             },
             {
                 "mode": "create_page",
-                "name": "Create confluence page",
+                "name": "create_confluence_page",
                 "description": JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
             },
         ]

libs/community/langchain_community/callbacks/bedrock_anthropic_callback.py

@@ -11,6 +11,7 @@ MODEL_COST_PER_1K_INPUT_TOKENS = {
     "anthropic.claude-3-sonnet-20240229-v1:0": 0.003,
     "anthropic.claude-3-5-sonnet-20240620-v1:0": 0.003,
     "anthropic.claude-3-5-sonnet-20241022-v2:0": 0.003,
+    "anthropic.claude-3-7-sonnet-20250219-v1:0": 0.003,
     "anthropic.claude-3-haiku-20240307-v1:0": 0.00025,
     "anthropic.claude-3-opus-20240229-v1:0": 0.015,
     "anthropic.claude-3-5-haiku-20241022-v1:0": 0.0008,
@@ -23,6 +24,7 @@ MODEL_COST_PER_1K_OUTPUT_TOKENS = {
     "anthropic.claude-3-sonnet-20240229-v1:0": 0.015,
     "anthropic.claude-3-5-sonnet-20240620-v1:0": 0.015,
     "anthropic.claude-3-5-sonnet-20241022-v2:0": 0.015,
+    "anthropic.claude-3-7-sonnet-20250219-v1:0": 0.015,
     "anthropic.claude-3-haiku-20240307-v1:0": 0.00125,
     "anthropic.claude-3-opus-20240229-v1:0": 0.075,
     "anthropic.claude-3-5-haiku-20241022-v1:0": 0.004,

libs/community/langchain_community/chat_models/deepinfra.py

@@ -214,9 +214,10 @@ class ChatDeepInfra(BaseChatModel):
     deepinfra_api_token: Optional[str] = None
     request_timeout: Optional[float] = Field(default=None, alias="timeout")
     temperature: Optional[float] = 1
-    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Run inference with this temperature. Must be in the closed
        interval [0.0, 1.0]."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for API call not explicitly specified."""
     top_p: Optional[float] = None
     """Decode using nucleus sampling: consider the smallest set of tokens whose
        probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""

libs/community/langchain_community/chat_models/litellm.py

@@ -260,9 +260,10 @@ class ChatLiteLLM(BaseChatModel):
     custom_llm_provider: Optional[str] = None
     request_timeout: Optional[Union[float, Tuple[float, float]]] = None
     temperature: Optional[float] = 1
-    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Run inference with this temperature. Must be in the closed
        interval [0.0, 1.0]."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for API call not explicitly specified."""
     top_p: Optional[float] = None
     """Decode using nucleus sampling: consider the smallest set of tokens whose
        probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""

libs/community/langchain_community/chat_models/mlx.py

@@ -160,6 +160,7 @@ class ChatMLX(BaseChatModel):
 
         try:
             import mlx.core as mx
+            from mlx_lm.sample_utils import make_logits_processors, make_sampler
             from mlx_lm.utils import generate_step
 
         except ImportError:
@@ -176,6 +177,9 @@ class ChatMLX(BaseChatModel):
         repetition_context_size: Optional[int] = model_kwargs.get(
             "repetition_context_size", None
         )
+        top_p: float = model_kwargs.get("top_p", 1.0)
+        min_p: float = model_kwargs.get("min_p", 0.0)
+        min_tokens_to_keep: int = model_kwargs.get("min_tokens_to_keep", 1)
 
         llm_input = self._to_chat_prompt(messages, tokenize=True, return_tensors="np")
 
@@ -183,13 +187,18 @@ class ChatMLX(BaseChatModel):
 
         eos_token_id = self.tokenizer.eos_token_id
 
+        sampler = make_sampler(temp or 0.0, top_p, min_p, min_tokens_to_keep)
+
+        logits_processors = make_logits_processors(
+            None, repetition_penalty, repetition_context_size
+        )
+
         for (token, prob), n in zip(
             generate_step(
                 prompt_tokens,
                 self.llm.model,
-                temp=temp,
-                repetition_penalty=repetition_penalty,
-                repetition_context_size=repetition_context_size,
+                sampler=sampler,
+                logits_processors=logits_processors,
             ),
             range(max_new_tokens),
         ):

libs/community/langchain_community/chat_models/perplexity.py

@@ -38,6 +38,7 @@ from langchain_core.messages import (
     SystemMessageChunk,
     ToolMessageChunk,
 )
+from langchain_core.messages.ai import UsageMetadata
 from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
@@ -59,6 +60,17 @@ def _is_pydantic_class(obj: Any) -> bool:
     return isinstance(obj, type) and is_basemodel_subclass(obj)
 
 
+def _create_usage_metadata(token_usage: dict) -> UsageMetadata:
+    input_tokens = token_usage.get("prompt_tokens", 0)
+    output_tokens = token_usage.get("completion_tokens", 0)
+    total_tokens = token_usage.get("total_tokens", input_tokens + output_tokens)
+    return UsageMetadata(
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        total_tokens=total_tokens,
+    )
+
+
 class ChatPerplexity(BaseChatModel):
     """`Perplexity AI` Chat models API.
 
@@ -238,9 +250,27 @@ class ChatPerplexity(BaseChatModel):
             messages=message_dicts, stream=True, **params
         )
         first_chunk = True
+        prev_total_usage: Optional[UsageMetadata] = None
         for chunk in stream_resp:
             if not isinstance(chunk, dict):
                 chunk = chunk.dict()
+            # Collect standard usage metadata (transform from aggregate to delta)
+            if total_usage := chunk.get("usage"):
+                lc_total_usage = _create_usage_metadata(total_usage)
+                if prev_total_usage:
+                    usage_metadata: Optional[UsageMetadata] = {
+                        "input_tokens": lc_total_usage["input_tokens"]
+                        - prev_total_usage["input_tokens"],
+                        "output_tokens": lc_total_usage["output_tokens"]
+                        - prev_total_usage["output_tokens"],
+                        "total_tokens": lc_total_usage["total_tokens"]
+                        - prev_total_usage["total_tokens"],
+                    }
+                else:
+                    usage_metadata = lc_total_usage
+                prev_total_usage = lc_total_usage
+            else:
+                usage_metadata = None
             if len(chunk["choices"]) == 0:
                 continue
             choice = chunk["choices"][0]
@@ -249,6 +279,8 @@ class ChatPerplexity(BaseChatModel):
             chunk = self._convert_delta_to_message_chunk(
                 choice["delta"], default_chunk_class
             )
+            if isinstance(chunk, AIMessageChunk) and usage_metadata:
+                chunk.usage_metadata = usage_metadata
             if first_chunk:
                 chunk.additional_kwargs |= {"citations": citations}
                 first_chunk = False
@@ -278,9 +310,15 @@ class ChatPerplexity(BaseChatModel):
         message_dicts, params = self._create_message_dicts(messages, stop)
         params = {**params, **kwargs}
         response = self.client.chat.completions.create(messages=message_dicts, **params)
+        if usage := getattr(response, "usage", None):
+            usage_metadata = _create_usage_metadata(usage.model_dump())
+        else:
+            usage_metadata = None
+
         message = AIMessage(
             content=response.choices[0].message.content,
             additional_kwargs={"citations": response.citations},
+            usage_metadata=usage_metadata,
         )
         return ChatResult(generations=[ChatGeneration(message=message)])
 

libs/community/langchain_community/chat_models/tongyi.py

@@ -123,6 +123,8 @@ def convert_dict_to_message(
                             tool_calls.append(parsed_tool)
                     except Exception as e:
                         invalid_tool_calls.append(make_invalid_tool_call(value, str(e)))
+        elif "partial" in _dict and isinstance(_dict["partial"], bool):
+            additional_kwargs = {"partial": _dict["partial"]}
         else:
             additional_kwargs = {}
 
@@ -204,6 +206,9 @@ def convert_message_to_dict(message: BaseMessage) -> dict:
         message_dict = {"role": "assistant", "content": message.content}
         if "tool_calls" in message.additional_kwargs:
             message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
+        # support Partial Mode for text continuation
+        if "partial" in message.additional_kwargs:
+            message_dict["partial"] = message.additional_kwargs["partial"]
     elif isinstance(message, SystemMessage):
         message_dict = {"role": "system", "content": message.content}
     elif isinstance(message, ToolMessage):
@@ -778,8 +783,6 @@ class ChatTongyi(BaseChatModel):
         ]
         if len(system_message_indices) == 1 and system_message_indices[0] != 0:
             raise ValueError("System message can only be the first message.")
-        elif len(system_message_indices) > 1:
-            raise ValueError("There can be only one system message at most.")
 
         params["messages"] = message_dicts
 

libs/community/langchain_community/document_loaders/url_playwright.py

@@ -177,6 +177,8 @@ class PlaywrightURLLoader(BaseLoader):
                     if response is None:
                         raise ValueError(f"page.goto() returned None for url {url}")
 
+                    page.wait_for_load_state("load")
+
                     text = self.evaluator.evaluate(page, browser, response)
                     metadata = {"source": url}
                     yield Document(page_content=text, metadata=metadata)
@@ -216,6 +218,8 @@ class PlaywrightURLLoader(BaseLoader):
                     if response is None:
                         raise ValueError(f"page.goto() returned None for url {url}")
 
+                    await page.wait_for_load_state("load")
+
                     text = await self.evaluator.evaluate_async(page, browser, response)
                     metadata = {"source": url}
                     yield Document(page_content=text, metadata=metadata)

libs/community/langchain_community/embeddings/ascend.py

@@ -30,6 +30,7 @@ class AscendEmbeddings(Embeddings, BaseModel):
     document_instruction: str = ""
     use_fp16: bool = True
     pooling_method: Optional[str] = "cls"
+    batch_size: int = 32
     model: Any
     tokenizer: Any
 
@@ -119,7 +120,18 @@ class AscendEmbeddings(Embeddings, BaseModel):
             )
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        return self.encode([self.document_instruction + text for text in texts])
+        try:
+            import numpy as np
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import numpy, please install with `pip install -U numpy`."
+            ) from e
+        embedding_list = []
+        for i in range(0, len(texts), self.batch_size):
+            texts_ = texts[i : i + self.batch_size]
+            emb = self.encode([self.document_instruction + text for text in texts_])
+            embedding_list.append(emb)
+        return np.concatenate(embedding_list)
 
     def embed_query(self, text: str) -> List[float]:
         return self.encode([self.query_instruction + text])[0]

libs/community/langchain_community/embeddings/llamacpp.py

@@ -139,7 +139,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
             Embeddings for the text.
         """
         embedding = self.client.embed(text)
-        if not isinstance(embedding, list):
-            return list(map(float, embedding))
-        else:
+        if embedding and isinstance(embedding, list) and isinstance(embedding[0], list):
             return list(map(float, embedding[0]))
+        else:
+            return list(map(float, embedding))

libs/community/langchain_community/graph_vectorstores/__init__.py

@@ -34,6 +34,7 @@ Example::
     - :class:`How to link Documents on hyperlinks in HTML <langchain_community.graph_vectorstores.extractors.html_link_extractor.HtmlLinkExtractor>`
     - :class:`How to link Documents on common keywords (using KeyBERT) <langchain_community.graph_vectorstores.extractors.keybert_link_extractor.KeybertLinkExtractor>`
     - :class:`How to link Documents on common named entities (using GliNER) <langchain_community.graph_vectorstores.extractors.gliner_link_extractor.GLiNERLinkExtractor>`
+    - `langchain-jieba: link extraction tailored for Chinese language <https://github.com/cqzyys/langchain-jieba>`_
 
 Get started
 -----------

libs/community/langchain_community/graph_vectorstores/visualize.py

@@ -20,7 +20,7 @@ _EDGE_DIRECTION = {
     "bidir": "both",
 }
 
-_WORD_RE = re.compile("\s*\S+")
+_WORD_RE = re.compile(r"\s*\S+")
 
 
 def _split_prefix(s: str, max_chars: int = 50) -> str:

libs/community/langchain_community/llms/mlx_pipeline.py

@@ -101,7 +101,9 @@ class MLXPipeline(LLM):
 
         tokenizer_config = tokenizer_config or {}
         if adapter_file:
-            model, tokenizer = load(model_id, tokenizer_config, adapter_file, lazy)
+            model, tokenizer = load(
+                model_id, tokenizer_config, adapter_path=adapter_file, lazy=lazy
+            )
         else:
             model, tokenizer = load(model_id, tokenizer_config, lazy=lazy)
 
@@ -141,6 +143,7 @@ class MLXPipeline(LLM):
     ) -> str:
         try:
             from mlx_lm import generate
+            from mlx_lm.sample_utils import make_logits_processors, make_sampler
 
         except ImportError:
             raise ImportError(
@@ -161,18 +164,23 @@ class MLXPipeline(LLM):
             "repetition_context_size", None
         )
         top_p: float = pipeline_kwargs.get("top_p", 1.0)
+        min_p: float = pipeline_kwargs.get("min_p", 0.0)
+        min_tokens_to_keep: int = pipeline_kwargs.get("min_tokens_to_keep", 1)
+
+        sampler = make_sampler(temp, top_p, min_p, min_tokens_to_keep)
+        logits_processors = make_logits_processors(
+            None, repetition_penalty, repetition_context_size
+        )
 
         return generate(
             model=self.model,
             tokenizer=self.tokenizer,
             prompt=prompt,
-            temp=temp,
             max_tokens=max_tokens,
             verbose=verbose,
             formatter=formatter,
-            repetition_penalty=repetition_penalty,
-            repetition_context_size=repetition_context_size,
-            top_p=top_p,
+            sampler=sampler,
+            logits_processors=logits_processors,
         )
 
     def _stream(
@@ -184,6 +192,7 @@ class MLXPipeline(LLM):
     ) -> Iterator[GenerationChunk]:
         try:
             import mlx.core as mx
+            from mlx_lm.sample_utils import make_logits_processors, make_sampler
             from mlx_lm.utils import generate_step
 
         except ImportError:
@@ -203,6 +212,8 @@ class MLXPipeline(LLM):
             "repetition_context_size", None
         )
         top_p: float = pipeline_kwargs.get("top_p", 1.0)
+        min_p: float = pipeline_kwargs.get("min_p", 0.0)
+        min_tokens_to_keep: int = pipeline_kwargs.get("min_tokens_to_keep", 1)
 
         prompt = self.tokenizer.encode(prompt, return_tensors="np")
 
@@ -212,14 +223,18 @@ class MLXPipeline(LLM):
         detokenizer = self.tokenizer.detokenizer
         detokenizer.reset()
 
+        sampler = make_sampler(temp or 0.0, top_p, min_p, min_tokens_to_keep)
+
+        logits_processors = make_logits_processors(
+            None, repetition_penalty, repetition_context_size
+        )
+
         for (token, prob), n in zip(
             generate_step(
                 prompt=prompt_tokens,
                 model=self.model,
-                temp=temp,
-                repetition_penalty=repetition_penalty,
-                repetition_context_size=repetition_context_size,
-                top_p=top_p,
+                sampler=sampler,
+                logits_processors=logits_processors,
             ),
             range(max_new_tokens),
         ):

libs/community/langchain_community/llms/openai.py

@@ -724,7 +724,7 @@ class BaseOpenAI(BaseLLM):
         Example:
             .. code-block:: python
 
-                max_tokens = openai.max_token_for_prompt("Tell me a joke.")
+                max_tokens = openai.max_tokens_for_prompt("Tell me a joke.")
         """
         num_tokens = self.get_num_tokens(prompt)
         return self.max_context_size - num_tokens

libs/community/langchain_community/llms/outlines.py

@@ -59,7 +59,7 @@ class Outlines(LLM):
     """Whether to stream the results, token by token."""
 
     regex: Optional[str] = None
-    """Regular expression for structured generation.
+    r"""Regular expression for structured generation.
     
     If provided, Outlines will guarantee that the generated text matches this regex.
     This can be useful for generating structured outputs like IP addresses, dates, etc.

libs/community/langchain_community/llms/tongyi.py

@@ -59,6 +59,7 @@ def check_response(resp: Any) -> Any:
         return resp
     elif resp["status_code"] in [400, 401]:
         raise ValueError(
+            f"request_id: {resp['request_id']} \n "
             f"status_code: {resp['status_code']} \n "
             f"code: {resp['code']} \n message: {resp['message']}"
         )

libs/community/langchain_community/llms/xinference.py

@@ -1,8 +1,10 @@
 from __future__ import annotations
 
+import json
 from typing import (
     TYPE_CHECKING,
     Any,
+    AsyncIterator,
     Dict,
     Generator,
     Iterator,
@@ -12,7 +14,12 @@ from typing import (
     Union,
 )
 
-from langchain_core.callbacks import CallbackManagerForLLMRun
+import aiohttp
+import requests
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
 from langchain_core.language_models.llms import LLM
 from langchain_core.outputs import GenerationChunk
 
@@ -126,6 +133,7 @@ class Xinference(LLM):
         self,
         server_url: Optional[str] = None,
         model_uid: Optional[str] = None,
+        api_key: Optional[str] = None,
         **model_kwargs: Any,
     ):
         try:
@@ -155,7 +163,13 @@ class Xinference(LLM):
         if self.model_uid is None:
             raise ValueError("Please provide the model UID")
 
-        self.client = RESTfulClient(server_url)
+        self._headers: Dict[str, str] = {}
+        self._cluster_authed = False
+        self._check_cluster_authenticated()
+        if api_key is not None and self._cluster_authed:
+            self._headers["Authorization"] = f"Bearer {api_key}"
+
+        self.client = RESTfulClient(server_url, api_key)
 
     @property
     def _llm_type(self) -> str:
@@ -171,6 +185,20 @@ class Xinference(LLM):
             **{"model_kwargs": self.model_kwargs},
         }
 
+    def _check_cluster_authenticated(self) -> None:
+        url = f"{self.server_url}/v1/cluster/auth"
+        response = requests.get(url)
+        if response.status_code == 404:
+            self._cluster_authed = False
+        else:
+            if response.status_code != 200:
+                raise RuntimeError(
+                    f"Failed to get cluster information, "
+                    f"detail: {response.json()['detail']}"
+                )
+            response_data = response.json()
+            self._cluster_authed = bool(response_data["auth"])
+
     def _call(
         self,
         prompt: str,
@@ -305,3 +333,61 @@ class Xinference(LLM):
                 return GenerationChunk(text=token)
         else:
             raise TypeError("stream_response type error!")
+
+    async def _astream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[GenerationChunk]:
+        generate_config = kwargs.get("generate_config", {})
+        generate_config = {**self.model_kwargs, **generate_config}
+        if stop:
+            generate_config["stop"] = stop
+        async for stream_resp in self._acreate_generate_stream(prompt, generate_config):
+            if stream_resp:
+                chunk = self._stream_response_to_generation_chunk(stream_resp)
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        chunk.text,
+                        verbose=self.verbose,
+                    )
+                yield chunk
+
+    async def _acreate_generate_stream(
+        self, prompt: str, generate_config: Optional[Dict[str, List[str]]] = None
+    ) -> AsyncIterator[str]:
+        request_body: Dict[str, Any] = {"model": self.model_uid, "prompt": prompt}
+        if generate_config is not None:
+            for key, value in generate_config.items():
+                request_body[key] = value
+
+        stream = bool(generate_config and generate_config.get("stream"))
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                url=f"{self.server_url}/v1/completions",
+                json=request_body,
+            ) as response:
+                if response.status != 200:
+                    if response.status == 404:
+                        raise FileNotFoundError(
+                            "astream call failed with status code 404."
+                        )
+                    else:
+                        optional_detail = response.text
+                        raise ValueError(
+                            f"astream call failed with status code {response.status}."
+                            f" Details: {optional_detail}"
+                        )
+
+                async for line in response.content:
+                    if not stream:
+                        yield json.loads(line)
+                    else:
+                        json_str = line.decode("utf-8")
+                        if line.startswith(b"data:"):
+                            json_str = json_str[len(b"data:") :].strip()
+                            if not json_str:
+                                continue
+                            yield json.loads(json_str)

libs/community/langchain_community/retrievers/tavily_search_api.py

@@ -123,7 +123,7 @@ class TavilySearchAPIRetriever(BaseRetriever):
             Document(
                 page_content=result.get("content", "")
                 if not self.include_raw_content
-                else result.get("raw_content", ""),
+                else (result.get("raw_content") or ""),
                 metadata={
                     "title": result.get("title", ""),
                     "source": result.get("url", ""),

libs/community/langchain_community/tools/tavily_search/tool.py

@@ -51,9 +51,12 @@ class TavilySearchResults(BaseTool):  # type: ignore[override, override]
 
             tool.invoke({'query': 'who won the last french open'})
 
-        .. code-block:: python
+        .. code-block:: json
 
-            '{\n  "url": "https://www.nytimes.com...", "content": "Novak Djokovic won the last French Open by beating Casper Ruud ...'
+            {
+                "url": "https://www.nytimes.com...",
+                "content": "Novak Djokovic won the last French Open by beating Casper Ruud ..."
+            }
 
     Invoke with tool call:
 
@@ -64,7 +67,7 @@ class TavilySearchResults(BaseTool):  # type: ignore[override, override]
         .. code-block:: python
 
             ToolMessage(
-                content='{\n  "url": "https://www.nytimes.com...", "content": "Novak Djokovic won the last French Open by beating Casper Ruud ...',
+                content='{ "url": "https://www.nytimes.com...", "content": "Novak Djokovic won the last French Open by beating Casper Ruud ..." }',
                 artifact={
                     'query': 'who won the last french open',
                     'follow_up_questions': None,

libs/community/langchain_community/utilities/jira.py

@@ -40,6 +40,9 @@ class JiraAPIWrapper(BaseModel):
         )
         values["jira_instance_url"] = jira_instance_url
 
+        if "jira_cloud" in values and values["jira_cloud"] is not None:
+            values["jira_cloud"] = str(values["jira_cloud"])
+
         jira_cloud_str = get_from_dict_or_env(values, "jira_cloud", "JIRA_CLOUD")
         jira_cloud = jira_cloud_str.lower() == "true"
         values["jira_cloud"] = jira_cloud

libs/community/langchain_community/utilities/tavily_search.py

@@ -176,10 +176,13 @@ class TavilySearchAPIWrapper(BaseModel):
         """Clean results from Tavily Search API."""
         clean_results = []
         for result in results:
-            clean_results.append(
-                {
-                    "url": result["url"],
-                    "content": result["content"],
-                }
-            )
+            clean_result = {
+                "title": result["title"],
+                "url": result["url"],
+                "content": result["content"],
+                "score": result["score"],
+            }
+            if raw_content := result.get("raw_content"):
+                clean_result["raw_content"] = raw_content
+            clean_results.append(clean_result)
         return clean_results