[Core] Tool function parsing

Support list, dict, set

.github/actions/people/app/main.py

@@ -547,7 +547,6 @@ if __name__ == "__main__":
         "obi1kenobi",
         "langchain-infra",
         "jacoblee93",
-        "isahers1",
         "dqbd",
         "bracesproul",
         "akira",

.github/workflows/_release.yml

@@ -135,7 +135,6 @@ jobs:
       - release-notes
     uses:
       ./.github/workflows/_test_release.yml
-    permissions: write-all
     with:
       working-directory: ${{ inputs.working-directory }}
       dangerous-nonmaster-release: ${{ inputs.dangerous-nonmaster-release }}
@@ -203,7 +202,7 @@ jobs:
           poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with test
+        run: poetry install --with test,test_integration
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the test PyPI version.
@@ -246,10 +245,6 @@ jobs:
         with:
           credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
 
-      - name: Import integration test dependencies
-        run: poetry install --with test,test_integration
-        working-directory: ${{ inputs.working-directory }}
-
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:

.github/workflows/people.yml

@@ -16,7 +16,6 @@ jobs:
   langchain-people:
     if: github.repository_owner == 'langchain-ai'
     runs-on: ubuntu-latest
-    permissions: write-all
     steps:
       - name: Dump GitHub context
         env:

README.md

@@ -38,25 +38,24 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**:  Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/v0.2/docs/concepts#langchain-expression-language-lcel), [components](https://python.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/).
-Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
+- **Open-source libraries**: Build your applications using LangChain's [modular building blocks](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) and [components](https://python.langchain.com/v0.2/docs/concepts/#components). Integrate with hundreds of [third-party providers](https://python.langchain.com/v0.2/docs/integrations/platforms/).
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
-- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
+- **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/v0.2/docs/langserve/).
 
 ### Open-source libraries
 - **`langchain-core`**: Base abstractions and LangChain Expression Language.
 - **`langchain-community`**: Third party integrations.
   - Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
+- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
 
 ### Productionization:
 - **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
 
 ### Deployment:
-- **[LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
+- **[LangServe](https://python.langchain.com/v0.2/docs/langserve/)**: A library for deploying LangChain chains as REST APIs.
 
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_062024.svg "LangChain Architecture Overview")
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack.svg "LangChain Architecture Overview")
 
 ## 🧱 What can you build with LangChain?
 
@@ -107,7 +106,7 @@ Retrieval Augmented Generation involves [loading data](https://python.langchain.
 
 **🤖 Agents**
 
-Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
+Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents) along with the [LangGraph](https://github.com/langchain-ai/langgraph) extension for building custom agents.
 
 ## 📖 Documentation
 
@@ -121,9 +120,10 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 ## 🌐 Ecosystem
 
-- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
-- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
-- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploy LangChain runnables and chains as REST APIs.
+- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production.
+- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
+- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs.
+  - [LangChain Templates](https://python.langchain.com/v0.2/docs/templates/): Example applications hosted with LangServe.
 
 
 ## 💁 Contributing

docs/Makefile

@@ -61,7 +61,7 @@ render:
 	$(PYTHON) scripts/notebook_convert.py $(INTERMEDIATE_DIR) $(OUTPUT_NEW_DOCS_DIR)
 
 md-sync:
-	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
+	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
 generate-references:
 	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(OUTPUT_NEW_DOCS_DIR)

docs/api_reference/templates/runnable_non_pydantic.rst

@@ -3,7 +3,6 @@
 
 .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 
-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
 
 .. currentmodule:: {{ module }}
 

docs/api_reference/templates/runnable_pydantic.rst

@@ -3,8 +3,6 @@
 
 .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 
-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
-
 .. currentmodule:: {{ module }}
 
 .. autopydantic_model:: {{ objname }}
@@ -19,6 +17,6 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, invoke, ainvoke, batch, abatch, batch_as_completed, abatch_as_completed, astream_log, stream, astream, astream_events, transform, atransform, get_output_schema, get_prompts, configurable_fields, configurable_alternatives, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign
 
 .. example_links:: {{ objname }}

docs/docs/concepts.mdx

@@ -51,8 +51,8 @@ A developer platform that lets you debug, test, evaluate, and monitor LLM applic
 <ThemedImage
   alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
   sources={{
-    light: useBaseUrl('/svg/langchain_stack_062024.svg'),
-    dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
+    light: useBaseUrl('/svg/langchain_stack.svg'),
+    dark: useBaseUrl('/svg/langchain_stack_dark.svg'),
   }}
   title="LangChain Framework Overview"
 />
@@ -89,7 +89,7 @@ With LCEL, **all** steps are automatically logged to [LangSmith](https://docs.sm
 Any chain created with LCEL can be easily deployed using [LangServe](/docs/langserve).
 
 ### Runnable interface
-<span data-heading-keywords="invoke,runnable"></span>
+<span data-heading-keywords="invoke"></span>
 
 To make it as easy as possible to create custom chains, we've implemented a ["Runnable"](https://api.python.langchain.com/en/stable/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.
 
@@ -179,15 +179,8 @@ For a full list of LangChain model providers with multimodal models, [check out
 ### LLMs
 <span data-heading-keywords="llm,llms"></span>
 
-:::caution
-Pure text-in/text-out LLMs tend to be older or lower-level. Many popular models are best used as [chat completion models](/docs/concepts/#chat-models),
-even for non-chat use cases.
-
-You are probably looking for [the section above instead](/docs/concepts/#chat-models).
-:::
-
 Language models that takes a string as input and returns a string.
-These are traditionally older models (newer models generally are [Chat Models](/docs/concepts/#chat-models), see above).
+These are traditionally older models (newer models generally are [Chat Models](/docs/concepts/#chat-models), see below).
 
 Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input.
 This gives them the same interface as [Chat Models](/docs/concepts/#chat-models).
@@ -550,28 +543,6 @@ If you are still using AgentExecutor, do not fear: we still have a guide on [how
 It is recommended, however, that you start to transition to LangGraph.
 In order to assist in this we have put together a [transition guide on how to do so](/docs/how_to/migrate_agent).
 
-#### ReAct agents
-<span data-heading-keywords="react,react agent"></span>
-
-One popular architecture for building agents is [**ReAct**](https://arxiv.org/abs/2210.03629).
-ReAct combines reasoning and acting in an iterative process - in fact the name "ReAct" stands for "Reason" and "Act".
-
-The general flow looks like this:
-
-- The model will "think" about what step to take in response to an input and any previous observations.
-- The model will then choose an action from available tools (or choose to respond to the user).
-- The model will generate arguments to that tool.
-- The agent runtime (executor) will parse out the chosen tool and call it with the generated arguments.
-- The executor will return the results of the tool call back to the model as an observation.
-- This process repeats until the agent chooses to respond.
-
-There are general prompting based implementations that do not require any model-specific features, but the most
-reliable implementations use features like [tool calling](/docs/how_to/tool_calling/) to reliably format outputs
-and reduce variance.
-
-Please see the [LangGraph documentation](https://langchain-ai.github.io/langgraph/) for more information,
-or [this how-to guide](/docs/how_to/migrate_agent/) for specific information on migrating to LangGraph.
-
 ### Callbacks
 
 LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
@@ -897,7 +868,7 @@ The standard interface consists of:
 The following how-to guides are good practical resources for using function/tool calling:
 
 - [How to return structured data from an LLM](/docs/how_to/structured_output/)
-- [How to use a model to call tools](/docs/how_to/tool_calling)
+- [How to use a model to call tools](/docs/how_to/tool_calling/)
 
 For a full list of model providers that support tool calling, [see this table](/docs/integrations/chat/#advanced-features).
 
@@ -1055,7 +1026,7 @@ See several videos and cookbooks showcasing RAG with LangGraph:
 - [Cookbooks for RAG using LangGraph](https://github.com/langchain-ai/langgraph/tree/main/examples/rag)
 
 See our LangGraph RAG recipes with partners:
-- [Meta](https://github.com/meta-llama/llama-recipes/tree/main/recipes/3p_integrations/langchain)
+- [Meta](https://github.com/meta-llama/llama-recipes/tree/main/recipes/use_cases/agents/langchain) 
 - [Mistral](https://github.com/mistralai/cookbook/tree/main/third_party/langchain)
 
 :::
@@ -1101,13 +1072,3 @@ This process is vital for building reliable applications.
 - It allows you to track results over time and automatically run your evaluators on a schedule or as part of CI/Code
 
 To learn more, check out [this LangSmith guide](https://docs.smith.langchain.com/concepts/evaluation).
-
-### Tracing
-<span data-heading-keywords="trace,tracing"></span>
-
-A trace is essentially a series of steps that your application takes to go from input to output.
-Traces contain individual steps called `runs`. These can be individual calls from a model, retriever,
-tool, or sub-chains.
-Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.
-
-For a deeper dive, check out [this LangSmith conceptual guide](https://docs.smith.langchain.com/concepts/tracing).

docs/docs/contributing/code.mdx

@@ -1,9 +1,36 @@
-# Setup
+---
+sidebar_position: 1
+---
+# Contribute Code
 
-This guide walks through how to run the repository locally and check in your first code.
+To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
+Please do not try to push directly to this repo unless you are a maintainer.
+
+Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
+maintainers.
+
+Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
+[Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
+
+It's essential that we maintain great documentation and testing. If you:
+- Fix a bug
+  - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
+- Make an improvement
+  - Update any affected example notebooks and documentation. These live in `docs`.
+  - Update unit and integration tests when relevant.
+- Add a feature
+  - Add a demo notebook in `docs/docs/`.
+  - Add unit and integration tests.
+
+We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
+best way to get our attention.
+
+## 🚀 Quick Start
+
+This quick start guide explains how to run the repository locally.
 For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer).
 
-## Dependency Management: Poetry and other env/dependency managers
+### Dependency Management: Poetry and other env/dependency managers
 
 This project utilizes [Poetry](https://python-poetry.org/) v1.7.1+ as a dependency manager.
 
@@ -14,7 +41,7 @@ Install Poetry: **[documentation on how to install it](https://python-poetry.org
 ❗Note: If you use `Conda` or `Pyenv` as your environment/package manager, after installing Poetry,
 tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
 
-## Different packages
+### Different packages
 
 This repository contains multiple packages:
 - `langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
@@ -32,7 +59,7 @@ For this quickstart, start with langchain-community:
 cd libs/community
 ```
 
-## Local Development Dependencies
+### Local Development Dependencies
 
 Install langchain-community development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
 
@@ -52,9 +79,9 @@ If you are still seeing this bug on v1.6.1+, you may also try disabling "modern
 (`poetry config installer.modern-installation false`) and re-installing requirements.
 See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-## Testing
+### Testing
 
-**Note:** In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional. See the following section about optional dependencies.
+_In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional; see section about optional dependencies_.
 
 Unit tests cover modular logic that does not require calls to outside APIs.
 If you add new logic, please add a unit test.
@@ -91,11 +118,11 @@ poetry install --with test
 make test
 ```
 
-## Formatting and Linting
+### Formatting and Linting
 
 Run these locally before submitting a PR; the CI system will check also.
 
-### Code Formatting
+#### Code Formatting
 
 Formatting for this project is done via [ruff](https://docs.astral.sh/ruff/rules/).
 
@@ -147,7 +174,7 @@ This can be very helpful when you've made changes to only certain parts of the p
 
 We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
 
-### Spellcheck
+#### Spellcheck
 
 Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
 Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.

docs/docs/contributing/code/guidelines.mdx

@@ -1,35 +0,0 @@
-# General guidelines
-
-Here are some things to keep in mind for all types of contributions:
-
-- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
-- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
-- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
-  - If you would like comments or feedback on your current progress, please open an issue or discussion and tag a maintainer.
-  - See the sections on [Testing](/docs/contributing/code/setup#testing) and [Formatting and Linting](/docs/contributing/code/setup#formatting-and-linting) for how to run these checks locally.
-- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
-- Look for duplicate PRs or issues that have already been opened before opening a new one.
-- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
-
-## Bugfixes
-
-We encourage and appreciate bugfixes. We ask that you:
-
-- Explain the bug in enough detail for maintainers to be able to reproduce it.
-  - If an accompanying issue exists, link to it. Prefix with `Fixes` so that the issue will close automatically when the PR is merged.
-- Avoid breaking changes if possible.
-- Include unit tests that fail without the bugfix.
-
-If you come across a bug and don't know how to fix it, we ask that you open an issue for it describing in detail the environment in which you encountered the bug.
-
-## New features
-
-We aim to keep the bar high for new features. We generally don't accept new core abstractions, changes to infra, changes to dependencies,
-or new agents/chains from outside contributors without an existing GitHub discussion or issue that demonstrates an acute need for them.
-
-- New features must come with docs, unit tests, and (if appropriate) integration tests.
-- New integrations must come with docs, unit tests, and (if appropriate) integration tests.
-  - See [this page](/docs/contributing/integrations) for more details on contributing new integrations.
-- New functionality should not inherit from or use deprecated methods or classes.
-- We will reject features that are likely to lead to security vulnerabilities or reports.
-- Do not add any hard dependencies. Integrations may add optional dependencies.

docs/docs/contributing/code/index.mdx

@@ -1,6 +0,0 @@
-# Contribute Code
-
-If you would like to add a new feature or update an existing one, please read the resources below before getting started:
-
-- [General guidelines](/docs/contributing/code/guidelines/)
-- [Setup](/docs/contributing/code/setup/)

docs/docs/contributing/documentation/_category_.yml

@@ -0,0 +1,2 @@
+label: 'Documentation'
+position: 3

docs/docs/contributing/documentation/index.mdx

@@ -1,7 +0,0 @@
-# Contribute Documentation
-
-Documentation is a vital part of LangChain. We welcome both new documentation for new features and 
-community improvements to our current documentation. Please read the resources below before getting started:
-
-- [Documentation style guide](/docs/contributing/documentation/style_guide/)
-- [Setup](/docs/contributing/documentation/setup/)

docs/docs/contributing/documentation/style_guide.mdx

@@ -1,8 +1,10 @@
 ---
-sidebar_class_name: "hidden"
+sidebar_label: "Style guide"
 ---
 
-# Documentation Style Guide
+# LangChain Documentation Style Guide
+
+## Introduction
 
 As LangChain continues to grow, the surface area of documentation required to cover it continues to grow too.
 This page provides guidelines for anyone writing documentation for LangChain, as well as some of our philosophies around
@@ -10,137 +12,116 @@ organization and structure.
 
 ## Philosophy
 
-LangChain's documentation follows the [Diataxis framework](https://diataxis.fr).
-Under this framework, all documentation falls under one of four categories: [Tutorials](/docs/contributing/documentation/style_guide/#tutorials),
-[How-to guides](/docs/contributing/documentation/style_guide/#how-to-guides),
-[References](/docs/contributing/documentation/style_guide/#references), and [Explanations](/docs/contributing/documentation/style_guide/#conceptual-guide).
+LangChain's documentation aspires to follow the [Diataxis framework](https://diataxis.fr).
+Under this framework, all documentation falls under one of four categories:
 
-### Tutorials
-
-Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
-gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way. They should **avoid** giving
-multiple permutations of ways to achieve that goal in-depth. Instead, it should guide a new user through a recommended path to accomplishing the tutorial's goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction. Information on how to address additional scenarios
-belongs in how-to guides.
-
-To quote the Diataxis website:
-
-> A tutorial serves the user’s *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
-
-In LangChain, these are often higher level guides that show off end-to-end use cases.
-
-Some examples include:
-
-- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain/)
-- [Build a Retrieval Augmented Generation (RAG) App](/docs/tutorials/rag/)
-  
-Here are some high-level tips on writing a good tutorial:
-
-- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
-- Be specific, not abstract and follow one path.
-  - No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
-- Get "a point on the board" as soon as possible - something the user can run that outputs something.
-  - You can iterate and expand afterwards.
-  - Try to frequently checkpoint at given steps where the user can run code and see progress.
-- Focus on results, not technical explanation.
-  - Crosslink heavily to appropriate conceptual/reference pages.
-- The first time you mention a LangChain concept, use its full name (e.g. "LangChain Expression Language (LCEL)"), and link to its conceptual/other documentation page.
-  - It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
-- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
-  
-### How-to guides
-
-A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
-It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
-should still give some background or list the scenarios where the information contained within can be relevant.
-They can and should discuss alternatives if one approach may be better than another in certain cases.
-
-To quote the Diataxis website:
-
-> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
-
-Some examples include:
-
-- [How to: return structured data from a model](/docs/how_to/structured_output/)
-- [How to: write a custom chat model](/docs/how_to/custom_chat_model/)
-
-Here are some high-level tips on writing a good how-to guide:
-
-- Clearly explain what you are guiding the user through at the start.
-- Assume higher intent than a tutorial and show what the user needs to do to get that task done.
-- Assume familiarity of concepts, but explain why suggested actions are helpful.
-  - Crosslink heavily to conceptual/reference pages.
-- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem.
-- Use lots of example code.
-  - Prefer full code blocks that the reader can copy and run.
-- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides.
-
-### Conceptual guide
-
-LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
-in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
-gaining a deeper understanding of the framework. Try to avoid excessively large code examples - the goal here is to
-impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
-
-This guide on documentation style is meant to fall under this category.
-
-To quote the Diataxis website:
-
-> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
-
-Some examples include:
-
-- [Retrieval conceptual docs](/docs/concepts/#retrieval)
-- [Chat model conceptual docs](/docs/concepts/#chat-models)
-
-Here are some high-level tips on writing a good conceptual guide:
-
-- Explain design decisions. Why does concept X exist and why was it designed this way?
-- Use analogies and reference other concepts and alternatives
-- Avoid blending in too much reference content
-- You can and should reference content covered in other guides, but make sure to link to them
-
-### References
-
-References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
-In LangChain, this is mainly our API reference pages, which are populated from docstrings within code.
-References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
-how to use something specific.
-
-To quote the Diataxis website:
-
-> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
-
-Many of the reference pages in LangChain are automatically generated from code,
-but here are some high-level tips on writing a good docstring:
-
-- Be concise
-- Discuss special cases and deviations from a user's expectations
-- Go into detail on required inputs and outputs
-- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
+- **Tutorials**: Lessons that take the reader by the hand through a series of conceptual steps to complete a project.
+  - An example of this is our [LCEL streaming guide](/docs/how_to/streaming).
+  - Our guides on [custom components](/docs/how_to/custom_chat_model) is another one.
+- **How-to guides**: Guides that take the reader through the steps required to solve a real-world problem.
+  - The clearest examples of this are our [Use case](/docs/how_to#use-cases) quickstart pages.
+- **Reference**: Technical descriptions of the machinery and how to operate it.
+  - Our [Runnable interface](/docs/concepts#interface) page is an example of this.
+  - The [API reference pages](https://api.python.langchain.com/) are another.
+- **Explanation**: Explanations that clarify and illuminate a particular topic.
+  - The [LCEL primitives pages](/docs/how_to/sequence) are an example of this.
 
 Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
 
-## General guidelines
+## Taxonomy
+
+Keeping the above in mind, we have sorted LangChain's docs into categories. It is helpful to think in these terms
+when contributing new documentation:
+
+### Getting started
+
+The [getting started section](/docs/introduction) includes a high-level introduction to LangChain, a quickstart that
+tours LangChain's various features, and logistical instructions around installation and project setup.
+
+It contains elements of **How-to guides** and **Explanations**.
+
+### Use cases
+
+[Use cases](/docs/how_to#use-cases) are guides that are meant to show how to use LangChain to accomplish a specific task (RAG, information extraction, etc.).
+The quickstarts should be good entrypoints for first-time LangChain developers who prefer to learn by getting something practical prototyped,
+then taking the pieces apart retrospectively. These should mirror what LangChain is good at.
+
+The quickstart pages here should fit the **How-to guide** category, with the other pages intended to be **Explanations** of more
+in-depth concepts and strategies that accompany the main happy paths.
+
+:::note
+The below sections are listed roughly in order of increasing level of abstraction.
+:::
+
+### Expression Language
+
+[LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language-lcel) is the fundamental way that most LangChain components fit together, and this section is designed to teach
+developers how to use it to build with LangChain's primitives effectively.
+
+This section should contains **Tutorials** that teach how to stream and use LCEL primitives for more abstract tasks, **Explanations** of specific behaviors,
+and some **References** for how to use different methods in the Runnable interface.
+
+### Components
+
+The [components section](/docs/concepts) covers concepts one level of abstraction higher than LCEL.
+Abstract base classes like `BaseChatModel` and `BaseRetriever` should be covered here, as well as core implementations of these base classes,
+such as `ChatPromptTemplate` and `RecursiveCharacterTextSplitter`. Customization guides belong here too.
+
+This section should contain mostly conceptual **Tutorials**, **References**, and **Explanations** of the components they cover.
+
+:::note
+As a general rule of thumb, everything covered in the `Expression Language` and `Components` sections (with the exception of the `Composition` section of components) should
+cover only components that exist in `langchain_core`.
+:::
+
+### Integrations
+
+The [integrations](/docs/integrations/platforms/) are specific implementations of components. These often involve third-party APIs and services.
+If this is the case, as a general rule, these are maintained by the third-party partner.
+
+This section should contain mostly **Explanations** and **References**, though the actual content here is more flexible than other sections and more at the
+discretion of the third-party provider.
+
+:::note
+Concepts covered in `Integrations` should generally exist in `langchain_community` or specific partner packages.
+:::
+
+### Guides and Ecosystem
+
+The [Guides](/docs/tutorials) and [Ecosystem](https://docs.smith.langchain.com/) sections should contain guides that address higher-level problems than the sections above.
+This includes, but is not limited to, considerations around productionization and development workflows.
+
+These should contain mostly **How-to guides**, **Explanations**, and **Tutorials**.
+
+### API references
+
+LangChain's API references. Should act as **References** (as the name implies) with some **Explanation**-focused content as well. 
+
+## Sample developer journey
+
+We have set up our docs to assist a new developer to LangChain. Let's walk through the intended path:
+
+- The developer lands on https://python.langchain.com, and reads through the introduction and the diagram.
+- If they are just curious, they may be drawn to the [Quickstart](/docs/tutorials/llm_chain) to get a high-level tour of what LangChain contains.
+- If they have a specific task in mind that they want to accomplish, they will be drawn to the Use-Case section. The use-case should provide a good, concrete hook that shows the value LangChain can provide them and be a good entrypoint to the framework.
+- They can then move to learn more about the fundamentals of LangChain through the Expression Language sections.
+- Next, they can learn about LangChain's various components and integrations.
+- Finally, they can get additional knowledge through the Guides.
+
+This is only an ideal of course - sections will inevitably reference lower or higher-level concepts that are documented in other sections.
+
+## Guidelines
 
 Here are some other guidelines you should think about when writing and organizing documentation.
 
-We generally do not merge new tutorials from outside contributors without an actue need.
-We welcome updates as well as new integration docs, how-tos, and references.
-
-### Avoid duplication
-
-Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
-be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
-
-### Link to other sections
+### Linking to other sections
 
 Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
 to allow a developer to learn more about an unfamiliar topic inline.
 
 This includes linking to the API references as well as conceptual sections!
 
-### Be concise
+### Conciseness
 
 In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
 re-explain it, unless the concept you are documenting presents some new wrinkle.
@@ -149,10 +130,9 @@ Be concise, including in code samples.
 
 ### General style
 
-- Use active voice and present tense whenever possible
-- Use examples and code snippets to illustrate concepts and usage
-- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
-- Use fewer cells with more code to make copy/paste easier
-- Use bullet points and numbered lists to break down information into easily digestible chunks
-- Use tables (especially for **Reference** sections) and diagrams often to present information visually
-- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
+- Use active voice and present tense whenever possible.
+- Use examples and code snippets to illustrate concepts and usage.
+- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically.
+- Use bullet points and numbered lists to break down information into easily digestible chunks.
+- Use tables (especially for **Reference** sections) and diagrams often to present information visually.
+- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages.

docs/docs/contributing/documentation/technical_logistics.mdx

@@ -1,8 +1,4 @@
----
-sidebar_class_name: "hidden"
----
-
-# Setup
+# Technical logistics
 
 LangChain documentation consists of two components:
 
@@ -16,6 +12,8 @@ used to generate the externally facing [API Reference](https://api.python.langch
 The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
 developers document their code well.
 
+The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
+
 The `API Reference` is largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/)
 from the code and is hosted by [Read the Docs](https://readthedocs.org/).
 
@@ -31,7 +29,7 @@ The content for the main documentation is located in the `/docs` directory of th
 
 The documentation is written using a combination of ipython notebooks (`.ipynb` files)
 and markdown (`.mdx` files). The notebooks are converted to markdown
-and then built using [Docusaurus 2](https://docusaurus.io/).
+using [Quarto](https://quarto.org) and then built using [Docusaurus 2](https://docusaurus.io/).
 
 Feel free to make contributions to the main documentation! 🥰
 
@@ -50,6 +48,10 @@ 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.
 
+### Install dependencies
+
+- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus. [Download link](https://quarto.org/docs/download/).
+
 From the **monorepo root**, run the following command to install the dependencies:
 
 ```bash
@@ -69,6 +71,8 @@ make docs_clean
 make api_docs_clean
 ```
 
+
+
 Next, you can build the documentation as outlined below:
 
 ```bash

docs/docs/contributing/index.mdx

@@ -12,8 +12,8 @@ As an open-source project in a rapidly developing field, we are extremely open t
 
 There are many ways to contribute to LangChain. Here are some common ways people contribute:
 
-- [**Documentation**](/docs/contributing/documentation/): Help improve our docs, including this one!
-- [**Code**](/docs/contributing/code/): Help us write code, fix bugs, or improve our infrastructure.
+- [**Documentation**](/docs/contributing/documentation/style_guide): Help improve our docs, including this one!
+- [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure.
 - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
 - [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users.
 

docs/docs/contributing/integrations.mdx

@@ -1,7 +1,6 @@
 ---
 sidebar_position: 5
 ---
-
 # Contribute Integrations
 
 To begin, make sure you have all the dependencies outlined in guide on [Contributing Code](/docs/contributing/code/).

docs/docs/contributing/repo_structure.mdx

@@ -7,7 +7,6 @@ If you plan on contributing to LangChain code or documentation, it can be useful
 to understand the high level structure of the repository.
 
 LangChain is organized as a [monorepo](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
-You can check out our [installation guide](/docs/how_to/installation/) for more on how they fit together.
 
 Here's the structure visualized as a tree:
 
@@ -52,7 +51,7 @@ There are other files in the root directory level, but their presence should be
 The `/docs` directory contains the content for the documentation that is shown
 at https://python.langchain.com/ and the associated API Reference https://api.python.langchain.com/en/latest/langchain_api_reference.html.
 
-See the [documentation](/docs/contributing/documentation/) guidelines to learn how to contribute to the documentation.
+See the [documentation](/docs/contributing/documentation/style_guide) guidelines to learn how to contribute to the documentation.
 
 ## Code
 
@@ -60,6 +59,6 @@ The `/libs` directory contains the code for the LangChain packages.
 
 To learn more about how to contribute code see the following guidelines:
 
-- [Code](/docs/contributing/code/): Learn how to develop in the LangChain codebase.
-- [Integrations](./integrations.mdx): Learn how to contribute to third-party integrations to `langchain-community` or to start a new partner package.
-- [Testing](./testing.mdx): Guidelines to learn how to write tests for the packages.
+- [Code](./code.mdx) Learn how to develop in the LangChain codebase.
+- [Integrations](./integrations.mdx) to learn how to contribute to third-party integrations to langchain-community or to start a new partner package.
+- [Testing](./testing.mdx) guidelines to learn how to write tests for the packages.

docs/docs/contributing/testing.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 6
+sidebar_position: 2
 ---
 
 # Testing

docs/docs/how_to/binding.ipynb

@@ -23,7 +23,7 @@
     "This guide assumes familiarity with the following concepts:\n",
     "- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
-    "- [Tool calling](/docs/how_to/tool_calling)\n",
+    "- [Tool calling](/docs/how_to/tool_calling/)\n",
     "\n",
     ":::\n",
     "\n",
@@ -142,7 +142,7 @@
     "\n",
     "## Attaching OpenAI tools\n",
     "\n",
-    "Another common use-case is tool calling. While you should generally use the [`.bind_tools()`](/docs/how_to/tool_calling) method for tool-calling models, you can also bind provider-specific args directly if you want lower level control:"
+    "Another common use-case is tool calling. While you should generally use the [`.bind_tools()`](/docs/how_to/tool_calling/) method for tool-calling models, you can also bind provider-specific args directly if you want lower level control:"
    ]
   },
   {

docs/docs/how_to/extraction_examples.ipynb

@@ -246,11 +246,11 @@
     "examples = [\n",
     "    (\n",
     "        \"The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.\",\n",
-    "        Data(people=[]),\n",
+    "        Person(name=None, height_in_meters=None, hair_color=None),\n",
     "    ),\n",
     "    (\n",
     "        \"Fiona traveled far from France to Spain.\",\n",
-    "        Data(people=[Person(name=\"Fiona\", height_in_meters=None, hair_color=None)]),\n",
+    "        Person(name=\"Fiona\", height_in_meters=None, hair_color=None),\n",
     "    ),\n",
     "]\n",
     "\n",

docs/docs/how_to/few_shot_examples.ipynb

@@ -23,7 +23,7 @@
     "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
     "- [Example selectors](/docs/concepts/#example-selectors)\n",
     "- [LLMs](/docs/concepts/#llms)\n",
-    "- [Vectorstores](/docs/concepts/#vector-stores)\n",
+    "- [Vectorstores](/docs/concepts/#vectorstores)\n",
     "\n",
     ":::\n",
     "\n",

docs/docs/how_to/few_shot_examples_chat.ipynb

@@ -23,7 +23,7 @@
     "- [Prompt templates](/docs/concepts/#prompt-templates)\n",
     "- [Example selectors](/docs/concepts/#example-selectors)\n",
     "- [Chat models](/docs/concepts/#chat-model)\n",
-    "- [Vectorstores](/docs/concepts/#vector-stores)\n",
+    "- [Vectorstores](/docs/concepts/#vectorstores)\n",
     "\n",
     ":::\n",
     "\n",
@@ -51,7 +51,7 @@
     "- `examples`: A list of dictionary examples to include in the final prompt.\n",
     "- `example_prompt`: converts each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=format_messages#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
     "\n",
-    "Below is a simple demonstration. First, define the examples you'd like to include. Let's give the LLM an unfamiliar mathematical operator, denoted by the \"🦜\" emoji:"
+    "Below is a simple demonstration. First, define the examples you'd like to include:"
    ]
   },
   {
@@ -59,7 +59,17 @@
    "execution_count": 1,
    "id": "5b79e400",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.0 is available.\n",
+      "You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
+      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install -qU langchain langchain-openai langchain-chroma\n",
     "\n",
@@ -69,50 +79,9 @@
     "os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "30856d92",
-   "metadata": {},
-   "source": [
-    "If we try to ask the model what the result of this expression is, it will fail:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "174dec5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='The expression \"2 🦜 9\" is not a standard mathematical operation or equation. It appears to be a combination of the number 2 and the parrot emoji 🦜 followed by the number 9. It does not have a specific mathematical meaning.', response_metadata={'token_usage': {'completion_tokens': 54, 'prompt_tokens': 17, 'total_tokens': 71}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aad12dda-5c47-4a1e-9949-6fe94e03242a-0', usage_metadata={'input_tokens': 17, 'output_tokens': 54, 'total_tokens': 71})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
-    "\n",
-    "model.invoke(\"What is 2 🦜 9?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6d58385",
-   "metadata": {},
-   "source": [
-    "Now let's see what happens if we give the LLM some examples to work with. We'll define some below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "0fc5a02a-6249-4e92-95c3-30fff9671e8b",
    "metadata": {
     "tags": []
@@ -122,8 +91,8 @@
     "from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
     "\n",
     "examples = [\n",
-    "    {\"input\": \"2 🦜 2\", \"output\": \"4\"},\n",
-    "    {\"input\": \"2 🦜 3\", \"output\": \"5\"},\n",
+    "    {\"input\": \"2+2\", \"output\": \"4\"},\n",
+    "    {\"input\": \"2+3\", \"output\": \"5\"},\n",
     "]"
    ]
   },
@@ -137,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "65e72ad1-9060-47d0-91a1-bc130c8b98ac",
    "metadata": {
     "tags": []
@@ -147,7 +116,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[HumanMessage(content='2 🦜 2'), AIMessage(content='4'), HumanMessage(content='2 🦜 3'), AIMessage(content='5')]\n"
+      "[HumanMessage(content='2+2'), AIMessage(content='4'), HumanMessage(content='2+3'), AIMessage(content='5')]\n"
      ]
     }
    ],
@@ -177,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "9f86d6d9-50de-41b6-b6c7-0f9980cc0187",
    "metadata": {
     "tags": []
@@ -193,17 +162,9 @@
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "dd8029c5",
-   "metadata": {},
-   "source": [
-    "And now let's ask the model the initial question and see how it does:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "97d443b1-6fae-4b36-bede-3ff7306288a3",
    "metadata": {
     "tags": []
@@ -212,10 +173,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='11', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5ec4e051-262f-408e-ad00-3f2ebeb561c3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
+       "AIMessage(content='A triangle does not have a square. The square of a number is the result of multiplying the number by itself.', response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 52, 'total_tokens': 75}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-3456c4ef-7b4d-4adb-9e02-8079de82a47a-0')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,9 +184,9 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chain = final_prompt | model\n",
+    "chain = final_prompt | ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
     "\n",
-    "chain.invoke({\"input\": \"What is 2 🦜 9?\"})"
+    "chain.invoke({\"input\": \"What's the square of a triangle?\"})"
    ]
   },
   {
@@ -233,8 +194,6 @@
    "id": "70ab7114-f07f-46be-8874-3705a25aba5f",
    "metadata": {},
    "source": [
-    "And we can see that the model has now inferred that the parrot emoji means addition from the given few-shot examples!\n",
-    "\n",
     "## Dynamic few-shot prompting\n",
     "\n",
     "Sometimes you may want to select only a few examples from your overall set to show based on the input. For this, you can replace the `examples` passed into `FewShotChatMessagePromptTemplate` with an `example_selector`. The other components remain the same as above! Our dynamic few-shot prompt template would look like:\n",
@@ -249,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "ad66f06a-66fd-4fcc-8166-5d0e3c801e57",
    "metadata": {
     "tags": []
@@ -261,9 +220,9 @@
     "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "examples = [\n",
-    "    {\"input\": \"2 🦜 2\", \"output\": \"4\"},\n",
-    "    {\"input\": \"2 🦜 3\", \"output\": \"5\"},\n",
-    "    {\"input\": \"2 🦜 4\", \"output\": \"6\"},\n",
+    "    {\"input\": \"2+2\", \"output\": \"4\"},\n",
+    "    {\"input\": \"2+3\", \"output\": \"5\"},\n",
+    "    {\"input\": \"2+4\", \"output\": \"6\"},\n",
     "    {\"input\": \"What did the cow say to the moon?\", \"output\": \"nothing at all\"},\n",
     "    {\n",
     "        \"input\": \"Write me a poem about the moon\",\n",
@@ -288,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "7790303a-f722-452e-8921-b14bdf20bdff",
    "metadata": {
     "tags": []
@@ -298,10 +257,10 @@
      "data": {
       "text/plain": [
        "[{'input': 'What did the cow say to the moon?', 'output': 'nothing at all'},\n",
-       " {'input': '2 🦜 4', 'output': '6'}]"
+       " {'input': '2+4', 'output': '6'}]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "253c255e-41d7-45f6-9d88-c7a0ced4b1bd",
    "metadata": {
     "tags": []
@@ -338,7 +297,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[HumanMessage(content='2 🦜 3'), AIMessage(content='5'), HumanMessage(content='2 🦜 4'), AIMessage(content='6')]\n"
+      "[HumanMessage(content='2+3'), AIMessage(content='5'), HumanMessage(content='2+2'), AIMessage(content='4')]\n"
      ]
     }
    ],
@@ -358,7 +317,7 @@
     "    ),\n",
     ")\n",
     "\n",
-    "print(few_shot_prompt.invoke(input=\"What's 3 🦜 3?\").to_messages())"
+    "print(few_shot_prompt.invoke(input=\"What's 3+3?\").to_messages())"
    ]
   },
   {
@@ -371,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "id": "e731cb45-f0ea-422c-be37-42af2a6cb2c4",
    "metadata": {
     "tags": []
@@ -381,7 +340,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "messages=[HumanMessage(content='2 🦜 3'), AIMessage(content='5'), HumanMessage(content='2 🦜 4'), AIMessage(content='6')]\n"
+      "messages=[HumanMessage(content='2+3'), AIMessage(content='5'), HumanMessage(content='2+2'), AIMessage(content='4')]\n"
      ]
     }
    ],
@@ -394,7 +353,7 @@
     "    ]\n",
     ")\n",
     "\n",
-    "print(few_shot_prompt.invoke(input=\"What's 3 🦜 3?\"))"
+    "print(few_shot_prompt.invoke(input=\"What's 3+3?\"))"
    ]
   },
   {
@@ -409,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "0568cbc6-5354-47f1-ab4d-dfcc616cf583",
    "metadata": {
     "tags": []
@@ -418,10 +377,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d1863e5e-17cd-4e9d-bf7a-b9f118747a65-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
+       "AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 51, 'total_tokens': 52}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-6bcbe158-a8e3-4a85-a754-1ba274a9f147-0')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,7 +388,7 @@
    "source": [
     "chain = final_prompt | ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
     "\n",
-    "chain.invoke({\"input\": \"What's 3 🦜 3?\"})"
+    "chain.invoke({\"input\": \"What's 3+3?\"})"
    ]
   },
   {
@@ -469,7 +428,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/how_to/index.mdx

@@ -21,7 +21,7 @@ For comprehensive descriptions of every class and function see the [API Referenc
 This highlights functionality that is core to using LangChain.
 
 - [How to: return structured data from a model](/docs/how_to/structured_output/)
-- [How to: use a model to call tools](/docs/how_to/tool_calling)
+- [How to: use a model to call tools](/docs/how_to/tool_calling/)
 - [How to: stream runnables](/docs/how_to/streaming)
 - [How to: debug your LLM apps](/docs/how_to/debugging/)
 
@@ -79,12 +79,6 @@ These are the core building blocks you can use when building applications.
 - [How to: stream a response back](/docs/how_to/chat_streaming)
 - [How to: track token usage](/docs/how_to/chat_token_usage_tracking)
 - [How to: track response metadata across providers](/docs/how_to/response_metadata)
-- [How to: let your end users choose their model](/docs/how_to/chat_models_universal_init/)
-- [How to: use chat model to call tools](/docs/how_to/tool_calling)
-- [How to: stream tool calls](/docs/how_to/tool_streaming)
-- [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot)
-- [How to: bind model-specific formated tools](/docs/how_to/tools_model_specific)
-- [How to: force specific tool call](/docs/how_to/tool_choice)
 - [How to: init any model in one line](/docs/how_to/chat_models_universal_init/)
 
 ### Messages
@@ -182,17 +176,15 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying
 
 ### Tools
 
-LangChain [Tools](/docs/concepts/#tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call.
+LangChain [Tools](/docs/concepts/#tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call).
 
 - [How to: create custom tools](/docs/how_to/custom_tools)
 - [How to: use built-in tools and built-in toolkits](/docs/how_to/tools_builtin)
-- [How to: use chat model to call tools](/docs/how_to/tool_calling)
-- [How to: pass tool results back to model](/docs/how_to/tool_results_pass_to_model)
+- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
 - [How to: add ad-hoc tool calling capability to LLMs and chat models](/docs/how_to/tools_prompting)
 - [How to: pass run time values to tools](/docs/how_to/tool_runtime)
 - [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
 - [How to: handle errors when calling tools](/docs/how_to/tools_error)
-- [How to: disable parallel tool calling](/docs/how_to/tool_choice)
 
 ### Multimodal
 
@@ -317,8 +309,7 @@ LangSmith allows you to closely trace, monitor and evaluate your LLM application
 It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build.
 
 LangSmith documentation is hosted on a separate site.
-You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/how_to_guides/), but we'll highlight a few sections that are particularly
-relevant to LangChain below:
+You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/how_to_guides/).
 
 ### Evaluation
 <span data-heading-keywords="evaluation,evaluate"></span>
@@ -327,13 +318,3 @@ Evaluating performance is a vital part of building LLM-powered applications.
 LangSmith helps with every step of the process from creating a dataset to defining metrics to running evaluators.
 
 To learn more, check out the [LangSmith evaluation how-to guides](https://docs.smith.langchain.com/how_to_guides#evaluation).
-
-### Tracing
-<span data-heading-keywords="trace,tracing"></span>
-
-Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.
-
-- [How to: trace with LangChain](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain)
-- [How to: add metadata and tags to traces](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain#add-metadata-and-tags-to-traces)
-
-You can see general tracing-related how-tos [in this section of the LangSmith docs](https://docs.smith.langchain.com/how_to_guides/tracing).

docs/docs/how_to/installation.mdx

@@ -2,14 +2,11 @@
 sidebar_position: 2
 ---
 
-# How to install LangChain packages
-
-The LangChain ecosystem is split into different packages, which allow you to choose exactly which pieces of
-functionality to install.
+# Installation
 
 ## Official release
 
-To install the main LangChain package, run:
+To install LangChain run:
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
@@ -24,24 +21,11 @@ import CodeBlock from "@theme/CodeBlock";
   </TabItem>
 </Tabs>
 
-While this package acts as a sane starting point to using LangChain,
-much of the value of LangChain comes when integrating it with various model providers, datastores, etc.
+This will install the bare minimum requirements of LangChain.
+A lot of the value of LangChain comes when integrating it with various model providers, datastores, etc.
 By default, the dependencies needed to do that are NOT installed. You will need to install the dependencies for specific integrations separately.
-We'll show how to do that in the next sections of this guide.
 
-## Ecosystem packages
-
-With the exception of the `langsmith` SDK, all packages in the LangChain ecosystem depend on `langchain-core`, which contains base
-classes and abstractions that other packages use. The dependency graph below shows how the difference packages are related.
-A directed arrow indicates that the source package depends on the target package:
-
-![](/img/ecosystem_packages.png)
-
-When installing a package, you do not need to explicitly install that package's explicit dependencies (such as `langchain-core`).
-However, you may choose to if you are using a feature only available in a certain version of that dependency.
-If you do, you should make sure that the installed or pinned version is compatible with any other integration packages you use.
-
-### From source
+## From source
 
 If you want to install from source, you can do so by cloning the repo and be sure that the directory is `PATH/TO/REPO/langchain/libs/langchain` running:
 
@@ -49,21 +33,21 @@ If you want to install from source, you can do so by cloning the repo and be sur
 pip install -e .
 ```
 
-### LangChain core
+## LangChain core
 The `langchain-core` package contains base abstractions that the rest of the LangChain ecosystem uses, along with the LangChain Expression Language. It is automatically installed by `langchain`, but can also be used separately. Install with:
 
 ```bash
 pip install langchain-core
 ```
 
-### LangChain community
+## LangChain community
 The `langchain-community` package contains third-party integrations. Install with:
 
 ```bash
 pip install langchain-community
 ```
 
-### LangChain experimental
+## LangChain experimental
 The `langchain-experimental` package holds experimental LangChain code, intended for research and experimental uses.
 Install with:
 
@@ -71,15 +55,14 @@ Install with:
 pip install langchain-experimental
 ```
 
-### LangGraph
-`langgraph` is a library for building stateful, multi-actor applications with LLMs. It integrates smoothly with LangChain, but can be used without it.
+## LangGraph
+`langgraph` is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.
 Install with:
 
 ```bash
 pip install langgraph
 ```
-
-### LangServe
+## LangServe
 LangServe helps developers deploy LangChain runnables and chains as a REST API.
 LangServe is automatically installed by LangChain CLI.
 If not using LangChain CLI, install with:
@@ -97,10 +80,9 @@ Install with:
 pip install langchain-cli
 ```
 
-### LangSmith SDK
-The LangSmith SDK is automatically installed by LangChain. However, it does not depend on
-`langchain-core`, and can be installed and used independently if desired.
-If you are not using LangChain, you can install it with:
+## LangSmith SDK
+The LangSmith SDK is automatically installed by LangChain.
+If not using LangChain, install with:
 
 ```bash
 pip install langsmith

docs/docs/how_to/message_history.ipynb

@@ -129,7 +129,7 @@
    "id": "a531da5e",
    "metadata": {},
    "source": [
-    "## What is the runnable you are trying to wrap?\n",
+    "## What is the runnable you are trying wrap?\n",
     "\n",
     "`RunnableWithMessageHistory` can only wrap certain types of Runnables. Specifically, it can be used for any Runnable that takes as input one of:\n",
     "\n",

docs/docs/how_to/migrate_agent.ipynb

@@ -1,19 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "adc7ee09",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "---\n",
-    "keywords: [create_react_agent, create_react_agent()]\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "457cdc67-1893-4653-8b0c-b185a5947e74",
@@ -21,18 +7,9 @@
    "source": [
     "# How to migrate from legacy LangChain agents to LangGraph\n",
     "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "- [Agents](/docs/concepts/#agents)\n",
-    "- [LangGraph](https://langchain-ai.github.io/langgraph/)\n",
-    "- [Tool calling](/docs/how_to/tool_calling/)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "Here we focus on how to move from legacy LangChain agents to more flexible [LangGraph](https://langchain-ai.github.io/langgraph/) agents.\n",
+    "Here we focus on how to move from legacy LangChain agents to LangGraph agents.\n",
     "LangChain agents (the [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
-    "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n",
+    "In this notebook we will show how those parameters map to the LangGraph [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
     "\n",
     "#### Prerequisites\n",
     "\n",
@@ -218,7 +195,7 @@
     "\n",
     "Let's take a look at all of these below. We will pass in custom instructions to get the agent to respond in Spanish.\n",
     "\n",
-    "First up, using `AgentExecutor`:"
+    "First up, using AgentExecutor:"
    ]
   },
   {
@@ -261,16 +238,7 @@
    "id": "bd5f5500-5ae4-4000-a9fd-8c5a2cc6404d",
    "metadata": {},
    "source": [
-    "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
-    "\n",
-    "LangGraph's prebuilt `create_react_agent` does not take a prompt template directly as a parameter, but instead takes a [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) parameter. This modifies messages before they are passed into the model, and can be one of four values:\n",
-    "\n",
-    "- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
-    "- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
-    "- A `Callable`, which should take in a list of messages. The output is then passed to the language model.\n",
-    "- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should should take in a list of messages. The output is then passed to the language model.\n",
-    "\n",
-    "Here's how it looks in action:"
+    "Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent). This can either be a string or a LangChain SystemMessage."
    ]
   },
   {
@@ -1244,18 +1212,6 @@
     "except GraphRecursionError as e:\n",
     "    print(\"Stopping agent prematurely due to triggering stop condition\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41377eb8",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "You've now learned how to migrate your LangChain agent executors to LangGraph.\n",
-    "\n",
-    "Next, check out other [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/)."
-   ]
   }
  ],
  "metadata": {

docs/docs/how_to/multimodal_prompts.ipynb

@@ -52,12 +52,7 @@
     "        (\"system\", \"Describe the image provided\"),\n",
     "        (\n",
     "            \"user\",\n",
-    "            [\n",
-    "                {\n",
-    "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data}\"},\n",
-    "                }\n",
-    "            ],\n",
+    "            [{\"type\": \"image_url\", \"image_url\": \"data:image/jpeg;base64,{image_data}\"}],\n",
     "        ),\n",
     "    ]\n",
     ")"
@@ -115,11 +110,11 @@
     "            [\n",
     "                {\n",
     "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data1}\"},\n",
+    "                    \"image_url\": \"data:image/jpeg;base64,{image_data1}\",\n",
     "                },\n",
     "                {\n",
     "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data2}\"},\n",
+    "                    \"image_url\": \"data:image/jpeg;base64,{image_data2}\",\n",
     "                },\n",
     "            ],\n",
     "        ),\n",

docs/docs/how_to/sql_csv.ipynb

@@ -351,7 +351,7 @@
    "id": "ab1b2e7c-6ea8-4674-98eb-a43c69f5c19d",
    "metadata": {},
    "source": [
-    "To help enforce proper use of our Python tool, we'll using [tool calling](/docs/how_to/tool_calling):"
+    "To help enforce proper use of our Python tool, we'll using [tool calling](/docs/how_to/tool_calling/):"
    ]
   },
   {

docs/docs/how_to/sql_query_checking.ipynb

@@ -243,7 +243,7 @@
      "text": [
       "================================\u001b[1m System Message \u001b[0m================================\n",
       "\n",
-      "You are a \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m expert. Given an input question, create a syntactically correct \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m query to run.\n",
+      "You are a \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m expert. Given an input question, creat a syntactically correct \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m query to run.\n",
       "Unless the user specifies in the question a specific number of examples to obtain, query for at most \u001b[33;1m\u001b[1;3m{top_k}\u001b[0m results using the LIMIT clause as per \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m. You can order the results to return the most informative data in the database.\n",
       "Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
       "Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
@@ -275,7 +275,7 @@
     }
    ],
    "source": [
-    "system = \"\"\"You are a {dialect} expert. Given an input question, create a syntactically correct {dialect} query to run.\n",
+    "system = \"\"\"You are a {dialect} expert. Given an input question, creat a syntactically correct {dialect} query to run.\n",
     "Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per {dialect}. You can order the results to return the most informative data in the database.\n",
     "Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
     "Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",

docs/docs/how_to/structured_output.ipynb

@@ -250,7 +250,7 @@
    "id": "e28c14d3",
    "metadata": {},
    "source": [
-    "Alternatively, you can use tool calling directly to allow the model to choose between options, if your [chosen model supports it](/docs/integrations/chat/). This involves a bit more parsing and setup but in some instances leads to better performance because you don't have to use nested schemas. See [this how-to guide](/docs/how_to/tool_calling) for more details."
+    "Alternatively, you can use tool calling directly to allow the model to choose between options, if your [chosen model supports it](/docs/integrations/chat/). This involves a bit more parsing and setup but in some instances leads to better performance because you don't have to use nested schemas. See [this how-to guide](/docs/how_to/tool_calling/) for more details."
    ]
   },
   {

docs/docs/how_to/tool_calling.ipynb

@@ -1,18 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "---\n",
-    "keywords: [tool calling, tool call]\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -24,7 +11,6 @@
     "This guide assumes familiarity with the following concepts:\n",
     "- [Chat models](/docs/concepts/#chat-models)\n",
     "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [Output parsers](/docs/concepts/#output-parsers)\n",
     "\n",
     ":::\n",
     "\n",
@@ -52,12 +38,6 @@
     "parameters matching the desired schema, then treat the generated output as your final \n",
     "result.\n",
     "\n",
-    ":::note\n",
-    "\n",
-    "If you only need formatted values, try the [.with_structured_output()](/docs/how_to/structured_output/#the-with_structured_output-method) chat model method as a simpler entrypoint.\n",
-    "\n",
-    ":::\n",
-    "\n",
     "However, tool calling goes beyond [structured output](/docs/how_to/structured_output/)\n",
     "since you can pass responses from called tools back to the model to create longer interactions.\n",
     "For instance, given a search engine tool, an LLM might handle a \n",
@@ -72,13 +52,8 @@
     "support variants of a tool calling feature.\n",
     "\n",
     "LangChain implements standard interfaces for defining tools, passing them to LLMs, \n",
-    "and representing tool calls. This guide and the other How-to pages in the Tool section will show you how to use tools with LangChain."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "and representing tool calls. This guide will show you how to use them.\n",
+    "\n",
     "## Passing tools to chat models\n",
     "\n",
     "Chat models that support tool calling features implement a `.bind_tools` method, which \n",
@@ -178,7 +153,7 @@
     "# | output: false\n",
     "# | echo: false\n",
     "\n",
-    "%pip install -qU langchain_openai\n",
+    "%pip install -qU langchain langchain_openai\n",
     "\n",
     "import os\n",
     "from getpass import getpass\n",
@@ -192,33 +167,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_g4RuAijtDcSeM96jXyCuiLSN', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 95, 'total_tokens': 113}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5157d15a-7e0e-4ab1-af48-3d98010cd152-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_g4RuAijtDcSeM96jXyCuiLSN'}], usage_metadata={'input_tokens': 95, 'output_tokens': 18, 'total_tokens': 113})"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "llm_with_tools = llm.bind_tools(tools)\n",
-    "\n",
-    "query = \"What is 3 * 12?\"\n",
-    "\n",
-    "llm_with_tools.invoke(query)"
+    "llm_with_tools = llm.bind_tools(tools)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, even though the prompt didn't really suggest a tool call, our LLM made one since it was forced to do so. You can look at the docs for [bind_tools()](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools."
+    "We can also use the `tool_choice` parameter to ensure certain behavior. For example, we can force our tool to call the multiply tool by using the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_forced_to_multiply = llm.bind_tools(tools, tool_choice=\"Multiply\")\n",
+    "llm_forced_to_multiply.invoke(\"what is 2 + 4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even if we pass it something that doesn't require multiplcation - it will still call the tool!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" which is OpenAI specific) keyword to the `tool_choice` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_forced_to_use_tool = llm.bind_tools(tools, tool_choice=\"any\")\n",
+    "llm_forced_to_use_tool.invoke(\"What day is today?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, even though the prompt didn't really suggest a tool call, our LLM made one since it was forced to do so. You can look at the docs for [`bind_tool`](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools."
    ]
   },
   {
@@ -250,10 +273,10 @@
       "text/plain": [
        "[{'name': 'Multiply',\n",
        "  'args': {'a': 3, 'b': 12},\n",
-       "  'id': 'call_TnadLbWJu9HwDULRb51RNSMw'},\n",
+       "  'id': 'call_KquHA7mSbgtAkpkmRPaFnJKa'},\n",
        " {'name': 'Add',\n",
        "  'args': {'a': 11, 'b': 49},\n",
-       "  'id': 'call_Q9vt1up05sOQScXvUYWzSpCg'}]"
+       "  'id': 'call_Fl0hQi4IBTzlpaJYlM5kPQhE'}]"
       ]
      },
      "execution_count": 5,
@@ -279,8 +302,7 @@
     "a name, string arguments, identifier, and error message.\n",
     "\n",
     "If desired, [output parsers](/docs/how_to#output-parsers) can further \n",
-    "process the output. For example, we can convert existing values populated on the `.tool_calls` attribute back to the original Pydantic class using the\n",
-    "[PydanticToolsParser](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.openai_tools.PydanticToolsParser.html):"
+    "process the output. For example, we can convert back to the original Pydantic class:"
    ]
   },
   {
@@ -300,27 +322,443 @@
     }
    ],
    "source": [
-    "from langchain_core.output_parsers import PydanticToolsParser\n",
+    "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
     "\n",
     "chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])\n",
     "chain.invoke(query)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming\n",
+    "\n",
+    "When tools are called in a streaming context, \n",
+    "[message chunks](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "will be populated with [tool call chunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
+    "objects in a list via the `.tool_call_chunks` attribute. A `ToolCallChunk` includes \n",
+    "optional string fields for the tool `name`, `args`, and `id`, and includes an optional \n",
+    "integer field `index` that can be used to join chunks together. Fields are optional \n",
+    "because portions of a tool call may be streamed across different chunks (e.g., a chunk \n",
+    "that includes a substring of the arguments may have null values for the tool name and id).\n",
+    "\n",
+    "Because message chunks inherit from their parent message class, an \n",
+    "[AIMessageChunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "with tool call chunks will also include `.tool_calls` and `.invalid_tool_calls` fields. \n",
+    "These fields are parsed best-effort from the message's tool call chunks.\n",
+    "\n",
+    "Note that not all providers currently support streaming for tool calls:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "[{'name': 'Multiply', 'args': '', 'id': 'call_3aQwTP9CYlFxwOvQZPHDu6wL', 'index': 0}]\n",
+      "[{'name': None, 'args': '{\"a\"', 'id': None, 'index': 0}]\n",
+      "[{'name': None, 'args': ': 3, ', 'id': None, 'index': 0}]\n",
+      "[{'name': None, 'args': '\"b\": 1', 'id': None, 'index': 0}]\n",
+      "[{'name': None, 'args': '2}', 'id': None, 'index': 0}]\n",
+      "[{'name': 'Add', 'args': '', 'id': 'call_SQUoSsJz2p9Kx2x73GOgN1ja', 'index': 1}]\n",
+      "[{'name': None, 'args': '{\"a\"', 'id': None, 'index': 1}]\n",
+      "[{'name': None, 'args': ': 11,', 'id': None, 'index': 1}]\n",
+      "[{'name': None, 'args': ' \"b\": ', 'id': None, 'index': 1}]\n",
+      "[{'name': None, 'args': '49}', 'id': None, 'index': 1}]\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in llm_with_tools.astream(query):\n",
+    "    print(chunk.tool_call_chunks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that adding message chunks will merge their corresponding tool call chunks. This is the principle by which LangChain's various [tool output parsers](/docs/how_to/output_parser_structured) support streaming.\n",
+    "\n",
+    "For example, below we accumulate tool call chunks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "[{'name': 'Multiply', 'args': '', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\"', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, ', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 1', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\"', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11,', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": ', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
+      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "first = True\n",
+    "async for chunk in llm_with_tools.astream(query):\n",
+    "    if first:\n",
+    "        gathered = chunk\n",
+    "        first = False\n",
+    "    else:\n",
+    "        gathered = gathered + chunk\n",
+    "\n",
+    "    print(gathered.tool_call_chunks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'str'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(gathered.tool_call_chunks[0][\"args\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And below we accumulate tool calls to demonstrate partial parsing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "[]\n",
+      "[{'name': 'Multiply', 'args': {}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 1}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
+      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "first = True\n",
+    "async for chunk in llm_with_tools.astream(query):\n",
+    "    if first:\n",
+    "        gathered = chunk\n",
+    "        first = False\n",
+    "    else:\n",
+    "        gathered = gathered + chunk\n",
+    "\n",
+    "    print(gathered.tool_calls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'dict'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(gathered.tool_calls[0][\"args\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Passing tool outputs to the model\n",
+    "\n",
+    "If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
+       " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_svc2GLSxNFALbaCAbSjMI9J8', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'Multiply'}, 'type': 'function'}, {'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 105, 'total_tokens': 155}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a79ad1dd-95f1-4a46-b688-4c83f327a7b3-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_svc2GLSxNFALbaCAbSjMI9J8'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh'}]),\n",
+       " ToolMessage(content='36', tool_call_id='call_svc2GLSxNFALbaCAbSjMI9J8'),\n",
+       " ToolMessage(content='60', tool_call_id='call_r8jxte3zW6h3MEGV3zH2qzFh')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage, ToolMessage\n",
+    "\n",
+    "messages = [HumanMessage(query)]\n",
+    "ai_msg = llm_with_tools.invoke(messages)\n",
+    "messages.append(ai_msg)\n",
+    "for tool_call in ai_msg.tool_calls:\n",
+    "    selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
+    "    tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
+    "    messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
+    "messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 171, 'total_tokens': 189}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-20b52149-e00d-48ea-97cf-f8de7a255f8c-0')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_with_tools.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we pass back the same `id` in the `ToolMessage` as the what we receive from the model in order to help the model match tool responses with tool calls.\n",
+    "\n",
+    "## Few-shot prompting\n",
+    "\n",
+    "For more complex tool use it's very useful to add few-shot examples to the prompt. We can do this by adding `AIMessage`s with `ToolCall`s and corresponding `ToolMessage`s to our prompt.\n",
+    "\n",
+    "For example, even with some special instructions our model can get tripped up by order of operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Multiply',\n",
+       "  'args': {'a': 119, 'b': 8},\n",
+       "  'id': 'call_T88XN6ECucTgbXXkyDeC2CQj'},\n",
+       " {'name': 'Add',\n",
+       "  'args': {'a': 952, 'b': -20},\n",
+       "  'id': 'call_licdlmGsRqzup8rhqJSb1yZ4'}]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_with_tools.invoke(\n",
+    "    \"Whats 119 times 8 minus 20. Don't do any math yourself, only use tools for math. Respect order of operations\"\n",
+    ").tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model shouldn't be trying to add anything yet, since it technically can't know the results of 119 * 8 yet.\n",
+    "\n",
+    "By adding a prompt with some examples we can correct this behavior:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Multiply',\n",
+       "  'args': {'a': 119, 'b': 8},\n",
+       "  'id': 'call_9MvuwQqg7dlJupJcoTWiEsDo'}]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "examples = [\n",
+    "    HumanMessage(\n",
+    "        \"What's the product of 317253 and 128472 plus four\", name=\"example_user\"\n",
+    "    ),\n",
+    "    AIMessage(\n",
+    "        \"\",\n",
+    "        name=\"example_assistant\",\n",
+    "        tool_calls=[\n",
+    "            {\"name\": \"Multiply\", \"args\": {\"x\": 317253, \"y\": 128472}, \"id\": \"1\"}\n",
+    "        ],\n",
+    "    ),\n",
+    "    ToolMessage(\"16505054784\", tool_call_id=\"1\"),\n",
+    "    AIMessage(\n",
+    "        \"\",\n",
+    "        name=\"example_assistant\",\n",
+    "        tool_calls=[{\"name\": \"Add\", \"args\": {\"x\": 16505054784, \"y\": 4}, \"id\": \"2\"}],\n",
+    "    ),\n",
+    "    ToolMessage(\"16505054788\", tool_call_id=\"2\"),\n",
+    "    AIMessage(\n",
+    "        \"The product of 317253 and 128472 plus four is 16505054788\",\n",
+    "        name=\"example_assistant\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "system = \"\"\"You are bad at math but are an expert at using a calculator. \n",
+    "\n",
+    "Use past tool usage as an example of how to correctly use the tools.\"\"\"\n",
+    "few_shot_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        *examples,\n",
+    "        (\"human\", \"{query}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = {\"query\": RunnablePassthrough()} | few_shot_prompt | llm_with_tools\n",
+    "chain.invoke(\"Whats 119 times 8 minus 20\").tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we get the correct output this time.\n",
+    "\n",
+    "Here's what the [LangSmith trace](https://smith.langchain.com/public/f70550a1-585f-4c9d-a643-13148ab1616f/r) looks like."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binding model-specific formats (advanced)\n",
+    "\n",
+    "Providers adopt different conventions for formatting tool schemas. \n",
+    "For instance, OpenAI uses a format like this:\n",
+    "\n",
+    "- `type`: The type of the tool. At the time of writing, this is always `\"function\"`.\n",
+    "- `function`: An object containing tool parameters.\n",
+    "- `function.name`: The name of the schema to output.\n",
+    "- `function.description`: A high level description of the schema to output.\n",
+    "- `function.parameters`: The nested details of the schema you want to extract, formatted as a [JSON schema](https://json-schema.org/) dict.\n",
+    "\n",
+    "We can bind this model-specific format directly to the model as well if preferred. Here's an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe', 'function': {'arguments': '{\"a\":119,\"b\":8}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 62, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-353e8a9a-7125-4f94-8c68-4f3da4c21120-0', tool_calls=[{'name': 'multiply', 'args': {'a': 119, 'b': 8}, 'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe'}])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "model_with_tools = model.bind(\n",
+    "    tools=[\n",
+    "        {\n",
+    "            \"type\": \"function\",\n",
+    "            \"function\": {\n",
+    "                \"name\": \"multiply\",\n",
+    "                \"description\": \"Multiply two integers together.\",\n",
+    "                \"parameters\": {\n",
+    "                    \"type\": \"object\",\n",
+    "                    \"properties\": {\n",
+    "                        \"a\": {\"type\": \"number\", \"description\": \"First integer\"},\n",
+    "                        \"b\": {\"type\": \"number\", \"description\": \"Second integer\"},\n",
+    "                    },\n",
+    "                    \"required\": [\"a\", \"b\"],\n",
+    "                },\n",
+    "            },\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "model_with_tools.invoke(\"Whats 119 times 8?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is functionally equivalent to the `bind_tools()` calls above."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Next steps\n",
     "\n",
-    "Now you've learned how to bind tool schemas to a chat model and to call those tools. Next, you can learn more about how to use tools:\n",
-    "\n",
-    "- Few shot promting [with tools](/docs/how_to/tools_few_shot/)\n",
-    "- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
-    "- Bind [model-specific tools](/docs/how_to/tools_model_specific/)\n",
-    "- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
-    "- Pass [tool results back to model](/docs/how_to/tool_results_pass_to_model)\n",
-    "\n",
-    "You can also check out some more specific uses of tool calling:\n",
+    "Now you've learned how to bind tool schemas to a chat model and to call those tools. Next, check out some more specific uses of tool calling:\n",
     "\n",
     "- Building [tool-using chains and agents](/docs/how_to#tools)\n",
     "- Getting [structured outputs](/docs/how_to/structured_output/) from models"
@@ -343,7 +781,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_calling_parallel.ipynb

@@ -1,108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Disabling parallel tool calling (OpenAI only)\n",
-    "\n",
-    "OpenAI tool calling performs tool calling in parallel by default. That means that if we ask a question like \"What is the weather in Tokyo, New York, and Chicago?\" and we have a tool for getting the weather, it will call the tool 3 times in parallel. We can force it to call only a single tool once by using the ``parallel_tool_call`` parameter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First let's set up our tools and model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def add(a: int, b: int) -> int:\n",
-    "    \"\"\"Adds a and b.\"\"\"\n",
-    "    return a + b\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiplies a and b.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "tools = [add, multiply]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's show a quick example of how disabling parallel tool calls work:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'add',\n",
-       "  'args': {'a': 2, 'b': 2},\n",
-       "  'id': 'call_Hh4JOTCDM85Sm9Pr84VKrWu5'}]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)\n",
-    "llm_with_tools.invoke(\"Please call the first tool two times\").tool_calls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, even though we explicitly told the model to call a tool twice, by disabling parallel tool calls the model was constrained to only calling one."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/tool_choice.ipynb

@@ -1,126 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to force tool calling behavior\n",
-    "\n",
-    "In order to force our LLM to spelect a specific tool, we can use the `tool_choice` parameter to ensure certain behavior. First, let's define our model and tools:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def add(a: int, b: int) -> int:\n",
-    "    \"\"\"Adds a and b.\"\"\"\n",
-    "    return a + b\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiplies a and b.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "tools = [add, multiply]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# | output: false\n",
-    "# | echo: false\n",
-    "\n",
-    "%pip install -qU langchain langchain_openai\n",
-    "\n",
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For example, we can force our tool to call the multiply tool by using the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "llm_forced_to_multiply = llm.bind_tools(tools, tool_choice=\"Multiply\")\n",
-    "llm_forced_to_multiply.invoke(\"what is 2 + 4\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Even if we pass it something that doesn't require multiplcation - it will still call the tool!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" which is OpenAI specific) keyword to the `tool_choice` parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "llm_forced_to_use_tool = llm.bind_tools(tools, tool_choice=\"any\")\n",
-    "llm_forced_to_use_tool.invoke(\"What day is today?\")"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -1,127 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to pass tool outputs to the model\n",
-    "\n",
-    "If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s. First, let's define our tools and our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def add(a: int, b: int) -> int:\n",
-    "    \"\"\"Adds a and b.\"\"\"\n",
-    "    return a + b\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiplies a and b.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "tools = [add, multiply]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
-    "llm_with_tools = llm.bind_tools(tools)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use ``ToolMessage`` to pass back the output of the tool calls to the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
-       " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_svc2GLSxNFALbaCAbSjMI9J8', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'Multiply'}, 'type': 'function'}, {'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 105, 'total_tokens': 155}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a79ad1dd-95f1-4a46-b688-4c83f327a7b3-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_svc2GLSxNFALbaCAbSjMI9J8'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh'}]),\n",
-       " ToolMessage(content='36', tool_call_id='call_svc2GLSxNFALbaCAbSjMI9J8'),\n",
-       " ToolMessage(content='60', tool_call_id='call_r8jxte3zW6h3MEGV3zH2qzFh')]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage, ToolMessage\n",
-    "\n",
-    "query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
-    "\n",
-    "messages = [HumanMessage(query)]\n",
-    "ai_msg = llm_with_tools.invoke(messages)\n",
-    "messages.append(ai_msg)\n",
-    "for tool_call in ai_msg.tool_calls:\n",
-    "    selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
-    "    tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
-    "    messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
-    "messages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 171, 'total_tokens': 189}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-20b52149-e00d-48ea-97cf-f8de7a255f8c-0')"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "llm_with_tools.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that we pass back the same `id` in the `ToolMessage` as the what we receive from the model in order to help the model match tool responses with tool calls."
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/tool_runtime.ipynb

@@ -12,7 +12,7 @@
     "- [Chat models](/docs/concepts/#chat-models)\n",
     "- [LangChain Tools](/docs/concepts/#tools)\n",
     "- [How to create tools](/docs/how_to/custom_tools)\n",
-    "- [How to use a model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling)\n",
+    "- [How to use a model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling/)\n",
     ":::\n",
     "\n",
     ":::{.callout-info} Supported models\n",
@@ -227,7 +227,7 @@
     "\n",
     "Chat models only output requests to invoke tools, they don't actually invoke the underlying tools.\n",
     "\n",
-    "To see how to invoke the tools, please refer to [how to use a model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling).\n",
+    "To see how to invoke the tools, please refer to [how to use a model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling/).\n",
     ":::"
    ]
   }

docs/docs/how_to/tool_streaming.ipynb

@@ -1,235 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to stream tool calls\n",
-    "\n",
-    "When tools are called in a streaming context, \n",
-    "[message chunks](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
-    "will be populated with [tool call chunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
-    "objects in a list via the `.tool_call_chunks` attribute. A `ToolCallChunk` includes \n",
-    "optional string fields for the tool `name`, `args`, and `id`, and includes an optional \n",
-    "integer field `index` that can be used to join chunks together. Fields are optional \n",
-    "because portions of a tool call may be streamed across different chunks (e.g., a chunk \n",
-    "that includes a substring of the arguments may have null values for the tool name and id).\n",
-    "\n",
-    "Because message chunks inherit from their parent message class, an \n",
-    "[AIMessageChunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
-    "with tool call chunks will also include `.tool_calls` and `.invalid_tool_calls` fields. \n",
-    "These fields are parsed best-effort from the message's tool call chunks.\n",
-    "\n",
-    "Note that not all providers currently support streaming for tool calls. Before we start let's define our tools and our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def add(a: int, b: int) -> int:\n",
-    "    \"\"\"Adds a and b.\"\"\"\n",
-    "    return a + b\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiplies a and b.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "tools = [add, multiply]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
-    "llm_with_tools = llm.bind_tools(tools)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's define our query and stream our output:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[]\n",
-      "[{'name': 'Multiply', 'args': '', 'id': 'call_3aQwTP9CYlFxwOvQZPHDu6wL', 'index': 0}]\n",
-      "[{'name': None, 'args': '{\"a\"', 'id': None, 'index': 0}]\n",
-      "[{'name': None, 'args': ': 3, ', 'id': None, 'index': 0}]\n",
-      "[{'name': None, 'args': '\"b\": 1', 'id': None, 'index': 0}]\n",
-      "[{'name': None, 'args': '2}', 'id': None, 'index': 0}]\n",
-      "[{'name': 'Add', 'args': '', 'id': 'call_SQUoSsJz2p9Kx2x73GOgN1ja', 'index': 1}]\n",
-      "[{'name': None, 'args': '{\"a\"', 'id': None, 'index': 1}]\n",
-      "[{'name': None, 'args': ': 11,', 'id': None, 'index': 1}]\n",
-      "[{'name': None, 'args': ' \"b\": ', 'id': None, 'index': 1}]\n",
-      "[{'name': None, 'args': '49}', 'id': None, 'index': 1}]\n",
-      "[]\n"
-     ]
-    }
-   ],
-   "source": [
-    "query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
-    "\n",
-    "async for chunk in llm_with_tools.astream(query):\n",
-    "    print(chunk.tool_call_chunks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that adding message chunks will merge their corresponding tool call chunks. This is the principle by which LangChain's various [tool output parsers](/docs/how_to/output_parser_structured) support streaming.\n",
-    "\n",
-    "For example, below we accumulate tool call chunks:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[]\n",
-      "[{'name': 'Multiply', 'args': '', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\"', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, ', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 1', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\"', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11,', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": ', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
-      "[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n"
-     ]
-    }
-   ],
-   "source": [
-    "first = True\n",
-    "async for chunk in llm_with_tools.astream(query):\n",
-    "    if first:\n",
-    "        gathered = chunk\n",
-    "        first = False\n",
-    "    else:\n",
-    "        gathered = gathered + chunk\n",
-    "\n",
-    "    print(gathered.tool_call_chunks)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'str'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(type(gathered.tool_call_chunks[0][\"args\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And below we accumulate tool calls to demonstrate partial parsing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[]\n",
-      "[]\n",
-      "[{'name': 'Multiply', 'args': {}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 1}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
-      "[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n"
-     ]
-    }
-   ],
-   "source": [
-    "first = True\n",
-    "async for chunk in llm_with_tools.astream(query):\n",
-    "    if first:\n",
-    "        gathered = chunk\n",
-    "        first = False\n",
-    "    else:\n",
-    "        gathered = gathered + chunk\n",
-    "\n",
-    "    print(gathered.tool_calls)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'dict'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(type(gathered.tool_calls[0][\"args\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/tools_few_shot.ipynb

@@ -1,175 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to use few-shot prompting with tool calling\n",
-    "\n",
-    "For more complex tool use it's very useful to add few-shot examples to the prompt. We can do this by adding `AIMessage`s with `ToolCall`s and corresponding `ToolMessage`s to our prompt.\n",
-    "\n",
-    "First let's define our tools and model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def add(a: int, b: int) -> int:\n",
-    "    \"\"\"Adds a and b.\"\"\"\n",
-    "    return a + b\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def multiply(a: int, b: int) -> int:\n",
-    "    \"\"\"Multiplies a and b.\"\"\"\n",
-    "    return a * b\n",
-    "\n",
-    "\n",
-    "tools = [add, multiply]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
-    "llm_with_tools = llm.bind_tools(tools)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's run our model where we can notice that even with some special instructions our model can get tripped up by order of operations. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'Multiply',\n",
-       "  'args': {'a': 119, 'b': 8},\n",
-       "  'id': 'call_T88XN6ECucTgbXXkyDeC2CQj'},\n",
-       " {'name': 'Add',\n",
-       "  'args': {'a': 952, 'b': -20},\n",
-       "  'id': 'call_licdlmGsRqzup8rhqJSb1yZ4'}]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "llm_with_tools.invoke(\n",
-    "    \"Whats 119 times 8 minus 20. Don't do any math yourself, only use tools for math. Respect order of operations\"\n",
-    ").tool_calls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model shouldn't be trying to add anything yet, since it technically can't know the results of 119 * 8 yet.\n",
-    "\n",
-    "By adding a prompt with some examples we can correct this behavior:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'Multiply',\n",
-       "  'args': {'a': 119, 'b': 8},\n",
-       "  'id': 'call_9MvuwQqg7dlJupJcoTWiEsDo'}]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "\n",
-    "examples = [\n",
-    "    HumanMessage(\n",
-    "        \"What's the product of 317253 and 128472 plus four\", name=\"example_user\"\n",
-    "    ),\n",
-    "    AIMessage(\n",
-    "        \"\",\n",
-    "        name=\"example_assistant\",\n",
-    "        tool_calls=[\n",
-    "            {\"name\": \"Multiply\", \"args\": {\"x\": 317253, \"y\": 128472}, \"id\": \"1\"}\n",
-    "        ],\n",
-    "    ),\n",
-    "    ToolMessage(\"16505054784\", tool_call_id=\"1\"),\n",
-    "    AIMessage(\n",
-    "        \"\",\n",
-    "        name=\"example_assistant\",\n",
-    "        tool_calls=[{\"name\": \"Add\", \"args\": {\"x\": 16505054784, \"y\": 4}, \"id\": \"2\"}],\n",
-    "    ),\n",
-    "    ToolMessage(\"16505054788\", tool_call_id=\"2\"),\n",
-    "    AIMessage(\n",
-    "        \"The product of 317253 and 128472 plus four is 16505054788\",\n",
-    "        name=\"example_assistant\",\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "system = \"\"\"You are bad at math but are an expert at using a calculator. \n",
-    "\n",
-    "Use past tool usage as an example of how to correctly use the tools.\"\"\"\n",
-    "few_shot_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", system),\n",
-    "        *examples,\n",
-    "        (\"human\", \"{query}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = {\"query\": RunnablePassthrough()} | few_shot_prompt | llm_with_tools\n",
-    "chain.invoke(\"Whats 119 times 8 minus 20\").tool_calls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we get the correct output this time.\n",
-    "\n",
-    "Here's what the [LangSmith trace](https://smith.langchain.com/public/f70550a1-585f-4c9d-a643-13148ab1616f/r) looks like."
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/tools_model_specific.ipynb

@@ -1,79 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# How to bind model-specific tools\n",
-    "\n",
-    "Providers adopt different conventions for formatting tool schemas. \n",
-    "For instance, OpenAI uses a format like this:\n",
-    "\n",
-    "- `type`: The type of the tool. At the time of writing, this is always `\"function\"`.\n",
-    "- `function`: An object containing tool parameters.\n",
-    "- `function.name`: The name of the schema to output.\n",
-    "- `function.description`: A high level description of the schema to output.\n",
-    "- `function.parameters`: The nested details of the schema you want to extract, formatted as a [JSON schema](https://json-schema.org/) dict.\n",
-    "\n",
-    "We can bind this model-specific format directly to the model as well if preferred. Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe', 'function': {'arguments': '{\"a\":119,\"b\":8}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 62, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-353e8a9a-7125-4f94-8c68-4f3da4c21120-0', tool_calls=[{'name': 'multiply', 'args': {'a': 119, 'b': 8}, 'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe'}])"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI()\n",
-    "\n",
-    "model_with_tools = model.bind(\n",
-    "    tools=[\n",
-    "        {\n",
-    "            \"type\": \"function\",\n",
-    "            \"function\": {\n",
-    "                \"name\": \"multiply\",\n",
-    "                \"description\": \"Multiply two integers together.\",\n",
-    "                \"parameters\": {\n",
-    "                    \"type\": \"object\",\n",
-    "                    \"properties\": {\n",
-    "                        \"a\": {\"type\": \"number\", \"description\": \"First integer\"},\n",
-    "                        \"b\": {\"type\": \"number\", \"description\": \"Second integer\"},\n",
-    "                    },\n",
-    "                    \"required\": [\"a\", \"b\"],\n",
-    "                },\n",
-    "            },\n",
-    "        }\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "model_with_tools.invoke(\"Whats 119 times 8?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is functionally equivalent to the `bind_tools()` method."
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/how_to/tools_prompting.ipynb

@@ -19,7 +19,7 @@
     "\n",
     ":::{.callout-caution}\n",
     "\n",
-    "Some models have been fine-tuned for tool calling and provide a dedicated API for tool calling. Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling. Please see the [how to use a chat model to call tools](/docs/how_to/tool_calling) guide for more information.\n",
+    "Some models have been fine-tuned for tool calling and provide a dedicated API for tool calling. Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling. Please see the [how to use a chat model to call tools](/docs/how_to/tool_calling/) guide for more information.\n",
     "\n",
     ":::\n",
     "\n",
@@ -34,7 +34,7 @@
     "\n",
     ":::\n",
     "\n",
-    "In this guide, we'll see how to add **ad-hoc** tool calling support to a chat model. This is an alternative method to invoke tools if you're using a model that does not natively support [tool calling](/docs/how_to/tool_calling).\n",
+    "In this guide, we'll see how to add **ad-hoc** tool calling support to a chat model. This is an alternative method to invoke tools if you're using a model that does not natively support [tool calling](/docs/how_to/tool_calling/).\n",
     "\n",
     "We'll do this by simply writing a prompt that will get the model to invoke the appropriate tools. Here's a diagram of the logic:\n",
     "\n",
@@ -87,7 +87,7 @@
    "id": "7ec6409b-21e5-4d0a-8a46-c4ef0b055dd3",
    "metadata": {},
    "source": [
-    "You can select any of the given models for this how-to guide. Keep in mind that most of these models already [support native tool calling](/docs/integrations/chat/), so using the prompting strategy shown here doesn't make sense for these models, and instead you should follow the [how to use a chat model to call tools](/docs/how_to/tool_calling) guide.\n",
+    "You can select any of the given models for this how-to guide. Keep in mind that most of these models already [support native tool calling](/docs/integrations/chat/), so using the prompting strategy shown here doesn't make sense for these models, and instead you should follow the [how to use a chat model to call tools](/docs/how_to/tool_calling/) guide.\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",

docs/docs/how_to/trim_messages.ipynb

@@ -323,7 +323,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='A: A \"Polly-gone\"!', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_66b29dffce', 'finish_reason': 'stop', 'logprobs': None}, id='run-83e96ddf-bcaa-4f63-824c-98b0f8a0d474-0', usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})"
+       "AIMessage(content='A \"polygon\"! Because it\\'s a \"poly-gone\" silent!', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 32, 'total_tokens': 46}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'stop', 'logprobs': None}, id='run-64cc4575-14d1-4f3f-b4af-97f24758f703-0', usage_metadata={'input_tokens': 32, 'output_tokens': 14, 'total_tokens': 46})"
       ]
      },
      "execution_count": 7,
@@ -391,17 +391,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "id": "a9517858-fc2f-4dc3-898d-bf98a0e905a0",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run c87e2f1b-81ad-4fa7-bfd9-ce6edb29a482 not found for run 7892ee8f-0669-4d6b-a2ca-ef8aae81042a. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='A \"polly-no-wanna-cracker\"!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 32, 'total_tokens': 42}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5bf7397cd3', 'finish_reason': 'stop', 'logprobs': None}, id='run-054dd309-3497-4e7b-b22a-c1859f11d32e-0', usage_metadata={'input_tokens': 32, 'output_tokens': 10, 'total_tokens': 42})"
+       "AIMessage(content=\"A polygon! Because it's a parrot gone quiet!\", response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 32, 'total_tokens': 43}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_319be4768e', 'finish_reason': 'stop', 'logprobs': None}, id='run-72dad96e-8b58-45f4-8c08-21f9f1a6b68f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 11, 'total_tokens': 43})"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -415,7 +422,7 @@
     "\n",
     "def dummy_get_session_history(session_id):\n",
     "    if session_id != \"1\":\n",
-    "        return InMemoryChatMessageHistory()\n",
+    "        raise InMemoryChatMessageHistory()\n",
     "    return chat_history\n",
     "\n",
     "\n",
@@ -457,9 +464,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-2",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-2"
   },
   "language_info": {
    "codemirror_mode": {
@@ -471,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/ai21.ipynb

@@ -18,9 +18,7 @@
     "# ChatAI21\n",
     "\n",
     "This notebook covers how to get started with AI21 chat models.\n",
-    "Note that different chat models support different parameters. See the ",
-    "[AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
-    "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/) \n",
+    "\n",
     "## Installation"
    ]
   },
@@ -46,8 +44,7 @@
    "source": [
     "## Environment Setup\n",
     "\n",
-    "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the ",
-    "`AI21_API_KEY` environment variable:\n"
+    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
    ]
   },
   {

docs/docs/integrations/chat/anthropic.ipynb

@@ -36,7 +36,7 @@
     "| [ChatAnthropic](https://api.python.langchain.com/en/latest/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html) | [langchain-anthropic](https://api.python.langchain.com/en/latest/anthropic_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-anthropic?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-anthropic?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",
+    "| [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",

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "afaf8039",
+   "id": "641f8cb0",
    "metadata": {},
    "source": [
     "---\n",
@@ -12,89 +12,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
+   "id": "38f26d7a",
    "metadata": {},
    "source": [
     "# AzureChatOpenAI\n",
     "\n",
-    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
+    ">[Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or a web-based interface in the Azure OpenAI Studio.\n",
     "\n",
-    "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
-    "\n",
-    ":::info Azure OpenAI vs OpenAI\n",
-    "\n",
-    "Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). OpenAI also provides its own model APIs. To access OpenAI services directly, use the [ChatOpenAI integration](/docs/integrations/chat/openai/).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [AzureChatOpenAI](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://api.python.langchain.com/en/latest/openai_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access AzureOpenAI models you'll need to create an Azure account, create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, get an Azure OpenAI API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python) to create your deployment and generate an API key. Once you've done this set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT environment variables:"
+    "This notebook goes over how to connect to an Azure-hosted OpenAI endpoint. First, we need to install the `langchain-openai` package."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\"Enter your AzureOpenAI API key: \")\n",
-    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain AzureOpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "id": "d83ba7de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,56 +34,65 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
+   "id": "e39133c8",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
    "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions.\n",
-    "- Replace `azure_deployment` with the name of your deployment,\n",
-    "- You can find the latest supported `api_version` here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference."
+    "Next, let's set some environment variables to help us connect to the Azure OpenAI service. You can find these values in the Azure portal."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "execution_count": null,
+   "id": "1d8d73bd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_openai import AzureChatOpenAI\n",
+    "import os\n",
     "\n",
-    "llm = AzureChatOpenAI(\n",
-    "    azure_deployment=\"YOUR-DEPLOYMENT\",\n",
-    "    api_version=\"2024-05-01-preview\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
+    "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"...\"\n",
+    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://<your-endpoint>.openai.azure.com/\"\n",
+    "os.environ[\"AZURE_OPENAI_API_VERSION\"] = \"2023-06-01-preview\"\n",
+    "os.environ[\"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\"] = \"chat\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b4f3e15",
+   "id": "e7b160f8",
    "metadata": {},
    "source": [
-    "## Invocation"
+    "Next, let's construct our model and chat with it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cbe4bb58-ba13-4355-8af9-cd990dc47a64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_openai import AzureChatOpenAI\n",
+    "\n",
+    "model = AzureChatOpenAI(\n",
+    "    openai_api_version=os.environ[\"AZURE_OPENAI_API_VERSION\"],\n",
+    "    azure_deployment=os.environ[\"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\"],\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
+   "id": "99509140",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-a6a732c2-cb02-4e50-9a9c-ab30eab034fc-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
+       "AIMessage(content=\"J'adore programmer.\", response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 19, 'total_tokens': 25}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-25ed88db-38f2-4b0c-a943-a03f217711a9-0')"
       ]
      },
      "execution_count": 4,
@@ -161,165 +101,95 @@
     }
    ],
    "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-084967d7-06f2-441f-b5c1-477e2a9e9d03-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
+    "message = HumanMessage(\n",
+    "    content=\"Translate this sentence from English to French. I love programming.\"\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",
-    ")"
+    "model.invoke([message])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "id": "f27fa24d",
    "metadata": {},
    "source": [
-    "## Specifying model version\n",
-    "\n",
-    "Azure OpenAI responses contain `model_name` response metadata property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the specific version of the model, which is set on the deployment in Azure. E.g. it does not distinguish between `gpt-35-turbo-0125` and `gpt-35-turbo-0301`. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
+    "## Model Version\n",
+    "Azure OpenAI responses contain `model` property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the version of the model, which is set on the deployment in Azure. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
     "\n",
     "To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "04b36e75-e8b7-4721-899e-76301ac2ecd9",
+   "execution_count": 5,
+   "id": "0531798a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-community"
+    "from langchain_community.callbacks import get_openai_callback"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "84c411b0-1790-4798-8bb7-47d8ece4c2dc",
-   "metadata": {},
+   "execution_count": 7,
+   "id": "aceddb72",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total Cost (USD): $0.000063\n"
+      "Total Cost (USD): $0.000041\n"
      ]
     }
    ],
    "source": [
-    "from langchain_community.callbacks import get_openai_callback\n",
-    "\n",
+    "model = AzureChatOpenAI(\n",
+    "    openai_api_version=os.environ[\"AZURE_OPENAI_API_VERSION\"],\n",
+    "    azure_deployment=os.environ[\n",
+    "        \"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\"\n",
+    "    ],  # in Azure, this deployment has version 0613 - input and output tokens are counted separately\n",
+    ")\n",
     "with get_openai_callback() as cb:\n",
-    "    llm.invoke(messages)\n",
+    "    model.invoke([message])\n",
     "    print(\n",
     "        f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\"\n",
     "    )  # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2e61eefd",
+   "metadata": {},
+   "source": [
+    "We can provide the model version to `AzureChatOpenAI` constructor. It will get appended to the model name returned by Azure OpenAI and cost will be counted correctly."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "21234693-d92b-4d69-8a7f-55aa062084bf",
+   "execution_count": 11,
+   "id": "8d5e54e9",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total Cost (USD): $0.000078\n"
+      "Total Cost (USD): $0.000044\n"
      ]
     }
    ],
    "source": [
-    "llm_0301 = AzureChatOpenAI(\n",
-    "    azure_deployment=\"YOUR-DEPLOYMENT\",\n",
-    "    api_version=\"2024-05-01-preview\",\n",
+    "model0301 = AzureChatOpenAI(\n",
+    "    openai_api_version=os.environ[\"AZURE_OPENAI_API_VERSION\"],\n",
+    "    azure_deployment=os.environ[\"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME\"],\n",
     "    model_version=\"0301\",\n",
     ")\n",
     "with get_openai_callback() as cb:\n",
-    "    llm_0301.invoke(messages)\n",
+    "    model0301.invoke([message])\n",
     "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
-   ]
   }
  ],
  "metadata": {
@@ -338,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/bedrock.ipynb

@@ -29,7 +29,7 @@
     "> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
     "> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
     "> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
-    "> generative AI capabilities into your applications using the AWS services you are already familiar with."
+    "> generative AI capabilities into your applications using the AWS services you are already familiar with.\n"
    ]
   },
   {
@@ -47,7 +47,7 @@
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet langchain-aws"
+    "%pip install --upgrade --quiet  langchain-aws"
    ]
   },
   {

docs/docs/integrations/chat/google_vertex_ai_palm.ipynb

@@ -35,7 +35,7 @@
     "| [ChatVertexAI](https://api.python.langchain.com/en/latest/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://api.python.langchain.com/en/latest/google_vertexai_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| [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",

docs/docs/integrations/chat/groq.ipynb

@@ -91,7 +91,7 @@
     "\n",
     "## Tool calling\n",
     "\n",
-    "Groq chat models support [tool calling](/docs/how_to/tool_calling) to generate output matching a specific schema. The model may choose to call multiple tools or the same tool multiple times if appropriate.\n",
+    "Groq chat models support [tool calling](/docs/how_to/tool_calling/) to generate output matching a specific schema. The model may choose to call multiple tools or the same tool multiple times if appropriate.\n",
     "\n",
     "Here's an example:"
    ]

docs/docs/integrations/chat/ibm_watsonx.ipynb

@@ -1,585 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "1c95cd76",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "---\n",
-    "sidebar_label: IBM watsonx.ai\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70996d8a",
-   "metadata": {},
-   "source": [
-    "# ChatWatsonx\n",
-    "\n",
-    ">ChatWatsonx is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
-    "\n",
-    "The aim of these examples is to show how to communicate with `watsonx.ai` models using `LangChain` LLMs API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef7b088a",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatWatsonx](https://api.python.langchain.com/en/latest/ibm_api_reference.html) | [langchain-ibm](https://api.python.langchain.com/en/latest/ibm_api_reference.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/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](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f406e092",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "To access IBM watsonx.ai models you'll need to create an IBM watsonx.ai account, get an API key, and install the `langchain-ibm` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "The cell below defines the credentials required to work with watsonx Foundation Model inferencing.\n",
-    "\n",
-    "**Action:** Provide the IBM Cloud user API key. For details, see\n",
-    "[Managing user API keys](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "11d572a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "watsonx_api_key = getpass()\n",
-    "os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c59782a7",
-   "metadata": {},
-   "source": [
-    "Additionally you are able to pass additional secrets as an environment variable. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f98c573c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
-    "os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CPD cluster\"\n",
-    "os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n",
-    "os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\"\n",
-    "os.environ[\"WATSONX_INSTANCE_ID\"] = \"your instance_id for accessing the CPD cluster\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3dc9176",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain IBM integration lives in the `langchain-ibm` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "387eda86",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -qU langchain-ibm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e36acbef",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "You might need to adjust model `parameters` for different models or tasks. For details, refer to [Available MetaNames](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "407cd500",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "parameters = {\n",
-    "    \"decoding_method\": \"sample\",\n",
-    "    \"max_new_tokens\": 100,\n",
-    "    \"min_new_tokens\": 1,\n",
-    "    \"stop_sequences\": [\".\"],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b586538",
-   "metadata": {},
-   "source": [
-    "Initialize the `WatsonxLLM` class with the previously set parameters.\n",
-    "\n",
-    "\n",
-    "**Note**: \n",
-    "\n",
-    "- To provide context for the API call, you must pass the `project_id` or `space_id`. To get your project or space ID, open your project or space, go to the **Manage** tab, and click **General**. For more information see: [Project documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects) or [Deployment space documentation](https://www.ibm.com/docs/en/watsonx/saas?topic=spaces-creating-deployment).\n",
-    "- Depending on the region of your provisioned service instance, use one of the urls listed in [watsonx.ai API Authentication](https://ibm.github.io/watsonx-ai-python-sdk/setup_cloud.html#authentication).\n",
-    "\n",
-    "In this example, we’ll use the `project_id` and Dallas URL.\n",
-    "\n",
-    "\n",
-    "You need to specify the `model_id` that will be used for inferencing. You can find the list of all the available models in [Supported foundation models](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "98371396",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_ibm import ChatWatsonx\n",
-    "\n",
-    "chat = ChatWatsonx(\n",
-    "    model_id=\"ibm/granite-13b-chat-v2\",\n",
-    "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
-    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
-    "    params=parameters,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2202f4e0",
-   "metadata": {},
-   "source": [
-    "Alternatively, you can use Cloud Pak for Data credentials. For details, see [watsonx.ai software setup](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html).  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "243ecccb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatWatsonx(\n",
-    "    model_id=\"ibm/granite-13b-chat-v2\",\n",
-    "    url=\"PASTE YOUR URL HERE\",\n",
-    "    username=\"PASTE YOUR USERNAME HERE\",\n",
-    "    password=\"PASTE YOUR PASSWORD HERE\",\n",
-    "    instance_id=\"openshift\",\n",
-    "    version=\"4.8\",\n",
-    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
-    "    params=parameters,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96ed13d4",
-   "metadata": {},
-   "source": [
-    "Instead of `model_id`, you can also pass the `deployment_id` of the previously tuned model. The entire model tuning workflow is described in [Working with TuneExperiment and PromptTuner](https://ibm.github.io/watsonx-ai-python-sdk/pt_working_with_class_and_prompt_tuner.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08e66c88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatWatsonx(\n",
-    "    deployment_id=\"PASTE YOUR DEPLOYMENT_ID HERE\",\n",
-    "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
-    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
-    "    params=parameters,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f571001d",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "To obtain completions, you can call the model directly using a string prompt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "beea2b5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Je t'aime pour écouter la Rock.\", response_metadata={'token_usage': {'generated_token_count': 12, 'input_token_count': 28}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-05b305ce-5401-4a10-b557-41a4b15c7f6f-0')"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Invocation\n",
-    "\n",
-    "messages = [\n",
-    "    (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
-    "    (\n",
-    "        \"human\",\n",
-    "        \"I love you for listening to Rock.\",\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "8ab1a25a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Sure, I can help you with that! Horses are large, powerful mammals that belong to the family Equidae.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-391776ff-3b38-4768-91e8-ff64177149e5-0')"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Invocation multiple chat\n",
-    "from langchain_core.messages import (\n",
-    "    HumanMessage,\n",
-    "    SystemMessage,\n",
-    ")\n",
-    "\n",
-    "system_message = SystemMessage(\n",
-    "    content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
-    ")\n",
-    "human_message = HumanMessage(content=\"horse\")\n",
-    "\n",
-    "chat.invoke([system_message, human_message])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20e4b568",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "Create `ChatPromptTemplate` objects which will be responsible for creating a random question."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "dd919925",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "system = (\n",
-    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
-    ")\n",
-    "human = \"{input}\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a013a53",
-   "metadata": {},
-   "source": [
-    "Provide a inputs and run the chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "68160377",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Python.', response_metadata={'token_usage': {'generated_token_count': 5, 'input_token_count': 23}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-1b1ccf5d-0e33-46f2-a087-e2a136ba1fb7-0')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain = prompt | chat\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love Python\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2c9da33",
-   "metadata": {},
-   "source": [
-    "## Streaming the Model output \n",
-    "\n",
-    "You can stream the model output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3f63166a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The moon is a natural satellite of the Earth, and it has been a source of fascination for humans for centuries."
-     ]
-    }
-   ],
-   "source": [
-    "system_message = SystemMessage(\n",
-    "    content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
-    ")\n",
-    "human_message = HumanMessage(content=\"moon\")\n",
-    "\n",
-    "for chunk in chat.stream([system_message, human_message]):\n",
-    "    print(chunk.content, end=\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a7a2aa1",
-   "metadata": {},
-   "source": [
-    "## Batch the Model output \n",
-    "\n",
-    "You can batch the model output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "9e948729",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content='Cats are domestic animals that belong to the Felidae family.', response_metadata={'token_usage': {'generated_token_count': 13, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-71a8bd7a-a1aa-497b-9bdd-a4d6fe1d471a-0'),\n",
-       " AIMessage(content='Dogs are domesticated mammals of the family Canidae, characterized by their adaptability to various environments and social structures.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-22b7a0cb-e44a-4b68-9921-872f82dcd82b-0')]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "message_1 = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"cat\"),\n",
-    "]\n",
-    "message_2 = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"dog\"),\n",
-    "]\n",
-    "\n",
-    "chat.batch([message_1, message_2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c739e1fe",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "### ChatWatsonx.bind_tools()\n",
-    "\n",
-    "Please note that `ChatWatsonx.bind_tools` is on beta state, so right now we only support `mistralai/mixtral-8x7b-instruct-v01` model.\n",
-    "\n",
-    "You should also redefine `max_new_tokens` parameter to get the entire model response. By default `max_new_tokens` is set ot 20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "328fce76",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_ibm import ChatWatsonx\n",
-    "\n",
-    "parameters = {\"max_new_tokens\": 200}\n",
-    "\n",
-    "chat = ChatWatsonx(\n",
-    "    model_id=\"mistralai/mixtral-8x7b-instruct-v01\",\n",
-    "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
-    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
-    "    params=parameters,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e1633a73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class GetWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a given location\"\"\"\n",
-    "\n",
-    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "\n",
-    "\n",
-    "llm_with_tools = chat.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3bf9b8ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='', additional_kwargs={'function_call': {'type': 'function'}, 'tool_calls': [{'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"Los Angeles\"}'}, 'id': None}, {'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"New York\"}'}, 'id': None}]}, response_metadata={'token_usage': {'generated_token_count': 99, 'input_token_count': 320}, 'model_name': 'mistralai/mixtral-8x7b-instruct-v01', 'system_fingerprint': '', 'finish_reason': 'eos_token'}, id='run-38627104-f2ac-4edb-8390-d5425fb65979-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None}, {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"Which city is hotter today: LA or NY?\",\n",
-    ")\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba03dbf4",
-   "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "38f10ba7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None},\n",
-       " {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ai_msg.tool_calls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ee72a59",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all IBM watsonx.ai features and configurations head to the API reference: https://api.python.langchain.com/en/latest/ibm_api_reference.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/llamacpp.ipynb

@@ -21,7 +21,7 @@
     "| [ChatLlamaCpp](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅ | ❌ | ❌ |\n",
     "\n",
     "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/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](/docs/how_to/logprobs/) |\n",
+    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/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](/docs/how_to/logprobs/) |\n",
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
     "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | \n",
     "\n",

docs/docs/integrations/chat/oci_generative_ai.ipynb

@@ -1,190 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: OCIGenAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatOCIGenAI\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
-    "\n",
-    "Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API.\n",
-    "Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available __[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)__ and __[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)__.\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/oci_generative_ai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOCIGenAI](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html) | [langchain-community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-oci-generative-ai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-oci-generative-ai?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 OCIGenAI models you'll need to install the `oci` and `langchain-community` packages.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "The credentials and authentication methods supported for this integration are equivalent to those used with other OCI services and follow the __[standard SDK authentication](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm)__ methods, specifically API Key, session token, instance principal, and resource principal.\n",
-    "\n",
-    "API key is the default authentication method used in the examples above. The following example demonstrates how to use a different authentication method (session token)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain OCIGenAI integration lives in the `langchain-community` package and you will also need to install the `oci` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community oci"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.oci_generative_ai import ChatOCIGenAI\n",
-    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
-    "\n",
-    "chat = ChatOCIGenAI(\n",
-    "    model_id=\"cohere.command-r-16k\",\n",
-    "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
-    "    compartment_id=\"MY_OCID\",\n",
-    "    model_kwargs={\"temperature\": 0.7, \"max_tokens\": 500},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    SystemMessage(content=\"your are an AI assistant.\"),\n",
-    "    AIMessage(content=\"Hi there human!\"),\n",
-    "    HumanMessage(content=\"tell me a joke.\"),\n",
-    "]\n",
-    "response = chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(response.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
-    "chain = prompt | chat\n",
-    "\n",
-    "response = chain.invoke({\"topic\": \"dogs\"})\n",
-    "print(response.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatOCIGenAI features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/ollama_functions.ipynb

@@ -20,12 +20,6 @@
     "Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use llama3 and phi3 models.\n",
     "For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n",
     "\n",
-    ":::warning\n",
-    "\n",
-    "This is an experimental wrapper that attempts to bolt-on tool calling support to models that do not natively support it. Use with caution.\n",
-    "\n",
-    ":::\n",
-    "\n",
     "## Setup\n",
     "\n",
     "Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n",

docs/docs/integrations/chat/openai.ipynb

@@ -41,7 +41,7 @@
     "| [ChatOpenAI](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://api.python.langchain.com/en/latest/openai_api_reference.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | 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",
+    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/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](/docs/how_to/logprobs/) |\n",
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
     "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | \n",
     "\n",
@@ -426,7 +426,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/async_chromium.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "Headless mode means that the browser is running without a graphical user interface.\n",
     "\n",
-    "In the below example we'll use the `AsyncChromiumLoader` to loads the page, and then the [`Html2TextTransformer`](/docs/integrations/document_transformers/html2text/) to strip out the HTML tags and other semantic information."
+    "`AsyncChromiumLoader` loads the page, and then we use `Html2TextTransformer` to transform to text."
    ]
   },
   {
@@ -23,22 +23,48 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet playwright beautifulsoup4 html2text\n",
+    "%pip install --upgrade --quiet  playwright beautifulsoup4\n",
     "!playwright install"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd2cdea7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<!DOCTYPE html><html lang=\"en\"><head><script src=\"https://s0.2mdn.net/instream/video/client.js\" asyn'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.document_loaders import AsyncChromiumLoader\n",
+    "\n",
+    "urls = [\"https://www.wsj.com\"]\n",
+    "loader = AsyncChromiumLoader(urls, user_agent=\"MyAppUserAgent\")\n",
+    "docs = loader.load()\n",
+    "docs[0].page_content[0:100]"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "00487c0f",
+   "id": "c64e7df9",
    "metadata": {},
    "source": [
-    "**Note:** If you are using Jupyter notebooks, you might also need to install and apply `nest_asyncio` before loading the documents like this:"
+    "If you are using Jupyter notebooks, you might need to apply `nest_asyncio` before loading the documents."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d374eef4",
+   "id": "5f2fe3c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,40 +74,6 @@
     "nest_asyncio.apply()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "dd2cdea7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<!DOCTYPE html><html lang=\"en\" dir=\"ltr\" class=\"docs-wrapper docs-doc-page docs-version-2.0 plugin-d'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.document_loaders import AsyncChromiumLoader\n",
-    "\n",
-    "urls = [\"https://docs.smith.langchain.com/\"]\n",
-    "loader = AsyncChromiumLoader(urls, user_agent=\"MyAppUserAgent\")\n",
-    "docs = loader.load()\n",
-    "docs[0].page_content[0:100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7eb5e6aa",
-   "metadata": {},
-   "source": [
-    "Now let's transform the documents into a more readable syntax using the transformer:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -91,7 +83,7 @@
     {
      "data": {
       "text/plain": [
-       "'Skip to main content\\n\\nGo to API Docs\\n\\nSearch`⌘``K`\\n\\nGo to App\\n\\n  * Quick start\\n  * Tutorials\\n\\n  * How-to guides\\n\\n  * Concepts\\n\\n  * Reference\\n\\n  * Pricing\\n  * Self-hosting\\n\\n  * LangGraph Cloud\\n\\n  *   * Quick start\\n\\nOn this page\\n\\n# Get started with LangSmith\\n\\n**LangSmith** is a platform for building production-grade LLM applications. It\\nallows you to closely monitor and evaluate your application, so you can ship\\nquickly and with confidence. Use of LangChain is not necessary - LangSmith\\nworks on it'"
+       "\"Skip to Main ContentSkip to SearchSkip to... Select * Top News * What's News *\\nFeatured Stories * Retirement * Life & Arts * Hip-Hop * Sports * Video *\\nEconomy * Real Estate * Sports * CMO * CIO * CFO * Risk & Compliance *\\nLogistics Report * Sustainable Business * Heard on the Street * Barron’s *\\nMarketWatch * Mansion Global * Penta * Opinion * Journal Reports * Sponsored\\nOffers Explore Our Brands * WSJ * * * * * Barron's * * * * * MarketWatch * * *\\n* * IBD # The Wall Street Journal SubscribeSig\""
       ]
      },
      "execution_count": 6,
@@ -124,7 +116,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/microsoft_excel.ipynb

@@ -12,50 +12,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0b01ee46",
+   "execution_count": 1,
+   "id": "e6616e3a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain-community unstructured openpyxl"
+    "from langchain_community.document_loaders import UnstructuredExcelLoader"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "a654e4d9",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Stanley Cups', metadata={'source': 'example_data/stanley-cups.xlsx', 'file_directory': 'example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups', 'page_number': 1, 'languages': ['eng'], 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Title'}),\n",
-       " Document(page_content='\\n\\n\\nTeam\\nLocation\\nStanley Cups\\n\\n\\nBlues\\nSTL\\n1\\n\\n\\nFlyers\\nPHI\\n2\\n\\n\\nMaple Leafs\\nTOR\\n13\\n\\n\\n', metadata={'source': 'example_data/stanley-cups.xlsx', 'file_directory': 'example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups', 'page_number': 1, 'text_as_html': '<table border=\"1\" class=\"dataframe\">\\n  <tbody>\\n    <tr>\\n      <td>Team</td>\\n      <td>Location</td>\\n      <td>Stanley Cups</td>\\n    </tr>\\n    <tr>\\n      <td>Blues</td>\\n      <td>STL</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <td>Flyers</td>\\n      <td>PHI</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <td>Maple Leafs</td>\\n      <td>TOR</td>\\n      <td>13</td>\\n    </tr>\\n  </tbody>\\n</table>', 'languages': ['eng'], 'parent_id': '17e9a90f9616f2abed8cf32b5bd3810d', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Table'}),\n",
-       " Document(page_content='Stanley Cups Since 67', metadata={'source': 'example_data/stanley-cups.xlsx', 'file_directory': 'example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups Since 67', 'page_number': 2, 'languages': ['eng'], 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Title'}),\n",
-       " Document(page_content='\\n\\n\\nTeam\\nLocation\\nStanley Cups\\n\\n\\nBlues\\nSTL\\n1\\n\\n\\nFlyers\\nPHI\\n2\\n\\n\\nMaple Leafs\\nTOR\\n0\\n\\n\\n', metadata={'source': 'example_data/stanley-cups.xlsx', 'file_directory': 'example_data', 'filename': 'stanley-cups.xlsx', 'last_modified': '2023-12-19T13:42:18', 'page_name': 'Stanley Cups Since 67', 'page_number': 2, 'text_as_html': '<table border=\"1\" class=\"dataframe\">\\n  <tbody>\\n    <tr>\\n      <td>Team</td>\\n      <td>Location</td>\\n      <td>Stanley Cups</td>\\n    </tr>\\n    <tr>\\n      <td>Blues</td>\\n      <td>STL</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <td>Flyers</td>\\n      <td>PHI</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <td>Maple Leafs</td>\\n      <td>TOR</td>\\n      <td>0</td>\\n    </tr>\\n  </tbody>\\n</table>', 'languages': ['eng'], 'parent_id': 'ee34bd8c186b57e3530d5443ffa58122', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'category': 'Table'})]"
+       "Document(page_content='\\n  \\n    \\n      Team\\n      Location\\n      Stanley Cups\\n    \\n    \\n      Blues\\n      STL\\n      1\\n    \\n    \\n      Flyers\\n      PHI\\n      2\\n    \\n    \\n      Maple Leafs\\n      TOR\\n      13\\n    \\n  \\n', metadata={'source': 'example_data/stanley-cups.xlsx', 'filename': 'stanley-cups.xlsx', 'file_directory': 'example_data', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border=\"1\" class=\"dataframe\">\\n  <tbody>\\n    <tr>\\n      <td>Team</td>\\n      <td>Location</td>\\n      <td>Stanley Cups</td>\\n    </tr>\\n    <tr>\\n      <td>Blues</td>\\n      <td>STL</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <td>Flyers</td>\\n      <td>PHI</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <td>Maple Leafs</td>\\n      <td>TOR</td>\\n      <td>13</td>\\n    </tr>\\n  </tbody>\\n</table>', 'category': 'Table'})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_community.document_loaders import UnstructuredExcelLoader\n",
-    "\n",
     "loader = UnstructuredExcelLoader(\"example_data/stanley-cups.xlsx\", mode=\"elements\")\n",
     "docs = loader.load()\n",
-    "\n",
-    "print(len(docs))\n",
-    "\n",
-    "docs"
+    "docs[0]"
    ]
   },
   {
@@ -91,7 +76,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-community azure-ai-documentintelligence"
+    "%pip install --upgrade --quiet  langchain langchain-community azure-ai-documentintelligence"
    ]
   },
   {
@@ -130,7 +115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_transformers/html2text.ipynb

@@ -19,12 +19,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet html2text"
+    "%pip install --upgrade --quiet  html2text"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "8ca0974b",
    "metadata": {},
    "outputs": [
@@ -32,8 +32,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n",
-      "Fetching pages: 100%|##########| 2/2 [00:00<00:00, 14.74it/s]\n"
+      "Fetching pages: 100%|############| 2/2 [00:00<00:00, 10.75it/s]\n"
      ]
     }
    ],
@@ -47,107 +46,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
+   "id": "ddf2be97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_transformers import Html2TextTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "a95a928c",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
+    "html2text = Html2TextTransformer()\n",
+    "docs_transformed = html2text.transform_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "18ef9fe9",
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "## Fantasy\n",
-      "\n",
-      "  * Football\n",
-      "\n",
-      "  * Baseball\n",
-      "\n",
-      "  * Basketball\n",
-      "\n",
-      "  * Hockey\n",
-      "\n",
-      "## ESPN Sites\n",
-      "\n",
-      "  * ESPN Deportes\n",
-      "\n",
-      "  * Andscape\n",
-      "\n",
-      "  * espnW\n",
-      "\n",
-      "  * ESPNFC\n",
-      "\n",
-      "  * X Games\n",
-      "\n",
-      "  * SEC Network\n",
-      "\n",
-      "## ESPN Apps\n",
-      "\n",
-      "  * ESPN\n",
-      "\n",
-      "  * ESPN Fantasy\n",
-      "\n",
-      "  * Tournament Challenge\n",
-      "\n",
-      "## Follow ESPN\n",
-      "\n",
-      "  * Facebook\n",
-      "\n",
-      "  * X/Twitter\n",
-      "\n",
-      "  * Instagram\n",
-      "\n",
-      "  * Snapchat\n",
-      "\n",
-      "  * TikTok\n",
-      "\n",
-      "  * YouTube\n",
-      "\n",
-      "## Fresh updates to our NBA mock draft: Everything we're hearing hours before\n",
-      "Round 1\n",
-      "\n",
-      "With hours until Round 1 begins (8 p.m. ET on ESPN and ABC), ESPN draft\n",
-      "insiders Jonathan Givony and Jeremy Woo have new intel on lottery picks and\n",
-      "more.\n",
-      "\n",
-      "2hJonathan Givony and Jeremy Woo\n",
-      "\n",
-      "Illustration by ESPN\n",
-      "\n",
-      "## From No. 1 to 100: Ranking the 2024 NBA draft prospects\n",
-      "\n",
-      "Who's No. 1? Where do the Kentucky, Duke and UConn players rank? Here's our\n",
-      "final Top 100 Big Board.\n",
-      "\n",
-      "6hJonathan Givony and Jeremy Woo\n",
-      "\n",
-      "  * Full draft order: All 58 picks over two rounds\n",
-      "  * Trade tracker: Details for all deals\n",
-      "\n",
-      "  * Betting buzz: Lakers favorites to draft Bronny\n",
-      "  * Use our NBA draft simu\n",
-      "ent system, LLM functions as the agent's brain,\n",
-      "complemented by several key components:\n",
-      "\n",
-      "  * **Planning**\n",
-      "    * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\n",
-      "    * Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\n",
-      "  * **Memory**\n",
-      "    * Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\n",
-      "    * Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\n",
-      "  * **Tool use**\n",
-      "    * The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including \n"
-     ]
+     "data": {
+      "text/plain": [
+       "\"  * ESPNFC\\n\\n  * X Games\\n\\n  * SEC Network\\n\\n## ESPN Apps\\n\\n  * ESPN\\n\\n  * ESPN Fantasy\\n\\n## Follow ESPN\\n\\n  * Facebook\\n\\n  * Twitter\\n\\n  * Instagram\\n\\n  * Snapchat\\n\\n  * YouTube\\n\\n  * The ESPN Daily Podcast\\n\\n2023 FIFA Women's World Cup\\n\\n## Follow live: Canada takes on Nigeria in group stage of Women's World Cup\\n\\n2m\\n\\nEPA/Morgan Hancock\\n\\n## TOP HEADLINES\\n\\n  * Snyder fined $60M over findings in investigation\\n  * NFL owners approve $6.05B sale of Commanders\\n  * Jags assistant comes out as gay in NFL milestone\\n  * O's alone atop East after topping slumping Rays\\n  * ACC's Phillips: Never condoned hazing at NU\\n\\n  * Vikings WR Addison cited for driving 140 mph\\n  * 'Taking his time': Patient QB Rodgers wows Jets\\n  * Reyna got U.S. assurances after Berhalter rehire\\n  * NFL Future Power Rankings\\n\\n## USWNT AT THE WORLD CUP\\n\\n### USA VS. VIETNAM: 9 P.M. ET FRIDAY\\n\\n## How do you defend against Alex Morgan? Former opponents sound off\\n\\nThe U.S. forward is unstoppable at this level, scoring 121 goals and adding 49\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_community.document_transformers import Html2TextTransformer\n",
-    "\n",
-    "urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
-    "html2text = Html2TextTransformer()\n",
-    "docs_transformed = html2text.transform_documents(docs)\n",
-    "\n",
-    "print(docs_transformed[0].page_content[1000:2000])\n",
-    "\n",
-    "print(docs_transformed[1].page_content[1000:2000])"
+    "docs_transformed[0].page_content[1000:2000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6045d660",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"t's brain,\\ncomplemented by several key components:\\n\\n  * **Planning**\\n    * Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\n    * Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n  * **Memory**\\n    * Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\n    * Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n  * **Tool use**\\n    * The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution c\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_transformed[1].page_content[1000:2000]"
    ]
   }
  ],
@@ -167,7 +125,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/ai21.ipynb

@@ -17,9 +17,7 @@
    "source": [
     "# AI21LLM\n",
     "\n",
-    "This example goes over how to use LangChain to interact with `AI21` Jurassic models. To use the Jamba model, use the [ChatAI21 object](https://python.langchain.com/v0.2/docs/integrations/chat/ai21/) instead.\n",
-    "\n",
-    "[See a full list of AI21 models and tools on LangChain.](https://pypi.org/project/langchain-ai21/)\n",
+    "This example goes over how to use LangChain to interact with `AI21` models.\n",
     "\n",
     "## Installation"
    ]

docs/docs/integrations/llms/bedrock.ipynb

@@ -34,7 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain_aws"
+    "%pip install --upgrade --quiet  boto3"
    ]
   },
   {
@@ -45,13 +45,74 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_aws import BedrockLLM\n",
+    "from langchain_community.llms import Bedrock\n",
     "\n",
-    "llm = BedrockLLM(\n",
+    "llm = Bedrock(\n",
     "    credentials_profile_name=\"bedrock-admin\", model_id=\"amazon.titan-text-express-v1\"\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using in a conversation chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import ConversationChain\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "\n",
+    "conversation = ConversationChain(\n",
+    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
+    ")\n",
+    "\n",
+    "conversation.predict(input=\"Hi there!\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conversation Chain With Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import Bedrock\n",
+    "from langchain_core.callbacks import StreamingStdOutCallbackHandler\n",
+    "\n",
+    "llm = Bedrock(\n",
+    "    credentials_profile_name=\"bedrock-admin\",\n",
+    "    model_id=\"amazon.titan-text-express-v1\",\n",
+    "    streaming=True,\n",
+    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conversation = ConversationChain(\n",
+    "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
+    ")\n",
+    "\n",
+    "conversation.predict(input=\"Hi there!\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -65,23 +126,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "custom_llm = BedrockLLM(\n",
+    "custom_llm = Bedrock(\n",
     "    credentials_profile_name=\"bedrock-admin\",\n",
     "    provider=\"cohere\",\n",
     "    model_id=\"<Custom model ARN>\",  # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n",
     "    model_kwargs={\"temperature\": 1},\n",
     "    streaming=True,\n",
+    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
     ")\n",
     "\n",
-    "custom_llm.invoke(input=\"What is the recipe of mayonnaise?\")"
+    "conversation = ConversationChain(\n",
+    "    llm=custom_llm, verbose=True, memory=ConversationBufferMemory()\n",
+    ")\n",
+    "conversation.predict(input=\"What is the recipe of mayonnaise?\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Guardrails for Amazon Bedrock\n",
+    "### Guardrails for Amazon Bedrock example \n",
     "\n",
+    "## Guardrails for Amazon Bedrock (Preview) \n",
     "[Guardrails for Amazon Bedrock](https://aws.amazon.com/bedrock/guardrails/) evaluates user inputs and model responses based on use case specific policies, and provides an additional layer of safeguards regardless of the underlying model. Guardrails can be applied across models, including Anthropic Claude, Meta Llama 2, Cohere Command, AI21 Labs Jurassic, and Amazon Titan Text, as well as fine-tuned models.\n",
     "**Note**: Guardrails for Amazon Bedrock is currently in preview and not generally available. Reach out through your usual AWS Support contacts if you’d like access to this feature.\n",
     "In this section, we are going to set up a Bedrock language model with specific guardrails that include tracing capabilities.   "
@@ -108,7 +174,7 @@
     "\n",
     "\n",
     "# Guardrails for Amazon Bedrock with trace\n",
-    "llm = BedrockLLM(\n",
+    "llm = Bedrock(\n",
     "    credentials_profile_name=\"bedrock-admin\",\n",
     "    model_id=\"<Model_ID>\",\n",
     "    model_kwargs={},\n",
@@ -134,7 +200,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/oci_generative_ai.ipynb

@@ -14,15 +14,15 @@
     "Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API.\n",
     "Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available __[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)__ and __[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)__.\n",
     "\n",
-    "This notebook explains how to use OCI's Generative AI complete models with LangChain."
+    "This notebook explains how to use OCI's Genrative AI models with LangChain."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup\n",
-    "Ensure that the oci sdk and the langchain-community package are installed"
+    "### Prerequisite\n",
+    "We will need to install the oci sdk"
    ]
   },
   {
@@ -31,7 +31,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -U oci langchain-community"
+    "!pip install -U oci"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OCI Generative AI API endpoint \n",
+    "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Authentication\n",
+    "The authentication methods supported for this langchain integration are:\n",
+    "\n",
+    "1. API Key\n",
+    "2. Session token\n",
+    "3. Instance principal\n",
+    "4. Resource principal \n",
+    "\n",
+    "These follows the standard SDK authentication methods detailed __[here](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm)__.\n",
+    " "
    ]
   },
   {
@@ -47,121 +71,95 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms.oci_generative_ai import OCIGenAI\n",
+    "from langchain_community.llms import OCIGenAI\n",
     "\n",
+    "# use default authN method API-key\n",
     "llm = OCIGenAI(\n",
-    "    model_id=\"cohere.command\",\n",
+    "    model_id=\"MY_MODEL\",\n",
     "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
     "    compartment_id=\"MY_OCID\",\n",
-    "    model_kwargs={\"temperature\": 0, \"max_tokens\": 500},\n",
     ")\n",
     "\n",
     "response = llm.invoke(\"Tell me one fact about earth\", temperature=0.7)\n",
     "print(response)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Chaining with prompt templates"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.chains import LLMChain\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "\n",
+    "# Use Session Token to authN\n",
     "llm = OCIGenAI(\n",
-    "    model_id=\"cohere.command\",\n",
+    "    model_id=\"MY_MODEL\",\n",
     "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
     "    compartment_id=\"MY_OCID\",\n",
-    "    model_kwargs={\"temperature\": 0, \"max_tokens\": 500},\n",
+    "    auth_type=\"SECURITY_TOKEN\",\n",
+    "    auth_profile=\"MY_PROFILE\",  # replace with your profile name\n",
+    "    model_kwargs={\"temperature\": 0.7, \"top_p\": 0.75, \"max_tokens\": 200},\n",
     ")\n",
     "\n",
     "prompt = PromptTemplate(input_variables=[\"query\"], template=\"{query}\")\n",
-    "llm_chain = prompt | llm\n",
+    "\n",
+    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
     "\n",
     "response = llm_chain.invoke(\"what is the capital of france?\")\n",
     "print(response)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Streaming"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = OCIGenAI(\n",
-    "    model_id=\"cohere.command\",\n",
+    "from langchain_community.embeddings import OCIGenAIEmbeddings\n",
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "embeddings = OCIGenAIEmbeddings(\n",
+    "    model_id=\"MY_EMBEDDING_MODEL\",\n",
     "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
     "    compartment_id=\"MY_OCID\",\n",
-    "    model_kwargs={\"temperature\": 0, \"max_tokens\": 500},\n",
     ")\n",
     "\n",
-    "for chunk in llm.stream(\"Write me a song about sparkling water.\"):\n",
-    "    print(chunk, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Authentication\n",
-    "The authentication methods supported for LlamaIndex are equivalent to those used with other OCI services and follow the __[standard SDK authentication](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm)__ methods, specifically API Key, session token, instance principal, and resource principal.\n",
+    "vectorstore = FAISS.from_texts(\n",
+    "    [\n",
+    "        \"Larry Ellison co-founded Oracle Corporation in 1977 with Bob Miner and Ed Oates.\",\n",
+    "        \"Oracle Corporation is an American multinational computer technology company headquartered in Austin, Texas, United States.\",\n",
+    "    ],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    " \n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
-    "API key is the default authentication method used in the examples above. The following example demonstrates how to use a different authentication method (session token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "llm = OCIGenAI(\n",
-    "    model_id=\"cohere.command\",\n",
+    "    model_id=\"MY_MODEL\",\n",
     "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
     "    compartment_id=\"MY_OCID\",\n",
-    "    auth_type=\"SECURITY_TOKEN\",\n",
-    "    auth_profile=\"MY_PROFILE\",  # replace with your profile name\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dedicated AI Cluster\n",
-    "To access models hosted in a dedicated AI cluster __[create an endpoint](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai-inference/20231130/)__ whose assigned OCID (currently prefixed by ‘ocid1.generativeaiendpoint.oc1.us-chicago-1’) is used as your model ID.\n",
+    ")\n",
     "\n",
-    "When accessing models hosted in a dedicated AI cluster you will need to initialize the OCIGenAI interface with two extra required params (\"provider\" and \"context_size\")."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = OCIGenAI(\n",
-    "    model_id=\"ocid1.generativeaiendpoint.oc1.us-chicago-1....\",\n",
-    "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
-    "    compartment_id=\"DEDICATED_COMPARTMENT_OCID\",\n",
-    "    auth_profile=\"MY_PROFILE\",  # replace with your profile name,\n",
-    "    provider=\"MODEL_PROVIDER\",  # e.g., \"cohere\" or \"meta\"\n",
-    "    context_size=\"MODEL_CONTEXT_SIZE\",  # e.g., 128000\n",
-    ")"
+    "chain = (\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "print(chain.invoke(\"when was oracle founded?\"))\n",
+    "print(chain.invoke(\"where is oracle headquartered?\"))"
    ]
   }
  ],

docs/docs/integrations/platforms/aws.mdx

@@ -18,6 +18,16 @@ pip install langchain-community boto3
 
 ### Bedrock Chat
 
+See a [usage example](/docs/integrations/chat/bedrock).
+
+```python
+from langchain_aws import ChatBedrock
+```
+
+## LLMs
+
+### Bedrock
+
 >[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of 
 > high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, 
 > `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to 
@@ -28,15 +38,6 @@ pip install langchain-community boto3
 > serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy 
 > generative AI capabilities into your applications using the AWS services you are already familiar with.
 
-See a [usage example](/docs/integrations/chat/bedrock).
-
-```python
-from langchain_aws import ChatBedrock
-```
-
-## LLMs
-
-### Bedrock
  
 See a [usage example](/docs/integrations/llms/bedrock).
 

docs/docs/integrations/providers/ascend.mdx

@@ -1,24 +0,0 @@
-# Ascend
-
->[Ascend](https://https://www.hiascend.com/) is Natural Process Unit provide by Huawei
-
-This page covers how to use ascend NPU with LangChain.
-
-### Installation
-
-Install using torch-npu using:
-
-```bash
-pip install torch-npu
-```
-
-Please follow the installation instructions as specified below:
-* Install CANN as shown [here](https://www.hiascend.com/document/detail/zh/canncommercial/700/quickstart/quickstart/quickstart_18_0002.html).
-
-### Embedding Models
-
-See a [usage example](/docs/integrations/text_embedding/ascend).
-
-```python
-from langchain_community.embeddings import AscendEmbeddings
-```

docs/docs/integrations/providers/databricks.md

@@ -1,111 +1,87 @@
 Databricks
 ==========
 
-> [Databricks](https://www.databricks.com/) Intelligence Platform is the world's first data intelligence platform powered by generative AI. Infuse AI into every facet of your business.
+The [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform.
 
 Databricks embraces the LangChain ecosystem in various ways:
 
-1. 🚀 **Model Serving** - Access state-of-the-art LLMs, such as DBRX, Llama3, Mixtral, or your fine-tuned models on [Databricks Model Serving](https://www.databricks.com/product/model-serving), via a highly available and low-latency inference endpoint. LangChain provides LLM (`Databricks`), Chat Model (`ChatDatabricks`), and Embeddings (`DatabricksEmbeddings`) implementations, streamlining the integration of your models hosted on Databricks Model Serving with your LangChain applications.
-2. 📃 **Vector Search** - [Databricks Vector Search](https://www.databricks.com/product/machine-learning/vector-search) is a serverless vector database seamlessly integrated within the Databricks Platform. Using `DatabricksVectorSearch`, you can incorporate the highly scalable and reliable similarity search engine into your LangChain applications.
-3. 📊 **MLflow** - [MLflow](https://mlflow.org/) is an open-source platform to manage full the ML lifecycle, including experiment management, evaluation, tracing, deployment, and more. [MLflow's LangChain Integration](/docs/integrations/providers/mlflow_tracking) streamlines the process of developing and operating modern compound ML systems.
-4. 🌐 **SQL Database** - [Databricks SQL](https://www.databricks.com/product/databricks-sql) is integrated with `SQLDatabase` in LangChain, allowing you to access the auto-optimizing, exceptionally performant data warehouse.
-5. 💡 **Open Models** - Databricks open sources models, such as [DBRX](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm), which are available through the [Hugging Face Hub](https://huggingface.co/databricks/dbrx-instruct). These models can be directly utilized with LangChain, leveraging its integration with the `transformers` library.
+1. Databricks connector for the SQLDatabase Chain: SQLDatabase.from_databricks() provides an easy way to query your data on Databricks through LangChain
+2. Databricks MLflow integrates with LangChain: Tracking and serving LangChain applications with fewer steps
+3. Databricks as an LLM provider: Deploy your fine-tuned LLMs on Databricks via serving endpoints or cluster driver proxy apps, and query it as langchain.llms.Databricks
+4. Databricks Dolly: Databricks open-sourced Dolly which allows for commercial use, and can be accessed through the Hugging Face Hub
 
-Chat Model
-----------
+Databricks connector for the SQLDatabase Chain
+----------------------------------------------
+You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain. 
 
-`ChatDatabricks` is a Chat Model class to access chat endpoints hosted on Databricks, including state-of-the-art models such as Llama3, Mixtral, and DBRX, as well as your own fine-tuned models.
 
-```
-from langchain_community.chat_models.databricks import ChatDatabricks
+Databricks MLflow integrates with LangChain
+-------------------------------------------
 
-chat_model = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
+MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. See the notebook [MLflow Callback Handler](/docs/integrations/providers/mlflow_tracking) for details about MLflow's integration with LangChain.
+
+Databricks provides a fully managed and hosted version of MLflow integrated with enterprise security features, high availability, and other Databricks workspace features such as experiment and run management and notebook revision capture. MLflow on Databricks offers an integrated experience for tracking and securing machine learning model training runs and running machine learning projects. See [MLflow guide](https://docs.databricks.com/mlflow/index.html) for more details.
+
+Databricks MLflow makes it more convenient to develop LangChain applications on Databricks. For MLflow tracking, you don't need to set the tracking uri. For MLflow Model Serving, you can save LangChain Chains in the MLflow langchain flavor, and then register and serve the Chain with a few clicks on Databricks, with credentials securely managed by MLflow Model Serving.
+
+Databricks External Models
+--------------------------
+
+[Databricks External Models](https://docs.databricks.com/generative-ai/external-models/index.html) is a service that is designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. The following example creates an endpoint that serves OpenAI's GPT-4 model and generates a chat response from it:
+
+```python
+from langchain_community.chat_models import ChatDatabricks
+from langchain_core.messages import HumanMessage
+from mlflow.deployments import get_deploy_client
+
+
+client = get_deploy_client("databricks")
+name = f"chat"
+client.create_endpoint(
+    name=name,
+    config={
+        "served_entities": [
+            {
+                "name": "test",
+                "external_model": {
+                    "name": "gpt-4",
+                    "provider": "openai",
+                    "task": "llm/v1/chat",
+                    "openai_config": {
+                        "openai_api_key": "{{secrets/<scope>/<key>}}",
+                    },
+                },
+            }
+        ],
+    },
+)
+chat = ChatDatabricks(endpoint=name, temperature=0.1)
+print(chat([HumanMessage(content="hello")]))
+# -> content='Hello! How can I assist you today?'
 ```
 
-See the [usage example](/docs/integrations/chat/databricks) for more guidance on how to use it within your LangChain application.
+Databricks Foundation Model APIs
+--------------------------------
 
-LLM
----
+[Databricks Foundation Model APIs](https://docs.databricks.com/machine-learning/foundation-models/index.html) allow you to access and query state-of-the-art open source models from dedicated serving endpoints. With Foundation Model APIs, developers can quickly and easily build applications that leverage a high-quality generative AI model without maintaining their own model deployment. The following example uses the `databricks-bge-large-en` endpoint to generate embeddings from  text:
 
-`Databricks` is an LLM class to access completion endpoints hosted on Databricks.
-
-```
-from langchain_community.llm.databricks import Databricks
-
-llm = Databricks(endpoint="your-completion-endpoint")
-```
-
-See the [usage example](/docs/integrations/llms/databricks) for more guidance on how to use it within your LangChain application.
-
-
-Embeddings
-----------
-
-`DatabricksEmbeddings` is an Embeddings class to access text-embedding endpoints hosted on Databricks, including state-of-the-art models such as BGE, as well as your own fine-tuned models.
-
-```
+```python
 from langchain_community.embeddings import DatabricksEmbeddings
 
+
 embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
+print(embeddings.embed_query("hello")[:3])
+# -> [0.051055908203125, 0.007221221923828125, 0.003879547119140625, ...]
 ```
 
-See the [usage example](/docs/integrations/text_embedding/databricks) for more guidance on how to use it within your LangChain application.
+Databricks as an LLM provider
+-----------------------------
+
+The notebook [Wrap Databricks endpoints as LLMs](/docs/integrations/llms/databricks#wrapping-a-serving-endpoint-custom-model) demonstrates how to serve a custom model that has been registered by MLflow as a Databricks endpoint.
+It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. 
 
 
-Vector Search
--------------
+Databricks Vector Search
+------------------------
 
-Databricks Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from [Delta](https://docs.databricks.com/en/introduction/delta-comparison.html) tables managed by [Unity Catalog](https://www.databricks.com/product/unity-catalog) and query them with a simple API to return the most similar vectors.
-
-```
-from langchain_community.vectorstores import DatabricksVectorSearch
-
-dvs = DatabricksVectorSearch(
-    index, text_column="text", embedding=embeddings, columns=["source"]
-)
-docs = dvs.similarity_search("What is vector search?)
-```
-
-See the [usage example](/docs/integrations/vectorstores/databricks_vector_search) for how to set up vector indices and integrate them with LangChain.
-
-
-MLflow Integration
-------------------
-
-In the context of LangChain integration, MLflow provides the following capabilities:
-
-- **Experiment Tracking**: Tracks and stores models, artifacts, and traces from your LangChain experiments.
-- **Dependency Management**: Automatically records dependency libraries, ensuring consistency among development, staging, and production environments.
-- **Model Evaluation** Offers native capabilities for evaluating LangChain applications.
-- **Tracing**: Visually traces data flows through your LangChain application.
-
-See [MLflow LangChain Integration](/docs/integrations/providers/mlflow_tracking) to learn about the full capabilities of using MLflow with LangChain through extensive code examples and guides.
-
-SQLDatabase
------------
-You can connect to Databricks SQL using the SQLDatabase wrapper of LangChain.
-```
-from langchain.sql_database import SQLDatabase
-
-db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")
-```
-
-See [Databricks SQL Agent](https://docs.databricks.com/en/large-language-models/langchain.html#databricks-sql-agent) for how to connect Databricks SQL with your LangChain Agent as a powerful querying tool.
-
-Open Models
------------
-
-To directly integrate Databricks's open models hosted on HuggingFace, you can use the [HuggingFace Integration](/docs/integrations/platforms/huggingface) of LangChain.
-
-```
-from langchain_huggingface import HuggingFaceEndpoint
-
-llm = HuggingFaceEndpoint(
-    repo_id="databricks/dbrx-instruct",
-    task="text-generation",
-    max_new_tokens=512,
-    do_sample=False,
-    repetition_penalty=1.03,
-)
-llm.invoke("What is DBRX model?")
-```
+Databricks Vector Search is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors. See the notebook [Databricks Vector Search](/docs/integrations/vectorstores/databricks_vector_search) for instructions to use it with LangChain.

docs/docs/integrations/providers/ibm.mdx

@@ -28,16 +28,6 @@ import os
 os.environ["WATSONX_APIKEY"] = "your IBM watsonx.ai api key"
 ```
 
-## Chat Model
-
-### ChatWatsonx
-
-See a [usage example](/docs/integrations/chat/ibm_watsonx).
-
-```python
-from langchain_ibm import ChatWatsonx
-```
-
 ## LLMs
 
 ### WatsonxLLM

docs/docs/integrations/providers/oci.mdx

@@ -2,29 +2,27 @@
 
 The `LangChain` integrations related to [Oracle Cloud Infrastructure](https://www.oracle.com/artificial-intelligence/).
 
-## OCI Generative AI
+## LLMs
+
+### OCI Generative AI
 > Oracle Cloud Infrastructure (OCI) [Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) is a fully managed service that provides a set of state-of-the-art,
 > customizable large language models (LLMs) that cover a wide range of use cases, and which are available through a single API.
 > Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned
 > custom models based on your own data on dedicated AI clusters. 
 
-To use, you should have the latest `oci` python SDK and the langchain_community package installed.
+To use, you should have the latest `oci` python SDK installed.
 
 ```bash
-pip install -U oci langchain-community
+pip install -U oci
 ```
 
-See [chat](/docs/integrations/llms/oci_generative_ai), [complete](/docs/integrations/chat/oci_generative_ai), and [embedding](/docs/integrations/text_embedding/oci_generative_ai) usage examples.
+See [usage examples](/docs/integrations/llms/oci_generative_ai).
 
 ```python
-from langchain_community.chat_models import ChatOCIGenAI
-
 from langchain_community.llms import OCIGenAI
-
-from langchain_community.embeddings import OCIGenAIEmbeddings
 ```
 
-## OCI Data Science Model Deployment Endpoint
+### OCI Data Science Model Deployment Endpoint
 
 > [OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a
 > fully managed and serverless platform for data science teams. Using the OCI Data Science
@@ -49,3 +47,12 @@ from langchain_community.llms import OCIModelDeploymentVLLM
 from langchain_community.llms import OCIModelDeploymentTGI
 ```
 
+## Text Embedding Models
+
+### OCI Generative AI
+
+See [usage examples](/docs/integrations/text_embedding/oci_generative_ai).
+
+```python
+from langchain_community.embeddings import OCIGenAIEmbeddings
+```

docs/docs/integrations/providers/pebblo/index.md

@@ -1,21 +0,0 @@
-# Pebblo
-
-[Pebblo](https://www.daxa.ai/pebblo) enables developers to safely load and retrieve data to promote their Gen AI app to deployment without
-worrying about the organization’s compliance and security requirements. The Pebblo SafeLoader identifies semantic topics and entities found in the
-loaded data and the Pebblo SafeRetriever enforces identity and semantic controls on the retrieved context. The results are
-summarized on the UI or a PDF report.
-
-
-## Pebblo Overview:
-
-`Pebblo` provides a safe way to load and retrieve data for Gen AI applications. 
-It includes:
-1. **Identity-aware Safe Loader** that loads data and identifies semantic topics and entities.
-2. **SafeRetrieval**  that enforces identity and semantic controls on the retrieved context.
-3. **User Data Report** that summarizes the data loaded and retrieved.
-
-## Example Notebooks
-
-For a more detailed examples of using Pebblo, see the following notebooks:
-* [PebbloSafeLoader](/docs/integrations/document_loaders/pebblo) shows how to use Pebblo loader to safely load data.
-* [PebbloRetrievalQA](/docs/integrations/providers/pebblo/pebblo_retrieval_qa) shows how to use Pebblo retrieval QA chain to safely retrieve data.

docs/docs/integrations/providers/pebblo/pebblo_retrieval_qa.ipynb

@@ -1,584 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3ce451e9-f8f1-4f27-8c6b-4a93a406d504",
-   "metadata": {},
-   "source": [
-    "# Identity-enabled RAG using PebbloRetrievalQA\n",
-    "\n",
-    "> PebbloRetrievalQA is a Retrieval chain with Identity & Semantic Enforcement for question-answering\n",
-    "against a vector database.\n",
-    "\n",
-    "This notebook covers how to retrieve documents using Identity & Semantic Enforcement (Deny Topics/Entities).\n",
-    "For more details on Pebblo and its SafeRetriever feature visit [Pebblo documentation](https://daxa-ai.github.io/pebblo/retrieval_chain/)\n",
-    "\n",
-    "### Steps:\n",
-    "\n",
-    "1. **Loading Documents:**\n",
-    "We will load documents with authorization and semantic metadata into an in-memory Qdrant vectorstore. This vectorstore will be used as a retriever in PebbloRetrievalQA. \n",
-    "\n",
-    "> **Note:** It is recommended to use [PebbloSafeLoader](https://daxa-ai.github.io/pebblo/rag) as the counterpart for loading documents with authentication and semantic metadata on the ingestion side. `PebbloSafeLoader` guarantees the secure and efficient loading of documents while maintaining the integrity of the metadata.\n",
-    "\n",
-    "\n",
-    "\n",
-    "2. **Testing Enforcement Mechanisms**:\n",
-    "   We will test Identity and Semantic Enforcement separately. For each use case, we will define a specific \"ask\" function with the required contexts (*auth_context* and *semantic_context*) and then pose our questions.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ee16b6b-5dac-4b5c-bb69-3ec87398a33c",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "### Dependencies\n",
-    "\n",
-    "We'll use an OpenAI LLM, OpenAI embeddings and a Qdrant vector store in this walkthrough.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e68494fa-f387-4481-9a6c-58294865d7b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain langchain_core langchain-community langchain-openai qdrant_client"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61498d51-0c38-40e2-adcd-19dfdf4d37ef",
-   "metadata": {},
-   "source": [
-    "### Identity-aware Data Ingestion\n",
-    "\n",
-    "Here we are using Qdrant as a vector database; however, you can use any of the supported vector databases.\n",
-    "\n",
-    "**PebbloRetrievalQA chain supports the following vector databases:**\n",
-    "- Qdrant\n",
-    "- Pinecone\n",
-    "\n",
-    "\n",
-    "**Load vector database with authorization and semantic information in metadata:**\n",
-    "\n",
-    "In this step, we capture the authorization and semantic information of the source document into the `authorized_identities`, `pebblo_semantic_topics`, and `pebblo_semantic_entities` fields within the metadata of the VectorDB entry for each chunk. \n",
-    "\n",
-    "\n",
-    "*NOTE: To use the PebbloRetrievalQA chain, you must always place authorization and semantic metadata in the specified fields. These fields must contain a list of strings.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ae4fcbc1-bdc3-40d2-b2df-8c82cad1f89c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vectordb loaded.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.vectorstores.qdrant import Qdrant\n",
-    "from langchain_core.documents import Document\n",
-    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
-    "from langchain_openai.llms import OpenAI\n",
-    "\n",
-    "llm = OpenAI()\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "collection_name = \"pebblo-identity-and-semantic-rag\"\n",
-    "\n",
-    "page_content = \"\"\"\n",
-    "**ACME Corp Financial Report**\n",
-    "\n",
-    "**Overview:**\n",
-    "ACME Corp, a leading player in the merger and acquisition industry, presents its financial report for the fiscal year ending December 31, 2020. \n",
-    "Despite a challenging economic landscape, ACME Corp demonstrated robust performance and strategic growth.\n",
-    "\n",
-    "**Financial Highlights:**\n",
-    "Revenue soared to $50 million, marking a 15% increase from the previous year, driven by successful deal closures and expansion into new markets. \n",
-    "Net profit reached $12 million, showcasing a healthy margin of 24%.\n",
-    "\n",
-    "**Key Metrics:**\n",
-    "Total assets surged to $80 million, reflecting a 20% growth, highlighting ACME Corp's strong financial position and asset base. \n",
-    "Additionally, the company maintained a conservative debt-to-equity ratio of 0.5, ensuring sustainable financial stability.\n",
-    "\n",
-    "**Future Outlook:**\n",
-    "ACME Corp remains optimistic about the future, with plans to capitalize on emerging opportunities in the global M&A landscape. \n",
-    "The company is committed to delivering value to shareholders while maintaining ethical business practices.\n",
-    "\n",
-    "**Bank Account Details:**\n",
-    "For inquiries or transactions, please refer to ACME Corp's US bank account:\n",
-    "Account Number: 123456789012\n",
-    "Bank Name: Fictitious Bank of America\n",
-    "\"\"\"\n",
-    "\n",
-    "documents = [\n",
-    "    Document(\n",
-    "        **{\n",
-    "            \"page_content\": page_content,\n",
-    "            \"metadata\": {\n",
-    "                \"pebblo_semantic_topics\": [\"financial-report\"],\n",
-    "                \"pebblo_semantic_entities\": [\"us-bank-account-number\"],\n",
-    "                \"authorized_identities\": [\"finance-team\", \"exec-leadership\"],\n",
-    "                \"page\": 0,\n",
-    "                \"source\": \"https://drive.google.com/file/d/xxxxxxxxxxxxx/view\",\n",
-    "                \"title\": \"ACME Corp Financial Report.pdf\",\n",
-    "            },\n",
-    "        }\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "vectordb = Qdrant.from_documents(\n",
-    "    documents,\n",
-    "    embeddings,\n",
-    "    location=\":memory:\",\n",
-    "    collection_name=collection_name,\n",
-    ")\n",
-    "\n",
-    "print(\"Vectordb loaded.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f630bb8b-67ba-41f9-8715-76d006207e75",
-   "metadata": {},
-   "source": [
-    "## Retrieval with Identity Enforcement\n",
-    "\n",
-    "PebbloRetrievalQA chain uses a SafeRetrieval to enforce that the snippets used for in-context are retrieved only from the documents authorized for the user. \n",
-    "To achieve this, the Gen-AI application needs to provide an authorization context for this retrieval chain. \n",
-    "This *auth_context* should be filled with the identity and authorization groups of the user accessing the Gen-AI app.\n",
-    "\n",
-    "\n",
-    "Here is the sample code for the `PebbloRetrievalQA` with `user_auth`(List of user authorizations, which may include their User ID and \n",
-    "    the groups they are part of) from the user accessing the RAG application, passed in `auth_context`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e978bee6-3a8c-459f-ab82-d380d7499b36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chains import PebbloRetrievalQA\n",
-    "from langchain_community.chains.pebblo_retrieval.models import AuthContext, ChainInput\n",
-    "\n",
-    "# Initialize PebbloRetrievalQA chain\n",
-    "qa_chain = PebbloRetrievalQA.from_chain_type(\n",
-    "    llm=llm,\n",
-    "    retriever=vectordb.as_retriever(),\n",
-    "    app_name=\"pebblo-identity-rag\",\n",
-    "    description=\"Identity Enforcement app using PebbloRetrievalQA\",\n",
-    "    owner=\"ACME Corp\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def ask(question: str, auth_context: dict):\n",
-    "    \"\"\"\n",
-    "    Ask a question to the PebbloRetrievalQA chain\n",
-    "    \"\"\"\n",
-    "    auth_context_obj = AuthContext(**auth_context) if auth_context else None\n",
-    "    chain_input_obj = ChainInput(query=question, auth_context=auth_context_obj)\n",
-    "    return qa_chain.invoke(chain_input_obj.dict())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a267e96-70cb-468f-b830-83b65e9b7f6f",
-   "metadata": {},
-   "source": [
-    "### 1. Questions by Authorized User\n",
-    "\n",
-    "We ingested data for authorized identities `[\"finance-team\", \"exec-leadership\"]`, so a user with the authorized identity/group `finance-team` should receive the correct answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "2688fc18-1eac-45a5-be55-aabbe6b25af5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "\n",
-      "Answer: \n",
-      "Revenue: $50 million (15% increase from previous year)\n",
-      "Net profit: $12 million (24% margin)\n",
-      "Total assets: $80 million (20% growth)\n",
-      "Debt-to-equity ratio: 0.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "auth = {\n",
-    "    \"user_id\": \"finance-user@acme.org\",\n",
-    "    \"user_auth\": [\n",
-    "        \"finance-team\",\n",
-    "    ],\n",
-    "}\n",
-    "\n",
-    "question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
-    "resp = ask(question, auth)\n",
-    "print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4db6566-6562-4a49-b19c-6d99299b374e",
-   "metadata": {},
-   "source": [
-    "### 2. Questions by Unauthorized User\n",
-    "\n",
-    "Since the user's authorized identity/group `eng-support` is not included in the authorized identities `[\"finance-team\", \"exec-leadership\"]`, we should not receive an answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "2d736ce3-6e05-48d3-a5e1-fb4e7cccc1ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "\n",
-      "Answer:  I don't know.\n"
-     ]
-    }
-   ],
-   "source": [
-    "auth = {\n",
-    "    \"user_id\": \"eng-user@acme.org\",\n",
-    "    \"user_auth\": [\n",
-    "        \"eng-support\",\n",
-    "    ],\n",
-    "}\n",
-    "\n",
-    "question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
-    "resp = ask(question, auth)\n",
-    "print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33a8afe1-3071-4118-9714-a17cba809ee4",
-   "metadata": {},
-   "source": [
-    "### 3. Using PromptTemplate to provide additional instructions\n",
-    "You can use PromptTemplate to provide additional instructions to the LLM for generating a custom response."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "59c055ba-fdd1-48c6-9bc9-2793eb47438d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "prompt_template = PromptTemplate.from_template(\n",
-    "    \"\"\"\n",
-    "Answer the question using the provided context. \n",
-    "If no context is provided, just say \"I'm sorry, but that information is unavailable, or Access to it is restricted.\".\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    ")\n",
-    "\n",
-    "question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
-    "prompt = prompt_template.format(question=question)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4d27c00-73d9-4ce8-bc70-29535deaf0e2",
-   "metadata": {},
-   "source": [
-    "#### 3.1 Questions by Authorized User"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "e68a13a4-b735-421d-9655-2a9a087ba9e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "\n",
-      "Answer: \n",
-      "Revenue soared to $50 million, marking a 15% increase from the previous year, and net profit reached $12 million, showcasing a healthy margin of 24%. Total assets also grew by 20% to $80 million, and the company maintained a conservative debt-to-equity ratio of 0.5.\n"
-     ]
-    }
-   ],
-   "source": [
-    "auth = {\n",
-    "    \"user_id\": \"finance-user@acme.org\",\n",
-    "    \"user_auth\": [\n",
-    "        \"finance-team\",\n",
-    "    ],\n",
-    "}\n",
-    "resp = ask(prompt, auth)\n",
-    "print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b97a9ca-bdc6-400a-923d-65a8536658be",
-   "metadata": {},
-   "source": [
-    "#### 3.2 Questions by Unauthorized Users"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "438e48c6-96a2-4d5e-81db-47f8c8f37739",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "\n",
-      "Answer: \n",
-      "I'm sorry, but that information is unavailable, or Access to it is restricted.\n"
-     ]
-    }
-   ],
-   "source": [
-    "auth = {\n",
-    "    \"user_id\": \"eng-user@acme.org\",\n",
-    "    \"user_auth\": [\n",
-    "        \"eng-support\",\n",
-    "    ],\n",
-    "}\n",
-    "resp = ask(prompt, auth)\n",
-    "print(f\"Question: {question}\\n\\nAnswer: {resp['result']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4306cab3-d070-405f-a23b-5c6011a61c50",
-   "metadata": {},
-   "source": [
-    "## Retrieval with Semantic Enforcement"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c3757cf-832f-483e-aafe-cb09b5130ec0",
-   "metadata": {},
-   "source": [
-    "The PebbloRetrievalQA chain uses SafeRetrieval to ensure that the snippets used in context are retrieved only from documents that comply with the\n",
-    "provided semantic context.\n",
-    "To achieve this, the Gen-AI application must provide a semantic context for this retrieval chain.\n",
-    "This `semantic_context` should include the topics and entities that should be denied for the user accessing the Gen-AI app.\n",
-    "\n",
-    "Below is a sample code for PebbloRetrievalQA with `topics_to_deny` and `entities_to_deny`. These are passed in `semantic_context` to the chain input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "daf37bf7-9a16-4102-8893-5b698cae1b07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List, Optional\n",
-    "\n",
-    "from langchain_community.chains import PebbloRetrievalQA\n",
-    "from langchain_community.chains.pebblo_retrieval.models import (\n",
-    "    ChainInput,\n",
-    "    SemanticContext,\n",
-    ")\n",
-    "\n",
-    "# Initialize PebbloRetrievalQA chain\n",
-    "qa_chain = PebbloRetrievalQA.from_chain_type(\n",
-    "    llm=llm,\n",
-    "    retriever=vectordb.as_retriever(),\n",
-    "    app_name=\"pebblo-semantic-rag\",\n",
-    "    description=\"Semantic Enforcement app using PebbloRetrievalQA\",\n",
-    "    owner=\"ACME Corp\",\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def ask(\n",
-    "    question: str,\n",
-    "    topics_to_deny: Optional[List[str]] = None,\n",
-    "    entities_to_deny: Optional[List[str]] = None,\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Ask a question to the PebbloRetrievalQA chain\n",
-    "    \"\"\"\n",
-    "    semantic_context = dict()\n",
-    "    if topics_to_deny:\n",
-    "        semantic_context[\"pebblo_semantic_topics\"] = {\"deny\": topics_to_deny}\n",
-    "    if entities_to_deny:\n",
-    "        semantic_context[\"pebblo_semantic_entities\"] = {\"deny\": entities_to_deny}\n",
-    "\n",
-    "    semantic_context_obj = (\n",
-    "        SemanticContext(**semantic_context) if semantic_context else None\n",
-    "    )\n",
-    "    chain_input_obj = ChainInput(query=question, semantic_context=semantic_context_obj)\n",
-    "    return qa_chain.invoke(chain_input_obj.dict())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9718819b-f5cd-4212-9947-d18cd507c8b7",
-   "metadata": {},
-   "source": [
-    "### 1. Without semantic enforcement\n",
-    "\n",
-    "Since no semantic enforcement is applied, the system should return the answer without excluding any context due to the semantic labels associated with the context.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "69158be1-f223-4d14-b61f-f4afdf5af526",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topics to deny: []\n",
-      "Entities to deny: []\n",
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "Answer: \n",
-      "Revenue for ACME Corp increased by 15% to $50 million in 2020, with a net profit of $12 million and a strong asset base of $80 million. The company also maintained a conservative debt-to-equity ratio of 0.5.\n"
-     ]
-    }
-   ],
-   "source": [
-    "topic_to_deny = []\n",
-    "entities_to_deny = []\n",
-    "question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
-    "resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)\n",
-    "print(\n",
-    "    f\"Topics to deny: {topic_to_deny}\\nEntities to deny: {entities_to_deny}\\n\"\n",
-    "    f\"Question: {question}\\nAnswer: {resp['result']}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8789c58-0d64-404e-bc09-92f6952022ac",
-   "metadata": {},
-   "source": [
-    "### 2. Deny financial-report topic\n",
-    "\n",
-    "Data has been ingested with the topics: `[\"financial-report\"]`.\n",
-    "Therefore, an app that denies the `financial-report` topic should not receive an answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "9b17b2fc-eefb-4229-a41e-2f943d2eb48e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topics to deny: ['financial-report']\n",
-      "Entities to deny: []\n",
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "Answer: \n",
-      "\n",
-      "Unfortunately, I do not have access to the financial performance of ACME Corp for the year 2020.\n"
-     ]
-    }
-   ],
-   "source": [
-    "topic_to_deny = [\"financial-report\"]\n",
-    "entities_to_deny = []\n",
-    "question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
-    "resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)\n",
-    "print(\n",
-    "    f\"Topics to deny: {topic_to_deny}\\nEntities to deny: {entities_to_deny}\\n\"\n",
-    "    f\"Question: {question}\\nAnswer: {resp['result']}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "894f21b0-2913-4ef6-b5ed-cbca8f74214d",
-   "metadata": {},
-   "source": [
-    "### 3. Deny us-bank-account-number entity\n",
-    "Since the entity `us-bank-account-number` is denied, the system should not return the answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "2b8abce3-7af3-437f-8999-2866a4b9beda",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Topics to deny: []\n",
-      "Entities to deny: ['us-bank-account-number']\n",
-      "Question: Share the financial performance of ACME Corp for the year 2020\n",
-      "Answer:  I don't have information about ACME Corp's financial performance for 2020.\n"
-     ]
-    }
-   ],
-   "source": [
-    "topic_to_deny = []\n",
-    "entities_to_deny = [\"us-bank-account-number\"]\n",
-    "question = \"Share the financial performance of ACME Corp for the year 2020\"\n",
-    "resp = ask(question, topics_to_deny=topic_to_deny, entities_to_deny=entities_to_deny)\n",
-    "print(\n",
-    "    f\"Topics to deny: {topic_to_deny}\\nEntities to deny: {entities_to_deny}\\n\"\n",
-    "    f\"Question: {question}\\nAnswer: {resp['result']}\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/text_embedding/ascend.ipynb

@@ -1,183 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a636f6f3-00d7-4248-8c36-3da51190e882",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.04053403 -0.05560051 -0.04385472 ...  0.09371872  0.02846981\n",
-      " -0.00576814]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.embeddings import AscendEmbeddings\n",
-    "\n",
-    "model = AscendEmbeddings(\n",
-    "    model_path=\"/root/.cache/modelscope/hub/yangjhchs/acge_text_embedding\",\n",
-    "    device_id=0,\n",
-    "    query_instruction=\"Represend this sentence for searching relevant passages: \",\n",
-    ")\n",
-    "emb = model.embed_query(\"hellow\")\n",
-    "print(emb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8d29ddaa-eef3-4a4e-93d8-0f1c13525fb4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[-0.00348254  0.03098977 -0.00203087 ...  0.08492374  0.03970494\n",
-      "  -0.03372753]\n",
-      " [-0.02198593 -0.01601127  0.00215684 ...  0.06065163  0.00126425\n",
-      "  -0.03634358]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "doc_embs = model.embed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
-    ")\n",
-    "print(doc_embs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "797a720d-c478-4254-be2c-975bc4529f57",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<coroutine object Embeddings.aembed_query at 0x7f9fac699cb0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.aembed_query(\"hellow\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "57e62e53-4d2c-4532-9b77-a46bc3da1130",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-0.04053403, -0.05560051, -0.04385472, ...,  0.09371872,\n",
-       "        0.02846981, -0.00576814], dtype=float32)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await model.aembed_query(\"hellow\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7e260457-8b50-4ca3-8f76-8a76d8bba8c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<coroutine object Embeddings.aembed_documents at 0x7fa093ff1a80>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.aembed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ce954b94-aaac-4d2c-80be-b2988c16af6d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-0.00348254,  0.03098977, -0.00203087, ...,  0.08492374,\n",
-       "         0.03970494, -0.03372753],\n",
-       "       [-0.02198593, -0.01601127,  0.00215684, ...,  0.06065163,\n",
-       "         0.00126425, -0.03634358]], dtype=float32)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await model.aembed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7823d69d-de79-4f95-90dd-38f4bdeb9bcc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/text_embedding/azureopenai.ipynb

@@ -20,16 +20,6 @@
     "Let's load the Azure OpenAI Embedding class with environment variables set to indicate to use Azure endpoints."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "228faf0c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet langchain_openai"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -190,9 +180,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -204,7 +194,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
+   }
   }
  },
  "nbformat": 4,

docs/docs/integrations/text_embedding/jina.ipynb

@@ -5,15 +5,9 @@
    "id": "1c0cf975",
    "metadata": {},
    "source": [
-    "# Jina"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da922b13-eaa8-4cdc-98dd-cf8f3d2e6ffa",
-   "metadata": {},
-   "source": [
-    "Install requirements"
+    "# Jina\n",
+    "\n",
+    "Let's load the Jina Embedding class."
    ]
   },
   {
@@ -26,14 +20,6 @@
     "pip install -U langchain-community"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "7911b286-130d-4971-b77c-7c7a077115b6",
-   "metadata": {},
-   "source": [
-    "Import libraries"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -48,14 +34,6 @@
     "from PIL import Image"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "59aa1c02-1216-43eb-8473-8e0468f0ddb6",
-   "metadata": {},
-   "source": [
-    "## Embed text and queries with Jina embedding models through JinaAI API"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -65,7 +43,9 @@
    "source": [
     "text_embeddings = JinaEmbeddings(\n",
     "    jina_api_key=\"jina_*\", model_name=\"jina-embeddings-v2-base-en\"\n",
-    ")"
+    ")\n",
+    "\n",
+    "image_embeddings = JinaEmbeddings(jina_api_key=\"jina_*\", model_name=\"jina-clip-v1\")"
    ]
   },
   {
@@ -75,7 +55,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "text = \"This is a test document.\""
+    "text = \"This is a test document.\"\n",
+    "\n",
+    "image = \"https://avatars.githubusercontent.com/u/126733545?v=4\"\n",
+    "\n",
+    "description = \"Logo of a parrot and a chain on green background\"\n",
+    "\n",
+    "im = Image.open(requests.get(image, stream=True).raw)\n",
+    "print(\"Image:\")\n",
+    "display(im)"
    ]
   },
   {
@@ -118,40 +106,6 @@
     "print(doc_result)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "338ea747-040e-4ed4-8ddf-9b2285987885",
-   "metadata": {},
-   "source": [
-    "## Embed images and queries with Jina CLIP through JinaAI API"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "575b5867-59fb-4fd1-911b-afee2eaca088",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "multimodal_embeddings = JinaEmbeddings(jina_api_key=\"jina_*\", model_name=\"jina-clip-v1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9b335f5-fa99-4931-95f6-7b187c0e2f30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image = \"https://avatars.githubusercontent.com/u/126733545?v=4\"\n",
-    "\n",
-    "description = \"Logo of a parrot and a chain on green background\"\n",
-    "\n",
-    "im = Image.open(requests.get(image, stream=True).raw)\n",
-    "print(\"Image:\")\n",
-    "display(im)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -159,7 +113,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image_result = multimodal_embeddings.embed_images([image])"
+    "image_result = image_embeddings.embed_images([image])"
    ]
   },
   {
@@ -179,7 +133,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "description_result = multimodal_embeddings.embed_documents([description])"
+    "description_result = image_embeddings.embed_documents([description])"
    ]
   },
   {
@@ -213,6 +167,14 @@
    "source": [
     "print(cosine_similarity)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f280807-a02b-4d4e-8ebd-01be33117999",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -231,7 +193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

docs/docs/integrations/text_embedding/ovhcloud.ipynb

@@ -7,14 +7,12 @@
    "source": [
     "# OVHcloud\n",
     "\n",
-    "> In order to use this model you need to create a new token on the AI Endpoints website: https://endpoints.ai.cloud.ovh.net/.\n",
-    "\n",
     "This notebook explains how to use OVHCloudEmbeddings, which is included in the langchain_community package, to embed texts in langchain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "3da0fce0",
    "metadata": {},
    "outputs": [
@@ -37,20 +35,6 @@
     "\"\"\" verify \"\"\"\n",
     "print(f\"Embedding generated by OVHCloudEmbeddings: {embed}\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47c9af05-4d25-40f2-9305-7bccf1e14c64",
-   "metadata": {},
-   "source": [
-    "## Further reading\n",
-    "- [Enhance your applications with AI Endpoints](https://blog.ovhcloud.com/enhance-your-applications-with-ai-endpoints/)\n",
-    "- [How to use AI Endpoints and LangChain4j](https://blog.ovhcloud.com/how-to-use-ai-endpoints-and-langchain4j/)\n",
-    "- [LLMs streaming with AI Endpoints and LangChain4j](https://blog.ovhcloud.com/llms-streaming-with-ai-endpoints-and-langchain4j/)\n",
-    "- [How to use AI Endpoints and LangChain to create a chatbot](https://blog.ovhcloud.com/how-to-use-ai-endpoints-and-langchain-to-create-a-chatbot/)\n",
-    "- [How to use AI Endpoints, LangChain and Javascript to create a chatbot](https://blog.ovhcloud.com/how-to-use-ai-endpoints-langchain-and-javascript-to-create-a-chatbot/)\n",
-    "- [RAG chatbot using AI Endpoints and LangChain](https://blog.ovhcloud.com/rag-chatbot-using-ai-endpoints-and-langchain/)"
-   ]
   }
  ],
  "metadata": {

docs/docs/integrations/tools/wikidata.ipynb

@@ -23,12 +23,12 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet \"wikibase-rest-api-client<0.2\" mediawikiapi"
+    "%pip install --upgrade --quiet  wikibase-rest-api-client mediawikiapi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "955988a1-ebc2-4c9a-9298-c493fe842de1",
    "metadata": {
     "execution": {
@@ -39,6 +39,26 @@
      "shell.execute_reply.started": "2024-03-06T22:55:15.973114Z"
     }
    },
+   "outputs": [],
+   "source": [
+    "from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun\n",
+    "\n",
+    "wikidata = WikidataQueryRun(api_wrapper=WikidataAPIWrapper())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9926a8a7-3e4e-4a97-ba43-7e5a274b9561",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-06T22:54:38.551998Z",
+     "iopub.status.busy": "2024-03-06T22:54:38.551266Z",
+     "iopub.status.idle": "2024-03-06T22:54:51.913177Z",
+     "shell.execute_reply": "2024-03-06T22:54:51.911636Z",
+     "shell.execute_reply.started": "2024-03-06T22:54:38.551955Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -57,7 +77,7 @@
       "sport: athletics\n",
       "place of birth: Maida Vale, Warrington Lodge\n",
       "educated at: King's College, Princeton University, Sherborne School, Hazlehurst Community Primary School\n",
-      "employer: Victoria University of Manchester, Government Communications Headquarters, University of Cambridge, National Physical Laboratory (United Kingdom)\n",
+      "employer: Victoria University of Manchester, Government Communications Headquarters, University of Cambridge, National Physical Laboratory\n",
       "place of death: Wilmslow\n",
       "field of work: cryptanalysis, computer science, mathematics, logic, cryptography\n",
       "cause of death: cyanide poisoning\n",
@@ -78,17 +98,13 @@
     }
    ],
    "source": [
-    "from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun\n",
-    "\n",
-    "wikidata = WikidataQueryRun(api_wrapper=WikidataAPIWrapper())\n",
-    "\n",
     "print(wikidata.run(\"Alan Turing\"))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7188d62f",
+   "id": "2762aa55-92bd-4e50-b433-8c5c37da465f",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -110,7 +126,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/zenguard.ipynb

@@ -1,178 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ZenGuard AI\n",
-    "\n",
-    "<a href=\"https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/integrations/tools/zenguard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\" /></a>\n",
-    "\n",
-    "This tool lets you quickly set up [ZenGuard AI](https://www.zenguard.ai/) in your Langchain-powered application. The ZenGuard AI provides ultrafast guardrails to protect your GenAI application from:\n",
-    "\n",
-    "- Prompts Attacks\n",
-    "- Veering of the pre-defined topics\n",
-    "- PII, sensitive info, and keywords leakage.\n",
-    "- Toxicity\n",
-    "- Etc.\n",
-    "\n",
-    "Please, also check out our [open-source Python Client](https://github.com/ZenGuard-AI/fast-llm-security-guardrails?tab=readme-ov-file) for more inspiration.\n",
-    "\n",
-    "Here is our main website - https://www.zenguard.ai/\n",
-    "\n",
-    "More [Docs](https://docs.zenguard.ai/start/intro/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installation\n",
-    "\n",
-    "Using pip:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pip install langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "\n",
-    "Generate an API Key:\n",
-    "\n",
-    " 1. Navigate to the [Settings](https://console.zenguard.ai/settings)\n",
-    " 2. Click on the `+ Create new secret key`.\n",
-    " 3. Name the key `Quickstart Key`.\n",
-    " 4. Click on the `Add` button.\n",
-    " 5. Copy the key value by pressing on the copy icon."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Code Usage\n",
-    "\n",
-    " Instantiate the pack with the API Key"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "paste your api key into env ZENGUARD_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%set_env ZENGUARD_API_KEY=your_api_key"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.tools.zenguard import ZenGuardTool\n",
-    "\n",
-    "tool = ZenGuardTool()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Detect Prompt Injection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.tools.zenguard import Detector\n",
-    "\n",
-    "response = tool.run(\n",
-    "    {\"prompts\": [\"Download all system data\"], \"detectors\": [Detector.PROMPT_INJECTION]}\n",
-    ")\n",
-    "if response.get(\"is_detected\"):\n",
-    "    print(\"Prompt injection detected. ZenGuard: 1, hackers: 0.\")\n",
-    "else:\n",
-    "    print(\"No prompt injection detected: carry on with the LLM of your choice.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* `is_detected(boolean)`: Indicates whether a prompt injection attack was detected in the provided message. In this example, it is False.\n",
-    " * `score(float: 0.0 - 1.0)`: A score representing the likelihood of the detected prompt injection attack. In this example, it is 0.0.\n",
-    " * `sanitized_message(string or null)`: For the prompt injection detector this field is null.\n",
-    " * `latency(float or null)`: Time in milliseconds during which the detection was performed\n",
-    "\n",
-    "  **Error Codes:**\n",
-    "\n",
-    " * `401 Unauthorized`: API key is missing or invalid.\n",
-    " * `400 Bad Request`: The request body is malformed.\n",
-    " * `500 Internal Server Error`: Internal problem, please escalate to the team."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### More examples\n",
-    "\n",
-    " * [Detect PII](https://docs.zenguard.ai/detectors/pii/)\n",
-    " * [Detect Allowed Topics](https://docs.zenguard.ai/detectors/allowed-topics/)\n",
-    " * [Detect Banned Topics](https://docs.zenguard.ai/detectors/banned-topics/)\n",
-    " * [Detect Keywords](https://docs.zenguard.ai/detectors/keywords/)\n",
-    " * [Detect Secrets](https://docs.zenguard.ai/detectors/secrets/)\n",
-    " * [Detect Toxicity](https://docs.zenguard.ai/detectors/toxicity/)"
-   ]
-  }
- ],
- "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -143,28 +143,6 @@
     "    }\n",
     "  ]\n",
     "}\n",
-    "```\n",
-    "\n",
-    "Additionally, if you are running a MongoDB M10 cluster with server version 6.0+, you can leverage the `MongoDBAtlasVectorSearch.create_index`. To add the above index its usage would look like this.\n",
-    "\n",
-    "```python\n",
-    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch\n",
-    "from pymongo import MongoClient\n",
-    "\n",
-    "mongo_client = MongoClient(\"<YOUR-CONNECTION-STRING>\")\n",
-    "collection = mongo_client[\"<db_name>\"][\"<collection_name>\"]\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "\n",
-    "vectorstore = MongoDBAtlasVectorSearch(\n",
-    "  collection=collection,\n",
-    "  embedding=embeddings,\n",
-    "  index_name=\"<ATLAS_VECTOR_SEARCH_INDEX_NAME>\",\n",
-    "  relevance_score_fn=\"cosine\",\n",
-    ")\n",
-    "\n",
-    "# Creates an index using the index_name provided and relevance_score_fn type\n",
-    "vectorstore.create_index(dimensions=1536)\n",
     "```"
    ]
   },
@@ -318,16 +296,6 @@
     "    }\n",
     "  ]\n",
     "}\n",
-    "```\n",
-    "\n",
-    "You can also update the index programmatically using the `MongoDBAtlasVectorSearch.create_index` method.\n",
-    "\n",
-    "```python\n",
-    "vectorstore.create_index(\n",
-    "  dimensions=1536,\n",
-    "  filters=[{\"type\":\"filter\", \"path\":\"page\"}],\n",
-    "  update=True\n",
-    ")\n",
     "```"
    ]
   },

docs/docs/introduction.mdx

@@ -8,10 +8,9 @@ sidebar_class_name: hidden
 **LangChain** is a framework for developing applications powered by large language models (LLMs).
 
 LangChain simplifies every stage of the LLM application lifecycle:
-- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/platforms/).
-Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
+- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel) and [components](/docs/concepts). Hit the ground running using [third-party integrations](/docs/integrations/platforms/) and [Templates](/docs/templates).
 - **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
-- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
+- **Deployment**: Turn any chain into an API with [LangServe](/docs/langserve).
 
 import ThemedImage from '@theme/ThemedImage';
 import useBaseUrl from '@docusaurus/useBaseUrl';
@@ -19,8 +18,8 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 <ThemedImage
   alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
   sources={{
-    light: useBaseUrl('/svg/langchain_stack_062024.svg'),
-    dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
+    light: useBaseUrl('/svg/langchain_stack.svg'),
+    dark: useBaseUrl('/svg/langchain_stack_dark.svg'),
   }}
   title="LangChain Framework Overview"
 />
@@ -31,7 +30,7 @@ Concretely, the framework consists of the following open-source libraries:
 - **`langchain-community`**: Third party integrations.
   - Partner packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`langchain-core`**.
 - **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
+- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
 - **[LangServe](/docs/langserve)**: Deploy LangChain chains as REST APIs.
 - **[LangSmith](https://docs.smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
 
@@ -44,17 +43,15 @@ These docs focus on the Python LangChain library. [Head here](https://js.langcha
 
 ## [Tutorials](/docs/tutorials)
 
-If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials section](/docs/tutorials).
+If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials](/docs/tutorials).
 This is the best place to get started.
 
 These are the best ones to get started with:
-
 - [Build a Simple LLM Application](/docs/tutorials/llm_chain)
 - [Build a Chatbot](/docs/tutorials/chatbot)
 - [Build an Agent](/docs/tutorials/agents)
-- [Introduction to LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
 
-Explore the full list of LangChain tutorials [here](/docs/tutorials), and check out other [LangGraph tutorials here](https://langchain-ai.github.io/langgraph/tutorials/).
+Explore the full list of tutorials [here](/docs/tutorials).
 
 
 ## [How-to guides](/docs/how_to)
@@ -63,14 +60,10 @@ Explore the full list of LangChain tutorials [here](/docs/tutorials), and check
 These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://api.python.langchain.com/en/latest/).
 However, these guides will help you quickly accomplish common tasks.
 
-Check out [LangGraph-specific how-tos here](https://langchain-ai.github.io/langgraph/how-tos/).
-
 ## [Conceptual guide](/docs/concepts)
 
 Introductions to all the key parts of LangChain you’ll need to know! [Here](/docs/concepts) you'll find high level explanations of all LangChain concepts.
 
-For a deeper dive into LangGraph concepts, check out [this page](https://langchain-ai.github.io/langgraph/concepts/).
-
 ## [API reference](https://api.python.langchain.com)
 Head to the reference section for full documentation of all classes and methods in the LangChain Python packages.
 
@@ -80,7 +73,10 @@ Head to the reference section for full documentation of all classes and methods
 Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
 
 ### [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph)
-Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
+Build stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
+
+### [🦜🏓 LangServe](/docs/langserve)
+Deploy LangChain runnables and chains as REST APIs.
 
 
 ## Additional resources

docs/docs/tutorials/agents.ipynb

@@ -343,7 +343,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ContentString: \n",
+      "ContentString: [{'id': 'toolu_01VTP7DUvSfgtYxsq9x4EwMp', 'input': {'query': 'weather san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "ToolCalls: [{'name': 'tavily_search_results_json', 'args': {'query': 'weather san francisco'}, 'id': 'toolu_01VTP7DUvSfgtYxsq9x4EwMp'}]\n"
      ]
     }
@@ -362,7 +362,7 @@
    "source": [
     "We can see that there's now no text content, but there is a tool call! It wants us to call the Tavily Search tool.\n",
     "\n",
-    "This isn't calling that tool yet - it's just telling us to. In order to actually call it, we'll want to create our agent."
+    "This isn't calling that tool yet - it's just telling us to. In order to actually calll it, we'll want to create our agent."
    ]
   },
   {

docs/docs/tutorials/chatbot.ipynb

@@ -143,7 +143,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 12, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d939617f-0c3b-45e9-a93f-13dafecbd4b5-0', usage_metadata={'input_tokens': 12, 'output_tokens': 10, 'total_tokens': 22})"
+       "AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 12, 'total_tokens': 22}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-8ecc8a9f-8b32-49ad-8e41-5caa26282f76-0', usage_metadata={'input_tokens': 12, 'output_tokens': 10, 'total_tokens': 22})"
       ]
      },
      "execution_count": 2,
@@ -172,7 +172,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm sorry, I don't have access to personal information unless you provide it to me. How may I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 26, 'prompt_tokens': 12, 'total_tokens': 38}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-47bc8c20-af7b-4fd2-9345-f0e9fdf18ce3-0', usage_metadata={'input_tokens': 12, 'output_tokens': 26, 'total_tokens': 38})"
+       "AIMessage(content=\"I'm sorry, I don't have access to that information.\", response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 12, 'total_tokens': 25}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-4e0066e8-0dcc-4aea-b4f9-b9029c81724f-0', usage_metadata={'input_tokens': 12, 'output_tokens': 13, 'total_tokens': 25})"
       ]
      },
      "execution_count": 3,
@@ -204,7 +204,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Your name is Bob. How can I help you, Bob?', response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 35, 'total_tokens': 48}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9f90291b-4df9-41dc-9ecf-1ee1081f4490-0', usage_metadata={'input_tokens': 35, 'output_tokens': 13, 'total_tokens': 48})"
+       "AIMessage(content='Your name is Bob. How can I assist you today, Bob?', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 35, 'total_tokens': 49}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-c377d868-1bfe-491a-82fb-1f9122939796-0', usage_metadata={'input_tokens': 35, 'output_tokens': 14, 'total_tokens': 49})"
       ]
      },
      "execution_count": 4,
@@ -261,7 +261,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "After that, we can import the relevant classes and set up our chain which wraps the model and adds in this message history. A key part here is the function we pass into as the `get_session_history`. This function is expected to take in a `session_id` and return a Message History object. This `session_id` is used to distinguish between separate conversations, and should be passed in as part of the config when calling the new chain (we'll show how to do that)."
+    "After that, we can import the relevant classes and set up our chain which wraps the model and adds in this message history. A key part here is the function we pass into as the `get_session_history`. This function is expected to take in a `session_id` and return a Message History object. This `session_id` is used to distinguish between separate conversations, and should be passed in as part of the config when calling the new chain (we'll show how to do that."
    ]
   },
   {
@@ -307,10 +307,17 @@
    "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run 9bdaa45d-604e-4891-9b0a-28754985f10b not found for run 271bd46a-f980-407a-af8a-9399420bce8d. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "'Hi Bob! How can I assist you today?'"
+       "'Hello Bob! How can I assist you today?'"
       ]
      },
      "execution_count": 8,
@@ -332,10 +339,17 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run 16482292-535c-449d-8a9d-d0fccf5112eb not found for run 7f2e501a-d5b4-4d8c-924b-aae9eb9d7267. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "'Your name is Bob. How can I help you today, Bob?'"
+       "'Your name is Bob. How can I assist you today, Bob?'"
       ]
      },
      "execution_count": 9,
@@ -364,10 +378,17 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run c14d7130-04c5-445f-9e22-442f7c7e8f07 not found for run 946beadc-5cf1-468f-bac4-ca5ddc10ea73. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "\"I'm sorry, I cannot determine your name as I am an AI assistant and do not have access to that information.\""
+       "\"I'm sorry, I don't know your name as you have not provided it.\""
       ]
      },
      "execution_count": 10,
@@ -398,6 +419,13 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run 4f61611c-3875-4b2d-9f89-af452866d55a not found for run 066a30b1-bbb0-4fee-a035-7fdb41c28d91. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -520,10 +548,17 @@
    "execution_count": 16,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run 51e624b3-19fd-435f-b580-2a3e4f2d0dc9 not found for run b411f007-b2ad-48c3-968c-aa5ecbb58aea. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "'Hello, Jim! How can I assist you today?'"
+       "'Hello Jim! How can I assist you today?'"
       ]
      },
      "execution_count": 16,
@@ -545,6 +580,13 @@
    "execution_count": 17,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run a30b22cd-698f-48a1-94a0-1a172242e292 not found for run 52b0b60d-5d2a-4610-a572-037602792ad6. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -656,6 +698,13 @@
    "execution_count": 22,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run d02b7778-4a91-4831-ace9-b33bb456dc90 not found for run ee0a20dd-5b9e-4862-b3c9-8e2e72b8eb82. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -681,6 +730,13 @@
    "execution_count": 23,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run 12422d4c-6494-490e-845e-08dcc1c6a4b9 not found for run a82eb759-f51d-4488-871b-6e2d601b4128. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -725,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -740,7 +796,7 @@
        " AIMessage(content='yes!')]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -785,16 +841,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"I'm sorry, but I don't have access to your personal information. How can I assist you today?\""
+       "\"I'm sorry, I don't have access to personal information. How can I assist you today?\""
       ]
      },
-     "execution_count": 25,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -828,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -837,7 +893,7 @@
        "'You asked \"what\\'s 2 + 2?\"'"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -861,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,16 +932,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run e1bb2af3-192b-4bd1-8734-6d2dff1d80b6 not found for run 0c734998-cf16-4708-8658-043a6c7b4a91. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "\"I'm sorry, I don't have access to that information. How can I assist you today?\""
+       "\"I'm sorry, I don't have access to your name. How can I assist you today?\""
       ]
      },
-     "execution_count": 28,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,16 +974,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run 181a1f04-9176-4837-80e8-ce74866775a2 not found for run ad402c5a-8341-4c62-ac58-cdf923b3b9ec. Treating as a root run.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "\"You haven't asked a math problem yet. Feel free to ask any math-related question you have, and I'll be happy to help you with it.\""
+       "\"You haven't asked a math problem yet. Feel free to ask any math question you have, and I'll do my best to help you with it.\""
       ]
      },
-     "execution_count": 29,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -959,14 +1029,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parent run e0ee52b6-1261-4f2d-98ca-f78c9019684b not found for run 0f6d7995-c32c-4bdb-b7a6-b3d932c13389. Treating as a root run.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|Hi| Todd|!| Sure|,| here|'s| a| joke| for| you|:| Why| couldn|'t| the| bicycle| find| its| way| home|?| Because| it| lost| its| bearings|!| 😄||"
+      "|Sure|,| Todd|!| Here|'s| a| joke| for| you|:\n",
+      "\n",
+      "|Why| don|'t| scientists| trust| atoms|?\n",
+      "\n",
+      "|Because| they| make| up| everything|!||"
      ]
     }
    ],
@@ -1003,9 +1084,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-2",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-2"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1017,7 +1098,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/llm_chain.ipynb

@@ -231,7 +231,7 @@
    "id": "d508b79d",
    "metadata": {},
    "source": [
-    "More commonly, we can \"chain\" the model with this output parser. This means this output parser will get called every time in this chain. This chain takes on the input type of the language model (string or list of message) and returns the output type of the output parser (string).\n",
+    "More commonly, we can \"chain\" the model with this output parser. This means this output parser will get called everytime in this chain. This chain takes on the input type of the language model (string or list of message) and returns the output type of the output parser (string).\n",
     "\n",
     "We can easily create the chain using the `|` operator. The `|` operator is used in LangChain to combine two elements together."
    ]

docs/docs/tutorials/pdf_qa.ipynb

@@ -122,7 +122,7 @@
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs customVarName=\"llm\" openaiParams={`model=\"gpt-4o\"`} />\n",
+    "<ChatModelTabs openaiParams={`model=\"gpt-4o\"`} />\n",
     "```"
    ]
   },

docs/docs/tutorials/rag.ipynb

@@ -41,7 +41,7 @@
     "### Indexing\n",
     "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/#document-loaders).\n",
     "2. **Split**: [Text splitters](/docs/concepts/#text-splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won't fit in a model's finite context window.\n",
-    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vector-stores) and [Embeddings](/docs/concepts/#embedding-models) model.\n",
+    "3. **Store**: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a [VectorStore](/docs/concepts/#vectorstores) and [Embeddings](/docs/concepts/#embedding-models) model.\n",
     "\n",
     "![index_diagram](../../static/img/rag_indexing.png)\n",
     "\n",

docs/docs/tutorials/sql_qa.ipynb

@@ -48,14 +48,14 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --upgrade --quiet langchain langchain-community langchain-openai faiss-cpu"
+    "%pip install --upgrade --quiet  langchain langchain-community langchain-openai"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use an OpenAI model and a [FAISS-powered vector store](/docs/integrations/vectorstores/faiss/) in this guide."
+    "We will use an OpenAI model in this guide."
    ]
   },
   {

docs/docusaurus.config.js

@@ -237,6 +237,10 @@ const config = {
                 href: "https://docs.smith.langchain.com/",
                 label: "LangSmith Docs",
               },
+              {
+                href: "https://github.com/langchain-ai/langserve",
+                label: "LangServe GitHub",
+              },
               {
                 href: "https://github.com/langchain-ai/langchain/tree/master/templates",
                 label: "Templates GitHub",

docs/scripts/model_feat_table.py

@@ -17,24 +17,28 @@ CHAT_MODEL_IGNORE = ("FakeListChatModel", "HumanInputChatModel")
 CHAT_MODEL_FEAT_TABLE = {
     "ChatAnthropic": {
         "tool_calling": True,
+        "structured_output": True,
         "multimodal": True,
         "package": "langchain-anthropic",
         "link": "/docs/integrations/chat/anthropic/",
     },
     "ChatMistralAI": {
         "tool_calling": True,
+        "structured_output": True,
         "json_model": True,
         "package": "langchain-mistralai",
         "link": "/docs/integrations/chat/mistralai/",
     },
     "ChatFireworks": {
         "tool_calling": True,
+        "structured_output": True,
         "json_mode": True,
         "package": "langchain-fireworks",
         "link": "/docs/integrations/chat/fireworks/",
     },
     "AzureChatOpenAI": {
         "tool_calling": True,
+        "structured_output": True,
         "json_mode": True,
         "multimodal": True,
         "package": "langchain-openai",
@@ -42,6 +46,7 @@ CHAT_MODEL_FEAT_TABLE = {
     },
     "ChatOpenAI": {
         "tool_calling": True,
+        "structured_output": True,
         "json_mode": True,
         "multimodal": True,
         "package": "langchain-openai",
@@ -49,12 +54,14 @@ CHAT_MODEL_FEAT_TABLE = {
     },
     "ChatTogether": {
         "tool_calling": True,
+        "structured_output": True,
         "json_mode": True,
         "package": "langchain-together",
         "link": "/docs/integrations/chat/together/",
     },
     "ChatVertexAI": {
         "tool_calling": True,
+        "structured_output": True,
         "multimodal": True,
         "package": "langchain-google-vertexai",
         "link": "/docs/integrations/chat/google_vertex_ai_palm/",
@@ -67,12 +74,14 @@ CHAT_MODEL_FEAT_TABLE = {
     },
     "ChatGroq": {
         "tool_calling": True,
+        "structured_output": True,
         "json_mode": True,
         "package": "langchain-groq",
         "link": "/docs/integrations/chat/groq/",
     },
     "ChatCohere": {
         "tool_calling": True,
+        "structured_output": True,
         "package": "langchain-cohere",
         "link": "/docs/integrations/chat/cohere/",
     },
@@ -95,25 +104,24 @@ CHAT_MODEL_FEAT_TABLE = {
     },
     "vLLM Chat (via ChatOpenAI)": {
         "local": True,
-        "package": "langchain-openai",
+        "package": "langchain-community",
         "link": "/docs/integrations/chat/vllm/",
     },
     "ChatEdenAI": {
         "tool_calling": True,
+        "structured_output": True,
         "package": "langchain-community",
         "link": "/docs/integrations/chat/edenai/",
     },
     "ChatLlamaCpp": {
         "tool_calling": True,
+        "structured_output": True,
         "local": True,
         "package": "langchain-community",
         "link": "/docs/integrations/chat/llamacpp",
     },
 }
 
-for feats in CHAT_MODEL_FEAT_TABLE.values():
-    feats["structured_output"] = feats.get("tool_calling", False)
-
 
 LLM_TEMPLATE = """\
 ---
@@ -224,7 +232,7 @@ def get_chat_model_table() -> str:
     ]
     title = [
         "Model",
-        "[Tool calling](/docs/how_to/tool_calling)",
+        "[Tool calling](/docs/how_to/tool_calling/)",
         "[Structured output](/docs/how_to/structured_output/)",
         "JSON mode",
         "Local",

docs/sidebars.js

@@ -21,9 +21,9 @@
 module.exports = {
   docs: [
     {
-      type: "doc",
-      label: "Introduction",
-      id: "introduction",
+          type: "doc",
+          label: "Introduction",
+          id: "introduction",
     },
     {
       type: "category",
@@ -64,6 +64,7 @@ module.exports = {
           href: "https://langchain-ai.github.io/langgraph/",
           label: "🦜🕸️ LangGraph"
         },
+        "langserve",
       ],
     },
     {
@@ -381,19 +382,11 @@ module.exports = {
       type: "category",
       label: "Contributing",
       items: [
-        "contributing/index",
-        "contributing/repo_structure",
-        "contributing/code/index",
-        { type: "doc", id: "contributing/code/guidelines", className: "hidden" },
-        { type: "doc", id: "contributing/code/setup", className: "hidden" },
-        "contributing/integrations",
-        "contributing/documentation/index",
-        { type: "doc", id: "contributing/documentation/style_guide", className: "hidden" },
-        { type: "doc", id: "contributing/documentation/setup", className: "hidden" },
-        "contributing/testing",
-        "contributing/faq",
+        {
+          type: "autogenerated",
+          dirName: "contributing",
+        },
       ],
-      collapsible: false,
     },
   ],
 };

docs/vercel_requirements.txt

@@ -7,5 +7,5 @@ langchain-cohere
 langchain-astradb
 langchain-nvidia-ai-endpoints
 langchain-elasticsearch
-urllib3==1.26.19
+urllib3==1.26.18
 nbconvert==7.16.4

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

@@ -19,7 +19,7 @@
     "\n",
     "- TODO: Make sure API reference link is correct.\n",
     "\n",
-    "This will help you getting started with __ModuleName__ [chat models](/docs/concepts/#chat-models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
+    "This notebook provides a quick overview for getting started with __ModuleName__ [chat models](/docs/concepts/#chat-models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
     "\n",
     "- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/v0.2/docs/integrations/chat/openai/ for an example.\n",
     "\n",
@@ -35,7 +35,7 @@
     "| [Chat__ModuleName__](https://api.python.langchain.com/en/latest/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
     "\n",
     "### 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",
+    "| [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",
@@ -253,7 +253,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py

@@ -1,7 +1,6 @@
 """__ModuleName__ document loader."""
 
 from typing import Iterator
-
 from langchain_core.document_loaders.base import BaseLoader
 from langchain_core.documents import Document
 
@@ -11,7 +10,7 @@ class __ModuleName__Loader(BaseLoader):
     # https://github.com/langchain-ai/langchain/blob/869523ad728e6b76d77f170cce13925b4ebc3c1e/libs/community/langchain_community/document_loaders/recursive_url_loader.py#L54
     """
     __ModuleName__ document loader integration
-
+    
     # TODO: Replace with relevant packages, env vars.
     Setup:
         Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
@@ -48,7 +47,7 @@ class __ModuleName__Loader(BaseLoader):
 
         .. code-block:: python
 
-            TODO: Example output
+            TODO: Example output  
 
     # TODO: Delete if async load is not implemented
     Async load:
@@ -67,6 +66,6 @@ class __ModuleName__Loader(BaseLoader):
     # Do not implement load(), a default implementation is already available.
     def lazy_load(self) -> Iterator[Document]:
         raise NotImplementedError()
-
+    
     # TODO: Implement if you would like to change default BaseLoader implementation
-    # async def alazy_load(self) -> AsyncIterator[Document]:
+    # async def alazy_load(self) -> AsyncIterator[Document]:

libs/cli/langchain_cli/namespaces/integration.py

@@ -153,9 +153,7 @@ def create_doc(
     component_type: Annotated[
         str,
         typer.Option(
-            help=(
-                "The type of component. Currently only 'ChatModel', 'DocumentLoader' supported."
-            ),
+            help=("The type of component. Currently only 'ChatModel', 'DocumentLoader' supported."),
         ),
     ] = "ChatModel",
     destination_dir: Annotated[
@@ -199,14 +197,9 @@ def create_doc(
 
     # copy over template from ../integration_template
     if component_type == "ChatModel":
-        docs_template = (
-            Path(__file__).parents[1] / "integration_template/docs/chat.ipynb"
-        )
+        docs_template = Path(__file__).parents[1] / "integration_template/docs/chat.ipynb"
     elif component_type == "DocumentLoader":
-        docs_template = (
-            Path(__file__).parents[1]
-            / "integration_template/docs/document_loaders.ipynb"
-        )
+        docs_template = Path(__file__).parents[1] / "integration_template/docs/document_loaders.ipynb"
     shutil.copy(docs_template, destination_path)
 
     # replacements in file

libs/community/README.md

@@ -15,7 +15,7 @@ LangChain Community contains third-party integrations that implement the base in
 
 For full documentation see the [API reference](https://api.python.langchain.com/en/stable/community_api_reference.html).
 
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](../../docs/static/svg/langchain_stack_062024.svg "LangChain Framework Overview")
+![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](../../docs/static/img/langchain_stack.png "LangChain Framework Overview")
 
 ## 📕 Releases & Versioning
 
@@ -27,4 +27,4 @@ All changes will be accompanied by a patch version increase.
 
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
 
-For detailed information on how to contribute, see the [Contributing Guide](https://python.langchain.com/docs/contributing/).
+For detailed information on how to contribute, see the [Contributing Guide](https://python.langchain.com/docs/contributing/).

libs/community/extended_testing_deps.txt

@@ -23,7 +23,7 @@ faiss-cpu>=1,<2
 feedparser>=6.0.10,<7
 fireworks-ai>=0.9.0,<0.10
 friendli-client>=1.2.4,<2
-geopandas>=0.13.1
+geopandas>=0.13.1,<0.14
 gitpython>=3.1.32,<4
 google-cloud-documentai>=2.20.1,<3
 gql>=3.4.1,<4
@@ -46,7 +46,7 @@ mwxml>=0.3.3,<0.4
 newspaper3k>=0.2.8,<0.3
 numexpr>=2.8.6,<3
 nvidia-riva-client>=2.14.0,<3
-oci>=2.128.0,<3
+oci>=2.119.1,<3
 openai<2
 openapi-pydantic>=0.3.2,<0.4
 oracle-ads>=2.9.1,<3

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

@@ -25,12 +25,6 @@ class AINetworkToolkit(BaseToolkit):
         data associated with this service.
 
         See https://python.langchain.com/docs/security for more information.
-
-    Parameters:
-        network: Optional. The network to connect to. Default is "testnet".
-            Options are "mainnet" or "testnet".
-        interface: Optional. The interface to use. If not provided, will
-            attempt to authenticate with the network. Default is None.
     """
 
     network: Optional[Literal["mainnet", "testnet"]] = "testnet"
@@ -38,17 +32,6 @@ class AINetworkToolkit(BaseToolkit):
 
     @root_validator(pre=True)
     def set_interface(cls, values: dict) -> dict:
-        """Set the interface if not provided.
-
-        If the interface is not provided, attempt to authenticate with the
-        network using the network value provided.
-
-        Args:
-            values: The values to validate.
-
-        Returns:
-            The validated values.
-        """
         if not values.get("interface"):
             values["interface"] = authenticate(network=values.get("network", "testnet"))
         return values
@@ -56,9 +39,7 @@ class AINetworkToolkit(BaseToolkit):
     class Config:
         """Pydantic config."""
 
-        # Allow extra fields. This is needed for the `interface` field.
         validate_all = True
-        # Allow arbitrary types. This is needed for the `interface` field.
         arbitrary_types_allowed = True
 
     def get_tools(self) -> List[BaseTool]:

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

@@ -16,12 +16,7 @@ if TYPE_CHECKING:
 
 
 class AmadeusToolkit(BaseToolkit):
-    """Toolkit for interacting with Amadeus which offers APIs for travel.
-
-    Parameters:
-        client: Optional. The Amadeus client. Default is None.
-        llm: Optional. The language model to use. Default is None.
-    """
+    """Toolkit for interacting with Amadeus which offers APIs for travel."""
 
     client: Client = Field(default_factory=authenticate)
     llm: Optional[BaseLanguageModel] = Field(default=None)
@@ -29,7 +24,6 @@ class AmadeusToolkit(BaseToolkit):
     class Config:
         """Pydantic config."""
 
-        # Allow extra fields. This is needed for the `client` field.
         arbitrary_types_allowed = True
 
     def get_tools(self) -> List[BaseTool]:

libs/community/langchain_community/agent_toolkits/base.py

@@ -1,5 +1,4 @@
 """Toolkits for agents."""
-
 from langchain_core.tools import BaseToolkit
 
 __all__ = ["BaseToolkit"]