Merge branch 'cc/anthropic_313' into cc/tool_attr

community
openai
2026-02-10 19:20:24 +00:00 · 2024-11-12 11:04:42 -05:00 · 2024-11-12 10:29:56 -05:00 · 2024-11-12 10:29:51 -05:00 · 2024-11-12 10:29:43 -05:00 · 2024-11-12 10:29:34 -05:00
103 changed files with 1384 additions and 5110 deletions
--- a/.github/workflows/_integration_test.yml
+++ b/.github/workflows/_integration_test.yml
@@ -79,7 +79,6 @@ jobs:
          VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
          UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
-          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
        run: |
          make integration_tests

--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -308,7 +308,6 @@ jobs:
          VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
          UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
-          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
        run: make integration_tests
        working-directory: ${{ inputs.working-directory }}

--- a/MIGRATE.md
+++ b/MIGRATE.md
@@ -1,11 +1,11 @@
 # Migrating

-Please see the following guides for migrating LangChain code:
+Please see the following guides for migratin LangChain code:

 * Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
 * Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
 * Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
-* Upgrade to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)
+* Upgrate to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)

-The [LangChain CLI](https://python.langchain.com/docs/versions/v0_3/#migrate-using-langchain-cli) can help you automatically upgrade your code to use non-deprecated imports. 
+The [LangChain CLI](https://python.langchain.com/docs/versions/v0_3/#migrate-using-langchain-cli) can help automatically upgrade your code to use non deprecated imports. 
 This will be especially helpful if you're still on either version 0.0.x or 0.1.x of LangChain.
--- a/docs/cassettes/output_parser_string_28eeace3-3896-497f-93ad-544cbfb7f15c.msgpack.zlib
+++ b/docs/cassettes/output_parser_string_28eeace3-3896-497f-93ad-544cbfb7f15c.msgpack.zlib
@@ -1 +0,0 @@
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--- a/docs/cassettes/output_parser_string_8ac74999-0740-4178-8efd-32a855592f71.msgpack.zlib
+++ b/docs/cassettes/output_parser_string_8ac74999-0740-4178-8efd-32a855592f71.msgpack.zlib
@@ -1 +0,0 @@
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--- a/docs/cassettes/output_parser_string_8c87553e-4f85-46c4-8f1e-666f6a261a50.msgpack.zlib
+++ b/docs/cassettes/output_parser_string_8c87553e-4f85-46c4-8f1e-666f6a261a50.msgpack.zlib
@@ -1 +0,0 @@
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--- a/docs/cassettes/output_parser_string_9cbb8848-9101-465e-b230-0f7af6fb4105.msgpack.zlib
+++ b/docs/cassettes/output_parser_string_9cbb8848-9101-465e-b230-0f7af6fb4105.msgpack.zlib
@@ -1 +0,0 @@
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--- a/docs/cassettes/summarize_map_reduce_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
+++ b/docs/cassettes/summarize_map_reduce_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
--- a/docs/cassettes/summarize_map_reduce_27c8fed0-b2d7-4549-a086-f5ee657efc41.msgpack.zlib
+++ b/docs/cassettes/summarize_map_reduce_27c8fed0-b2d7-4549-a086-f5ee657efc41.msgpack.zlib
--- a/docs/cassettes/summarize_map_reduce_4f26c1e3-3d3c-44f7-bb5f-46db9dc40f4b.msgpack.zlib
+++ b/docs/cassettes/summarize_map_reduce_4f26c1e3-3d3c-44f7-bb5f-46db9dc40f4b.msgpack.zlib
--- a/docs/cassettes/summarize_refine_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
+++ b/docs/cassettes/summarize_refine_0701bb7d-fbc6-497e-a577-25d56e6e43c6.msgpack.zlib
@@ -1 +1 @@
-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
--- a/docs/cassettes/summarize_refine_21711ff5-4e06-4843-9109-e7d89e679449.msgpack.zlib
+++ b/docs/cassettes/summarize_refine_21711ff5-4e06-4843-9109-e7d89e679449.msgpack.zlib
--- a/docs/docs/concepts/chat_history.mdx
+++ b/docs/docs/concepts/chat_history.mdx
@@ -17,7 +17,7 @@ Most conversations start with a **system message** that sets the context for the

 The **assistant** may respond directly to the user or if configured with tools request that a [tool](/docs/concepts/tool_calling) be invoked to perform a specific task.

-A full conversation often involves a combination of two patterns of alternating messages:
+So a full conversation often involves a combination of two patterns of alternating messages:

 1. The **user** and the **assistant** representing a back-and-forth conversation.
 2. The **assistant** and **tool messages** representing an ["agentic" workflow](/docs/concepts/agents) where the assistant is invoking tools to perform specific tasks.
--- a/docs/docs/concepts/chat_models.mdx
+++ b/docs/docs/concepts/chat_models.mdx
@@ -2,13 +2,13 @@

 ## Overview

-Large Language Models (LLMs) are advanced machine learning models that excel in a wide range of language-related tasks such as text generation, translation, summarization, question answering, and more, without needing task-specific fine tuning for every scenario.
+Large Language Models (LLMs) are advanced machine learning models that excel in a wide range of language-related tasks such as text generation, translation, summarization, question answering, and more, without needing task-specific tuning for every scenario.

 Modern LLMs are typically accessed through a chat model interface that takes a list of [messages](/docs/concepts/messages) as input and returns a [message](/docs/concepts/messages) as output.

 The newest generation of chat models offer additional capabilities:

-* [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable LLMs to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
+* [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable AI to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
 * [Structured output](/docs/concepts/structured_outputs): A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
 * [Multimodality](/docs/concepts/multimodality): The ability to work with data other than text; for example, images, audio, and video.

@@ -19,7 +19,7 @@ LangChain provides a consistent interface for working with chat models from diff
 * Integrations with many chat model providers (e.g., Anthropic, OpenAI, Ollama, Microsoft Azure, Google Vertex, Amazon Bedrock, Hugging Face, Cohere, Groq). Please see [chat model integrations](/docs/integrations/chat/) for an up-to-date list of supported models.
 * Use either LangChain's [messages](/docs/concepts/messages) format or OpenAI format.
 * Standard [tool calling API](/docs/concepts/tool_calling): standard interface for binding tools to models, accessing tool call requests made by models, and sending tool results back to the model.
-* Standard API for [structuring outputs](/docs/concepts/structured_outputs/#structured-output-method) via the `with_structured_output` method.
+* Standard API for structuring outputs (/docs/concepts/structured_outputs) via the `with_structured_output` method.
 * Provides support for [async programming](/docs/concepts/async), [efficient batching](/docs/concepts/runnables/#optimized-parallel-execution-batch), [a rich streaming API](/docs/concepts/streaming).
 * Integration with [LangSmith](https://docs.smith.langchain.com) for monitoring and debugging production-grade applications based on LLMs.
 * Additional features like standardized [token usage](/docs/concepts/messages/#aimessage), [rate limiting](#rate-limiting), [caching](#caching) and more.
@@ -85,7 +85,7 @@ Many chat models have standardized parameters that can be used to configure the
 | Parameter      | Description                                                                                                                                                                                                                                                                                                    |
 |----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | `model`        | The name or identifier of the specific AI model you want to use (e.g., `"gpt-3.5-turbo"` or `"gpt-4"`).                                                                                                                                                                                                        |
-| `temperature`  | Controls the randomness of the model's output. A higher value (e.g., 1.0) makes responses more creative, while a lower value (e.g., 0.0) makes them more deterministic and focused.                                                                                                                            |
+| `temperature`  | Controls the randomness of the model's output. A higher value (e.g., 1.0) makes responses more creative, while a lower value (e.g., 0.1) makes them more deterministic and focused.                                                                                                                            |
 | `timeout`      | The maximum time (in seconds) to wait for a response from the model before canceling the request. Ensures the request doesn’t hang indefinitely.                                                                                                                                                               |
 | `max_tokens`   | Limits the total number of tokens (words and punctuation) in the response. This controls how long the output can be.                                                                                                                                                                                           |
 | `stop`         | Specifies stop sequences that indicate when the model should stop generating tokens. For example, you might use specific strings to signal the end of a response.                                                                                                                                              |
@@ -97,9 +97,9 @@ Many chat models have standardized parameters that can be used to configure the
 Some important things to note:

 - Standard parameters only apply to model providers that expose parameters with the intended functionality. For example, some providers do not expose a configuration for maximum output tokens, so max_tokens can't be supported on these.
- Standard parameters are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in `langchain-community`.
+- Standard params are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in ``langchain-community``.

-Chat models also accept other parameters that are specific to that integration. To find all the parameters supported by a Chat model head to the their respective [API reference](https://python.langchain.com/api_reference/) for that model.
+ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the [API reference](https://python.langchain.com/api_reference/) for that model.

 ## Tool calling

@@ -150,7 +150,7 @@ An alternative approach is to use semantic caching, where you cache responses ba

 A semantic cache introduces a dependency on another model on the critical path of your application (e.g., the semantic cache may rely on an [embedding model](/docs/concepts/embedding_models) to convert text to a vector representation), and it's not guaranteed to capture the meaning of the input accurately.

-However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider, costs, and improve response times.
+However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider and improve response times.

 Please see the [how to cache chat model responses](/docs/how_to/chat_model_caching/) guide for more details.

--- a/docs/docs/concepts/document_loaders.mdx
+++ b/docs/docs/concepts/document_loaders.mdx
@@ -29,7 +29,7 @@ loader = CSVLoader(
 data = loader.load()
 ```

-When working with large datasets, you can use the `.lazy_load` method:
+or if working with large datasets, you can use the `.lazy_load` method:

 ```python
 for document in loader.lazy_load():
--- a/docs/docs/concepts/index.mdx
+++ b/docs/docs/concepts/index.mdx
@@ -8,155 +8,82 @@ The conceptual guide does not cover step-by-step instructions or specific implem

 ## High level

-### [Why LangChain?](/docs/concepts/why_langchain)
- Overview of the value that LangChain provides.
-### [Architecture](/docs/concepts/architecture)
- How packages are organized in the LangChain ecosystem.
+- **[Why LangChain?](/docs/concepts/why_langchain)**: Overview of the value that LangChain provides.
+- **[Architecture](/docs/concepts/architecture)**: How packages are organized in the LangChain ecosystem.

 ## Concepts

-### [Chat models](/docs/concepts/chat_models)
- LLMs exposed via a chat API that process sequences of messages as input and output a message.
-### [Messages](/docs/concepts/messages)
- The unit of communication in chat models, used to represent model input and output.
-### [Chat history](/docs/concepts/chat_history)
- A conversation represented as a sequence of messages, alternating between user messages and model responses.
-### [Tools](/docs/concepts/tools)
- A function with an associated schema defining the function's name, description, and the arguments it accepts.
-### [Tool calling](/docs/concepts/tool_calling)
- A type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
-### [Structured output](/docs/concepts/structured_outputs)
- A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
-### [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/)
- Information about a conversation that is persisted so that it can be used in future conversations.
-### [Multimodality](/docs/concepts/multimodality)
- The ability to work with data that comes in different forms, such as text, audio, images, and video.
-### [Runnable interface](/docs/concepts/runnables)
- The base abstraction that many LangChain components and the LangChain Expression Language are built on.
-### [LangChain Expression Language (LCEL)](/docs/concepts/lcel)
- A syntax for orchestrating LangChain components. Most useful for simpler applications.
-### [Document loaders](/docs/concepts/document_loaders)
- Load a source as a list of documents.
-### [Retrieval](/docs/concepts/retrieval)
- Information retrieval systems can retrieve structured or unstructured data from a datasource in response to a query.
-### [Text splitters](/docs/concepts/text_splitters)
- Split long text into smaller chunks that can be individually indexed to enable granular retrieval.
-### [Embedding models](/docs/concepts/embedding_models)
- Models that represent data such as text or images in a vector space.
-### [Vector stores](/docs/concepts/vectorstores)
- Storage of and efficient search over vectors and associated metadata.
-### [Retriever](/docs/concepts/retrievers)
- A component that returns relevant documents from a knowledge base in response to a query.
-### [Retrieval Augmented Generation (RAG)](/docs/concepts/rag)
- A technique that enhances language models by combining them with external knowledge bases.
-### [Agents](/docs/concepts/agents)
- Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tool](/docs/concepts/tools).
-### [Prompt templates](/docs/concepts/prompt_templates)
- Component for factoring out the static parts of a model "prompt" (usually a sequence of messages). Useful for serializing, versioning, and reusing these static parts.
-### [Output parsers](/docs/concepts/output_parsers)
- Responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
-### [Few-shot prompting](/docs/concepts/few_shot_prompting)
- A technique for improving model performance by providing a few examples of the task to perform in the prompt.
-### [Example selectors](/docs/concepts/example_selectors)
- Used to select the most relevant examples from a dataset based on a given input. Example selectors are used in few-shot prompting to select examples for a prompt.
-### [Async programming](/docs/concepts/async)
- The basics that one should know to use LangChain in an asynchronous context.
-### [Callbacks](/docs/concepts/callbacks)
- Callbacks enable the execution of custom auxiliary code in built-in components. Callbacks are used to stream outputs from LLMs in LangChain, trace the intermediate steps of an application, and more.
-### [Tracing](/docs/concepts/tracing)
- The process of recording the steps that an application takes to go from input to output. Tracing is essential for debugging and diagnosing issues in complex applications.
-### [Evaluation](/docs/concepts/evaluation)
- The process of assessing the performance and effectiveness of AI applications. This involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose. This process is vital for building reliable applications.
+- **[Chat models](/docs/concepts/chat_models)**: LLMs exposed via a chat API that process sequences of messages as input and output a message.
+- **[Messages](/docs/concepts/messages)**: The unit of communication in chat models, used to represent model input and output.
+- **[Chat history](/docs/concepts/chat_history)**: A conversation represented as a sequence of messages, alternating between user messages and model responses.
+- **[Tools](/docs/concepts/tools)**: A function with an associated schema defining the function's name, description, and the arguments it accepts.
+- **[Tool calling](/docs/concepts/tool_calling)**: A type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
+- **[Structured output](/docs/concepts/structured_outputs)**: A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
+- **[Memory](https://langchain-ai.github.io/langgraph/concepts/memory/)**: Information about a conversation that is persisted so that it can be used in future conversations.
+- **[Multimodality](/docs/concepts/multimodality)**: The ability to work with data that comes in different forms, such as text, audio, images, and video.
+- **[Runnable interface](/docs/concepts/runnables)**: The base abstraction that many LangChain components and the LangChain Expression Language are built on.
+- **[LangChain Expression Language (LCEL)](/docs/concepts/lcel)**: A syntax for orchestrating LangChain components. Most useful for simpler applications.
+- **[Document loaders](/docs/concepts/document_loaders)**: Load a source as a list of documents.
+- **[Retrieval](/docs/concepts/retrieval)**: Information retrieval systems can retrieve structured or unstructured data from a datasource in response to a query.
+- **[Text splitters](/docs/concepts/text_splitters)**: Split long text into smaller chunks that can be individually indexed to enable granular retrieval.
+- **[Embedding models](/docs/concepts/embedding_models)**: Models that represent data such as text or images in a vector space.
+- **[Vector stores](/docs/concepts/vectorstores)**: Storage of and efficient search over vectors and associated metadata.
+- **[Retriever](/docs/concepts/retrievers)**: A component that returns relevant documents from a knowledge base in response to a query.
+- **[Retrieval Augmented Generation (RAG)](/docs/concepts/rag)**: A technique that enhances language models by combining them with external knowledge bases.
+- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tool](/docs/concepts/tools).
+- **[Prompt templates](/docs/concepts/prompt_templates)**: Component for factoring out the static parts of a model "prompt" (usually a sequence of messages). Useful for serializing, versioning, and reusing these static parts.
+- **[Output parsers](/docs/concepts/output_parsers)**: Responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
+- **[Few-shot prompting](/docs/concepts/few_shot_prompting)**: A technique for improving model performance by providing a few examples of the task to perform in the prompt.
+- **[Example selectors](/docs/concepts/example_selectors)**: Used to select the most relevant examples from a dataset based on a given input. Example selectors are used in few-shot prompting to select examples for a prompt.
+- **[Async programming](/docs/concepts/async)**: The basics that one should know to use LangChain in an asynchronous context.
+- **[Callbacks](/docs/concepts/callbacks)**: Callbacks enable the execution of custom auxiliary code in built-in components. Callbacks are used to stream outputs from LLMs in LangChain, trace the intermediate steps of an application, and more.
+- **[Tracing](/docs/concepts/tracing)**: The process of recording the steps that an application takes to go from input to output. Tracing is essential for debugging and diagnosing issues in complex applications.
+- **[Evaluation](/docs/concepts/evaluation)**: The process of assessing the performance and effectiveness of AI applications. This involves testing the model's responses against a set of predefined criteria or benchmarks to ensure it meets the desired quality standards and fulfills the intended purpose. This process is vital for building reliable applications.

 ## Glossary

-### [AIMessageChunk](/docs/concepts/messages#aimessagechunk)
- A partial response from an AI message. Used when streaming responses from a chat model.
-### [AIMessage](/docs/concepts/messages#aimessage)
- Represents a complete response from an AI model.
-### [astream_events](/docs/concepts/chat_models#key-methods)
- Stream granular information from [LCEL](/docs/concepts/lcel) chains.
-### [BaseTool](/docs/concepts/tools/#tool-interface)
- The base class for all tools in LangChain.
-### [batch](/docs/concepts/runnables)
- Use to execute a runnable with batch inputs a Runnable.
-### [bind_tools](/docs/concepts/tool_calling/#tool-binding)
- Allows models to interact with tools.
-### [Caching](/docs/concepts/chat_models#caching)
- Storing results to avoid redundant calls to a chat model.
-### [Chat models](/docs/concepts/multimodality/#multimodality-in-chat-models)
- Chat models that handle multiple data modalities.
-### [Configurable runnables](/docs/concepts/runnables/#configurable-runnables)
- Creating configurable Runnables.
-### [Context window](/docs/concepts/chat_models#context-window)
- The maximum size of input a chat model can process.
-### [Conversation patterns](/docs/concepts/chat_history#conversation-patterns)
- Common patterns in chat interactions.
-### [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)
- LangChain's representation of a document.
-### [Embedding models](/docs/concepts/multimodality/#multimodality-in-embedding-models)
- Models that generate vector embeddings for various data types.
-### [HumanMessage](/docs/concepts/messages#humanmessage)
- Represents a message from a human user.
-### [InjectedState](/docs/concepts/tools#injectedstate)
- A state injected into a tool function.
-### [InjectedStore](/docs/concepts/tools#injectedstore)
- A store that can be injected into a tool for data persistence.
-### [InjectedToolArg](/docs/concepts/tools#injectedtoolarg)
- Mechanism to inject arguments into tool functions.
-### [input and output types](/docs/concepts/runnables#input-and-output-types)
- Types used for input and output in Runnables.
-### [Integration packages](/docs/concepts/architecture/#integration-packages)
- Third-party packages that integrate with LangChain.
-### [invoke](/docs/concepts/runnables)
- A standard method to invoke a Runnable.
-### [JSON mode](/docs/concepts/structured_outputs#json-mode)
- Returning responses in JSON format.
-### [langchain-community](/docs/concepts/architecture#langchain-community)
- Community-driven components for LangChain.
-### [langchain-core](/docs/concepts/architecture#langchain-core)
- Core langchain package. Includes base interfaces and in-memory implementations.
-### [langchain](/docs/concepts/architecture#langchain)
- A package for higher level components (e.g., some pre-built chains).
-### [langgraph](/docs/concepts/architecture#langgraph)
- Powerful orchestration layer for LangChain. Use to build complex pipelines and workflows.
-### [langserve](/docs/concepts/architecture#langserve)
- Use to deploy LangChain Runnables as REST endpoints. Uses FastAPI. Works primarily for LangChain Runnables, does not currently integrate with LangGraph.
-### [Managing chat history](/docs/concepts/chat_history#managing-chat-history)
- Techniques to maintain and manage the chat history.
-### [OpenAI format](/docs/concepts/messages#openai-format)
- OpenAI's message format for chat models.
-### [Propagation of RunnableConfig](/docs/concepts/runnables/#propagation-of-runnableconfig)
- Propagating configuration through Runnables. Read if working with python 3.9, 3.10 and async.
-### [rate-limiting](/docs/concepts/chat_models#rate-limiting)
- Client side rate limiting for chat models.
-### [RemoveMessage](/docs/concepts/messages/#removemessage)
- An abstraction used to remove a message from chat history, used primarily in LangGraph.
-### [role](/docs/concepts/messages#role)
- Represents the role (e.g., user, assistant) of a chat message.
-### [RunnableConfig](/docs/concepts/runnables/#runnableconfig)
- Use to pass run time information to Runnables (e.g., `run_name`, `run_id`, `tags`, `metadata`, `max_concurrency`, `recursion_limit`, `configurable`).
-### [Standard parameters for chat models](/docs/concepts/chat_models#standard-parameters)
- Parameters such as API key, `temperature`, and `max_tokens`,
-### [stream](/docs/concepts/streaming)
- Use to stream output from a Runnable or a graph.
-### [Tokenization](/docs/concepts/tokens)
- The process of converting data into tokens and vice versa.
-### [Tokens](/docs/concepts/tokens)
- The basic unit that a language model reads, processes, and generates under the hood.
-### [Tool artifacts](/docs/concepts/tools#tool-artifacts)
- Add artifacts to the output of a tool that will not be sent to the model, but will be available for downstream processing.
-### [Tool binding](/docs/concepts/tool_calling#tool-binding)
- Binding tools to models.
-### [@tool](/docs/concepts/tools/#create-tools-using-the-tool-decorator)
- Decorator for creating tools in LangChain.
-### [Toolkits](/docs/concepts/tools#toolkits)
- A collection of tools that can be used together.
-### [ToolMessage](/docs/concepts/messages#toolmessage)
- Represents a message that contains the results of a tool execution.
-### [Vector stores](/docs/concepts/vectorstores)
- Datastores specialized for storing and efficiently searching vector embeddings.
-### [with_structured_output](/docs/concepts/structured_outputs/#structured-output-method)
- A helper method for chat models that natively support [tool calling](/docs/concepts/tool_calling) to get structured output matching a given schema specified via Pydantic, JSON schema or a function.
-### [with_types](/docs/concepts/runnables#with_types)
- Method to overwrite the input and output types of a runnable. Useful when working with complex LCEL chains and deploying with LangServe.
+- **[AIMessageChunk](/docs/concepts/messages#aimessagechunk)**: A partial response from an AI message. Used when streaming responses from a chat model.
+- **[AIMessage](/docs/concepts/messages#aimessage)**: Represents a complete response from an AI model.
+- **[astream_events](/docs/concepts/chat_models#key-methods)**: Stream granular information from [LCEL](/docs/concepts/lcel) chains.
+- **[BaseTool](/docs/concepts/tools/#tool-interface)**: The base class for all tools in LangChain.
+- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs a Runnable.
+- **[bind_tools](/docs/concepts/tool_calling/#tool-binding)**: Allows models to interact with tools.
+- **[Caching](/docs/concepts/chat_models#caching)**: Storing results to avoid redundant calls to a chat model.
+- **[Chat models](/docs/concepts/multimodality/#multimodality-in-chat-models)**: Chat models that handle multiple data modalities.
+- **[Configurable runnables](/docs/concepts/runnables/#configurable-runnables)**: Creating configurable Runnables.
+- **[Context window](/docs/concepts/chat_models#context-window)**: The maximum size of input a chat model can process.
+- **[Conversation patterns](/docs/concepts/chat_history#conversation-patterns)**: Common patterns in chat interactions.
+- **[Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)**: LangChain's representation of a document.
+- **[Embedding models](/docs/concepts/multimodality/#multimodality-in-embedding-models)**: Models that generate vector embeddings for various data types.
+- **[HumanMessage](/docs/concepts/messages#humanmessage)**: Represents a message from a human user.
+- **[InjectedState](/docs/concepts/tools#injectedstate)**: A state injected into a tool function.
+- **[InjectedStore](/docs/concepts/tools#injectedstore)**: A store that can be injected into a tool for data persistence.
+- **[InjectedToolArg](/docs/concepts/tools#injectedtoolarg)**: Mechanism to inject arguments into tool functions.
+- **[input and output types](/docs/concepts/runnables#input-and-output-types)**: Types used for input and output in Runnables.
+- **[Integration packages](/docs/concepts/architecture/#integration-packages)**: Third-party packages that integrate with LangChain.
+- **[invoke](/docs/concepts/runnables)**: A standard method to invoke a Runnable.
+- **[JSON mode](/docs/concepts/structured_outputs#json-mode)**: Returning responses in JSON format.
+- **[langchain-community](/docs/concepts/architecture#langchain-community)**: Community-driven components for LangChain.
+- **[langchain-core](/docs/concepts/architecture#langchain-core)**: Core langchain package. Includes base interfaces and in-memory implementations.
+- **[langchain](/docs/concepts/architecture#langchain)**: A package for higher level components (e.g., some pre-built chains).
+- **[langgraph](/docs/concepts/architecture#langgraph)**: Powerful orchestration layer for LangChain. Use to build complex pipelines and workflows.
+- **[langserve](/docs/concepts/architecture#langserve)**: Use to deploy LangChain Runnables as REST endpoints. Uses FastAPI. Works primarily for LangChain Runnables, does not currently integrate with LangGraph.
+- **[Managing chat history](/docs/concepts/chat_history#managing-chat-history)**: Techniques to maintain and manage the chat history.
+- **[OpenAI format](/docs/concepts/messages#openai-format)**: OpenAI's message format for chat models.
+- **[Propagation of RunnableConfig](/docs/concepts/runnables/#propagation-of-runnableconfig)**: Propagating configuration through Runnables. Read if working with python 3.9, 3.10 and async.
+- **[rate-limiting](/docs/concepts/chat_models#rate-limiting)**: Client side rate limiting for chat models.
+- **[RemoveMessage](/docs/concepts/messages/#removemessage)**: An abstraction used to remove a message from chat history, used primarily in LangGraph.
+- **[role](/docs/concepts/messages#role)**: Represents the role (e.g., user, assistant) of a chat message.
+- **[RunnableConfig](/docs/concepts/runnables/#runnableconfig)**: Use to pass run time information to Runnables (e.g., `run_name`, `run_id`, `tags`, `metadata`, `max_concurrency`, `recursion_limit`, `configurable`).
+- **[Standard parameters for chat models](/docs/concepts/chat_models#standard-parameters)**: Parameters such as API key, `temperature`, and `max_tokens`,
+- **[stream](/docs/concepts/streaming)**: Use to stream output from a Runnable or a graph.
+- **[Tokenization](/docs/concepts/tokens)**: The process of converting data into tokens and vice versa.
+- **[Tokens](/docs/concepts/tokens)**: The basic unit that a language model reads, processes, and generates under the hood.
+- **[Tool artifacts](/docs/concepts/tools#tool-artifacts)**: Add artifacts to the output of a tool that will not be sent to the model, but will be available for downstream processing.
+- **[Tool binding](/docs/concepts/tool_calling#tool-binding)**: Binding tools to models.
+- **[@tool](/docs/concepts/tools/#create-tools-using-the-tool-decorator)**: Decorator for creating tools in LangChain.
+- **[Toolkits](/docs/concepts/tools#toolkits)**: A collection of tools that can be used together.
+- **[ToolMessage](/docs/concepts/messages#toolmessage)**: Represents a message that contains the results of a tool execution.
+- **[Vector stores](/docs/concepts/vectorstores)**: Datastores specialized for storing and efficiently searching vector embeddings.
+- **[with_structured_output](/docs/concepts/structured_outputs/#structured-output-method)**: A helper method for chat models that natively support [tool calling](/docs/concepts/tool_calling) to get structured output matching a given schema specified via Pydantic, JSON schema or a function.
+- **[with_types](/docs/concepts/runnables#with_types)**: Method to overwrite the input and output types of a runnable. Useful when working with complex LCEL chains and deploying with LangServe.
--- a/docs/docs/concepts/lcel.mdx
+++ b/docs/docs/concepts/lcel.mdx
@@ -6,7 +6,7 @@

 The **L**ang**C**hain **E**xpression **L**anguage (LCEL) takes a [declarative](https://en.wikipedia.org/wiki/Declarative_programming) approach to building new [Runnables](/docs/concepts/runnables) from existing Runnables.

-This means that you describe what *should* happen, rather than *how* it should happen, allowing LangChain to optimize the run-time execution of the chains.
+This means that you describe what you want to happen, rather than how you want it to happen, allowing LangChain to optimize the run-time execution of the chains.

 We often refer to a `Runnable` created using LCEL as a "chain". It's important to remember that a "chain" is `Runnable` and it implements the full [Runnable Interface](/docs/concepts/runnables).

@@ -20,8 +20,8 @@ We often refer to a `Runnable` created using LCEL as a "chain". It's important t

 LangChain optimizes the run-time execution of chains built with LCEL in a number of ways:

- **Optimized parallel execution**: Run Runnables in parallel using [RunnableParallel](#runnableparallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables/#optimized-parallel-execution-batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
- **Guaranteed Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables/#asynchronous-support). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
+- **Optimize parallel execution**: Run Runnables in parallel using [RunnableParallel](#runnableparallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables/#optimized-parallel-execution-batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
+- **Guarantee Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables/#asynchronous-support). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
 - **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/text_llms) comes out).

 Other benefits include:
@@ -38,7 +38,7 @@ LCEL is an [orchestration solution](https://en.wikipedia.org/wiki/Orchestration_

 While we have seen users run chains with hundreds of steps in production, we generally recommend using LCEL for simpler orchestration tasks. When the application requires complex state management, branching, cycles or multiple agents, we recommend that users take advantage of [LangGraph](/docs/concepts/architecture#langgraph).

-In LangGraph, users define graphs that specify the application's flow. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.
+In LangGraph, users define graphs that specify the flow of the application. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.

 Here are some guidelines:

--- a/docs/docs/concepts/messages.mdx
+++ b/docs/docs/concepts/messages.mdx
@@ -8,7 +8,7 @@

 Messages are the unit of communication in [chat models](/docs/concepts/chat_models). They are used to represent the input and output of a chat model, as well as any additional context or metadata that may be associated with a conversation.

-Each message has a **role** (e.g., "user", "assistant") and **content** (e.g., text, multimodal data) with additional metadata that varies depending on the chat model provider.
+Each message has a **role** (e.g., "user", "assistant"), **content** (e.g., text, multimodal data), and additional metadata that can vary depending on the chat model provider.

 LangChain provides a unified message format that can be used across chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider.

@@ -39,7 +39,6 @@ The content of a message text or a list of dictionaries representing [multimodal
 Currently, most chat models support text as the primary content type, with some models also supporting multimodal data. However, support for multimodal data is still limited across most chat model providers.

 For more information see:
-* [SystemMessage](#systemmessage) -- for content which should be passed to direct the conversation
 * [HumanMessage](#humanmessage) -- for content in the input from the user.
 * [AIMessage](#aimessage) -- for content in the response from the model.
 * [Multimodality](/docs/concepts/multimodality) -- for more information on multimodal content.
--- a/docs/docs/concepts/output_parsers.mdx
+++ b/docs/docs/concepts/output_parsers.mdx
@@ -26,7 +26,6 @@ LangChain has lots of different types of output parsers. This is a list of outpu

 | Name                                                                                                                                                                                                                                    | Supports Streaming | Has Format Instructions | Calls LLM | Input Type         | Output Type          | Description                                                                                                                                                                                                                                              |
 |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------|-------------------------|-----------|--------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Str](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html)                                                                                                         | ✅                  |                         |           | `str` \| `Message` | String                | Parses texts from message objects. Useful for handling variable formats of message content (e.g., extracting text from content blocks).                                                                                                                |
 | [JSON](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JSONOutputParser.html#langchain_core.output_parsers.json.JSONOutputParser)                                                     | ✅                  | ✅                       |           | `str` \| `Message` | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
 | [XML](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)                                                          | ✅                  | ✅                       |           | `str` \| `Message` | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
 | [CSV](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)                          | ✅                  | ✅                       |           | `str` \| `Message` | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
--- a/docs/docs/concepts/retrieval.mdx
+++ b/docs/docs/concepts/retrieval.mdx
@@ -27,7 +27,7 @@ These systems accommodate various data formats:
 - Unstructured text (e.g., documents) is often stored in vector stores or lexical search indexes.
 - Structured data is typically housed in relational or graph databases with defined schemas.

-Despite the growing diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces. 
+Despite this diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces. 
 Models play a crucial role in this process by translating natural language queries into formats compatible with the underlying search index or database. 
 This translation enables more intuitive and flexible interactions with complex data structures.

@@ -41,7 +41,7 @@ This translation enables more intuitive and flexible interactions with complex d

 ## Query analysis 

-While users typically prefer to interact with retrieval systems using natural language, these systems may require specific query syntax or benefit from certain keywords. 
+While users typically prefer to interact with retrieval systems using natural language, retrieval systems can specific query syntax or benefit from particular keywords. 
 Query analysis serves as a bridge between raw user input and optimized search queries. Some common applications of query analysis include:

 1. **Query Re-writing**: Queries can be re-written or expanded to improve semantic or lexical searches.
--- a/docs/docs/concepts/runnables.mdx
+++ b/docs/docs/concepts/runnables.mdx
@@ -1,6 +1,6 @@
 # Runnable interface

-The Runnable interface is the foundation for working with LangChain components, and it's implemented across many of them, such as [language models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [retrievers](/docs/concepts/retrievers), [compiled LangGraph graphs](
+The Runnable interface is foundational for working with LangChain components, and it's implemented across many of them, such as [language models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [retrievers](/docs/concepts/retrievers), [compiled LangGraph graphs](
 https://langchain-ai.github.io/langgraph/concepts/low_level/#compiling-your-graph) and more.

 This guide covers the main concepts and methods of the Runnable interface, which allows developers to interact with various LangChain components in a consistent and predictable manner.
@@ -42,7 +42,7 @@ Some Runnables may provide their own implementations of `batch` and `batch_as_co
 rely on a `batch` API provided by a model provider).

 :::note
-The async versions of `abatch` and `abatch_as_completed` relies on asyncio's [gather](https://docs.python.org/3/library/asyncio-task.html#asyncio.gather) and [as_completed](https://docs.python.org/3/library/asyncio-task.html#asyncio.as_completed) functions to run the `ainvoke` method in parallel.
+The async versions of `abatch` and `abatch_as_completed` these rely on asyncio's [gather](https://docs.python.org/3/library/asyncio-task.html#asyncio.gather) and [as_completed](https://docs.python.org/3/library/asyncio-task.html#asyncio.as_completed) functions to run the `ainvoke` method in parallel.
 :::

 :::tip
@@ -58,7 +58,7 @@ Runnables expose an asynchronous API, allowing them to be called using the `awai

 Please refer to the [Async Programming with LangChain](/docs/concepts/async) guide for more details.

-## Streaming APIs
+## Streaming apis
 <span data-heading-keywords="streaming-api"></span>

 Streaming is critical in making applications based on LLMs feel responsive to end-users.
@@ -101,7 +101,7 @@ This is an advanced feature that is unnecessary for most users. You should proba
 skip this section unless you have a specific need to inspect the schema of a Runnable.
 :::

-In more advanced use cases, you may want to programmatically **inspect** the Runnable and determine what input and output types the Runnable expects and produces.
+In some advanced uses, you may want to programmatically **inspect** the Runnable and determine what input and output types the Runnable expects and produces.

 The Runnable interface provides methods to get the [JSON Schema](https://json-schema.org/) of the input and output types of a Runnable, as well as [Pydantic schemas](https://docs.pydantic.dev/latest/) for the input and output types.

@@ -315,7 +315,7 @@ the `RunnableConfig` manually to sub-calls in some cases. Please see the
 [Propagating RunnableConfig](#propagation-of-runnableconfig) section for more information.
 :::

-## Creating a runnable from a function {#custom-runnables}
+## Creating a runnable from a function

 You may need to create a custom Runnable that runs arbitrary logic. This is especially
 useful if using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to compose
--- a/docs/docs/concepts/streaming.mdx
+++ b/docs/docs/concepts/streaming.mdx
@@ -77,13 +77,13 @@ When using `stream()` or `astream()` with chat models, the output is streamed as

 [LangGraph](/docs/concepts/architecture#langgraph) compiled graphs are [Runnables](/docs/concepts/runnables) and support the standard streaming APIs.

-When using the *stream* and *astream* methods with LangGraph, you can choose **one or more** [streaming mode](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamMode) which allow you to control the type of output that is streamed. The available streaming modes are:
+When using the *stream* and *astream* methods with LangGraph, you can **one or more** [streaming mode](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamMode) which allow you to control the type of output that is streamed. The available streaming modes are:

 - **"values"**: Emit all values of the [state](https://langchain-ai.github.io/langgraph/concepts/low_level/) for each step.
 - **"updates"**: Emit only the node name(s) and updates that were returned by the node(s) after each step.
 - **"debug"**: Emit debug events for each step.
 - **"messages"**: Emit LLM [messages](/docs/concepts/messages) [token-by-token](/docs/concepts/tokens).
- **"custom"**: Emit custom output written using [LangGraph's StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter).
+- **"custom"**: Emit custom output witten using [LangGraph's StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter).

 For more information, please see:
 * [LangGraph streaming conceptual guide](https://langchain-ai.github.io/langgraph/concepts/streaming/) for more information on how to stream when working with LangGraph.
--- a/docs/docs/concepts/structured_outputs.mdx
+++ b/docs/docs/concepts/structured_outputs.mdx
@@ -119,11 +119,11 @@ json_object = json.loads(ai_msg.content)

 There are a few challenges when producing structured output with the above methods: 

-(1) When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.  
+(1) If using tool calling, tool call arguments needs to be parsed from a dictionary back to the original schema.  

 (2) In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting. 

-(3) When JSON mode is used, the output needs to be parsed into a JSON object. 
+(3) If using JSON mode, the output needs to be parsed into a JSON object. 

 With these challenges in mind, LangChain provides a helper function (`with_structured_output()`) to streamline the process.

--- a/docs/docs/concepts/tool_calling.mdx
+++ b/docs/docs/concepts/tool_calling.mdx
@@ -128,7 +128,7 @@ For more details on usage, see our [how-to guides](/docs/how_to/#tools)!

 [Tools](/docs/concepts/tools/) implement the [Runnable](/docs/concepts/runnables/) interface, which means that they can be invoked (e.g., `tool.invoke(args)`) directly.

-[LangGraph](https://langchain-ai.github.io/langgraph/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode)) that will often invoke the tool in behalf of the user.
+[LangGraph](https://langchain-ai.github.io/langgraph/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)) that will often invoke the tool in behalf of the user.

 :::info[Further reading]

--- a/docs/docs/concepts/tools.mdx
+++ b/docs/docs/concepts/tools.mdx
@@ -6,7 +6,7 @@

 ## Overview

-The **tool** abstraction in LangChain associates a Python **function** with a **schema** that defines the function's **name**, **description** and **expected arguments**. 
+The **tool** abstraction in LangChain associates a python **function** with a **schema** that defines the function's **name**, **description** and **input**. 

 **Tools** can be passed to [chat models](/docs/concepts/chat_models) that support [tool calling](/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs.

@@ -14,7 +14,7 @@ The **tool** abstraction in LangChain associates a Python **function** with a **

 - Tools are a way to encapsulate a function and its schema in a way that can be passed to a chat model.
 - Create tools using the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator, which simplifies the process of tool creation, supporting the following:
-   - Automatically infer the tool's **name**, **description** and **expected arguments**, while also supporting customization.
+   - Automatically infer the tool's **name**, **description** and **inputs**, while also supporting customization.
   - Defining tools that return **artifacts** (e.g. images, dataframes, etc.)
   - Hiding input arguments from the schema (and hence from the model) using **injected tool arguments**.

--- a/docs/docs/concepts/why_langchain.mdx
+++ b/docs/docs/concepts/why_langchain.mdx
@@ -1,9 +1,9 @@
-# Why LangChain?
+# Why langchain?

-The goal of `langchain` the Python package and LangChain the company is to make it as easy as possible for developers to build applications that reason.
+The goal of `langchain` the Python package and LangChain the company is to make it as easy possible for developers to build applications that reason.
 While LangChain originally started as a single open source package, it has evolved into a company and a whole ecosystem.
 This page will talk about the LangChain ecosystem as a whole.
-Most of the components within the LangChain ecosystem can be used by themselves - so if you feel particularly drawn to certain components but not others, that is totally fine! Pick and choose whichever components you like best for your own use case!
+Most of the components within in the LangChain ecosystem can be used by themselves - so if you feel particularly drawn to certain components but not others, that is totally fine! Pick and choose whichever components you like best.

 ## Features

@@ -17,8 +17,8 @@ LangChain exposes a standard interface for key components, making it easy to swi
 [Orchestration](https://en.wikipedia.org/wiki/Orchestration_(computing)) is crucial for building such applications.

 3. **Observability and evaluation:** As applications become more complex, it becomes increasingly difficult to understand what is happening within them.
-Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice).
-For example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. 
+Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice):
+for example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost. 
 [Observability](https://en.wikipedia.org/wiki/Observability) and evaluations can help developers monitor their applications and rapidly answer these types of questions with confidence.


@@ -72,11 +72,11 @@ There are several common characteristics of LLM applications that this orchestra
 * **[Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/):** The application needs to maintain [short-term and / or long-term memory](https://langchain-ai.github.io/langgraph/concepts/memory/).
 * **[Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/):** The application needs human interaction, e.g., pausing, reviewing, editing, approving certain steps.

-The recommended way to orchestrate components for complex applications is [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/).
+The recommended way to do orchestration for these complex applications is [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/).
 LangGraph is a library that gives developers a high degree of control by expressing the flow of the application as a set of nodes and edges.
 LangGraph comes with built-in support for [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), [memory](https://langchain-ai.github.io/langgraph/concepts/memory/), and other features.
-It's particularly well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications. 
-Importantly, individual LangChain components can be used as LangGraph nodes, but you can also use LangGraph **without** using LangChain components.
+It's particularly  well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications. 
+Importantly, individual LangChain components can be used within LangGraph nodes, but you can also use LangGraph **without** using LangChain components.

 :::info[Further reading]

--- a/docs/docs/contributing/how_to/documentation/style_guide.mdx
+++ b/docs/docs/contributing/how_to/documentation/style_guide.mdx
@@ -4,8 +4,8 @@ sidebar_class_name: "hidden"

 # Documentation Style Guide

-As LangChain continues to grow, the amount of documentation required to cover the various concepts and integrations continues to grow too.
-This page provides guidelines for anyone writing documentation for LangChain and outlines some of our philosophies around
+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
 organization and structure.

 ## Philosophy
@@ -18,9 +18,9 @@ Under this framework, all documentation falls under one of four categories: [Tut
 ### Tutorials

 Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
-gain an understanding of concepts and how they interact by showing one way to achieve a specific goal in a hands-on manner. 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 accomplish 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 goal that is clearly stated in the tutorial's introduction. Information on how to address additional scenarios
+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:
@@ -53,8 +53,8 @@ Here are some high-level tips on writing a good tutorial:
 ### 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 focused on solving an immediate problem. However,
-it should still provide some background or list certain scenarios where the information may be relevant.
+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:
@@ -79,10 +79,10 @@ Here are some high-level tips on writing a good how-to guide:

 ### Conceptual guide

-LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. These guides should cover LangChain terms and concepts
-in a more abstract way than how-to guides or tutorials, targeting curious users interested in
-gaining a deeper understanding and insights of the framework. Try to avoid excessively large code examples as the primary goal is to
-provide perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
+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.

@@ -137,14 +137,14 @@ be only one (very rarely two), canonical pages for a given concept or feature. I

 ### Link to other sections

-Because sections of the docs do not exist in a vacuum, it is important to link to other sections frequently,
-to allow a developer to learn more about an unfamiliar topic within the flow of reading.
+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 and conceptual sections!
+This includes linking to the API references as well as conceptual sections!

 ### Be concise

-In general, take a less-is-more approach. If another section with a good explanation of a concept exists, you should link to it rather than
+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.

 Be concise, including in code samples.
--- a/docs/docs/contributing/tutorials/docs.mdx
+++ b/docs/docs/contributing/tutorials/docs.mdx
@@ -8,7 +8,7 @@ This tutorial will guide you through making a simple documentation edit, like co

 ---

-## Editing a Documentation Page on GitHub
+## Editing a Documentation Page on GitHub**

 Sometimes you want to make a small change, like fixing a typo, and the easiest way to do this is to use GitHub's editor directly.

--- a/docs/docs/how_to/chatbots_memory.ipynb
+++ b/docs/docs/how_to/chatbots_memory.ipynb
@@ -15,7 +15,7 @@
   "source": [
    "# How to add memory to chatbots\n",
    "\n",
-    "A key feature of chatbots is their ability to use the content of previous conversational turns as context. This state management can take several forms, including:\n",
+    "A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:\n",
    "\n",
    "- Simply stuffing previous messages into a chat model prompt.\n",
    "- The above, but trimming old messages to reduce the amount of distracting information the model has to deal with.\n",
@@ -185,7 +185,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    " We'll pass the latest input to the conversation here and let LangGraph keep track of the conversation history using the checkpointer:"
+    " We'll pass the latest input to the conversation here and let the LangGraph keep track of the conversation history using the checkpointer:"
   ]
  },
  {
--- a/docs/docs/how_to/custom_chat_model.ipynb
+++ b/docs/docs/how_to/custom_chat_model.ipynb
@@ -503,7 +503,7 @@
    "\n",
    "Documentation:\n",
    "\n",
-    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [API Reference](https://python.langchain.com/api_reference/langchain/index.html).\n",
+    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://python.langchain.com/api_reference/langchain/index.html).\n",
    "* The class doc-string for the model contains a link to the model API if the model is powered by a service.\n",
    "\n",
    "Tests:\n",
--- a/docs/docs/how_to/index.mdx
+++ b/docs/docs/how_to/index.mdx
@@ -115,7 +115,6 @@ What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of languag

 [Output Parsers](/docs/concepts/output_parsers) are responsible for taking the output of an LLM and parsing into more structured format.

- [How to: parse text from message objects](/docs/how_to/output_parser_string)
 - [How to: use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
 - [How to: parse JSON output](/docs/how_to/output_parser_json)
 - [How to: parse XML output](/docs/how_to/output_parser_xml)
--- a/docs/docs/how_to/output_parser_custom.ipynb
+++ b/docs/docs/how_to/output_parser_custom.ipynb
@@ -238,7 +238,7 @@
   "id": "3a96a846-1296-4d92-8e76-e29e583dee22",
   "metadata": {},
   "source": [
-    "Here's a simple parser that can parse a **string** representation of a boolean (e.g., `YES` or `NO`) and convert it into the corresponding `boolean` type."
+    "Here's a simple parser that can parse a **string** representation of a booealn (e.g., `YES` or `NO`) and convert it into the corresponding `boolean` type."
   ]
  },
  {
--- a/docs/docs/how_to/output_parser_string.ipynb
+++ b/docs/docs/how_to/output_parser_string.ipynb
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1d6024e0-3847-4418-b8a8-6b8f83adf4c2",
-   "metadata": {},
-   "source": [
-    "# How to parse text from message objects\n",
-    "\n",
-    ":::info Prerequisites\n",
-    "\n",
-    "This guide assumes familiarity with the following concepts:\n",
-    "- [Chat models](/docs/concepts/chat_models/)\n",
-    "- [Messages](/docs/concepts/messages/)\n",
-    "- [Output parsers](/docs/concepts/output_parsers/)\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel/)\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "LangChain [message](/docs/concepts/messages/) objects support content in a [variety of formats](/docs/concepts/messages/#content), including text, [multimodal data](/docs/concepts/multimodality/), and a list of [content block](/docs/concepts/messages/#aimessage) dicts.\n",
-    "\n",
-    "The format of [Chat model](/docs/concepts/chat_models/) response content may depend on the provider. For example, the chat model for [Anthropic](/docs/integrations/chat/anthropic/) will return string content for typical string input:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8ac74999-0740-4178-8efd-32a855592f71",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hi there! How are you doing today? Is there anything I can help you with?'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n",
-    "\n",
-    "response = llm.invoke(\"Hello\")\n",
-    "response.content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69b7c3ae-0022-4737-9db7-f44db3402de2",
-   "metadata": {},
-   "source": [
-    "But when tool calls are generated, the response content is structured into content blocks that convey the model's reasoning process:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8c87553e-4f85-46c4-8f1e-666f6a261a50",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'text': \"I'll help you get the current weather for San Francisco, California. Let me check that for you right away.\",\n",
-       "  'type': 'text'},\n",
-       " {'id': 'toolu_015PwwcKxWYctKfY3pruHFyy',\n",
-       "  'input': {'location': 'San Francisco, CA'},\n",
-       "  'name': 'get_weather',\n",
-       "  'type': 'tool_use'}]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str) -> str:\n",
-    "    \"\"\"Get the weather from a location.\"\"\"\n",
-    "\n",
-    "    return \"Sunny.\"\n",
-    "\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([get_weather])\n",
-    "\n",
-    "response = llm_with_tools.invoke(\"What's the weather in San Francisco, CA?\")\n",
-    "response.content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "039f6d62-098f-42c9-8b07-43cb1f2a831b",
-   "metadata": {},
-   "source": [
-    "To automatically parse text from message objects irrespective of the format of the underlying content, we can use [StrOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html). We can compose it with a chat model as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "0bb9b4dd-64a9-463d-9c71-df147630f3c3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "\n",
-    "chain = llm_with_tools | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4929c724-471f-4f77-a231-36e9af9418a3",
-   "metadata": {},
-   "source": [
-    "[StrOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) simplifies the extraction of text from message objects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9cbb8848-9101-465e-b230-0f7af6fb4105",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I'll help you check the weather in San Francisco, CA right away.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = chain.invoke(\"What's the weather in San Francisco, CA?\")\n",
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13642ad5-325d-4d9b-b97e-cac40345bfbc",
-   "metadata": {},
-   "source": [
-    "This is particularly useful in streaming contexts:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "28eeace3-3896-497f-93ad-544cbfb7f15c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|I'll| help| you get| the current| weather for| San Francisco, California|. Let| me retrieve| that| information for you.||||||||||"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in chain.stream(\"What's the weather in San Francisco, CA?\"):\n",
-    "    print(chunk, end=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "858e2071-a483-404e-9eca-c73a4466fd83",
-   "metadata": {},
-   "source": [
-    "See the [API Reference](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) for more information."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/docs/docs/how_to/structured_output.ipynb
+++ b/docs/docs/how_to/structured_output.ipynb
@@ -56,7 +56,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
   "id": "6d55008f",
   "metadata": {},
   "outputs": [],
@@ -81,7 +81,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
   "id": "070bf702",
   "metadata": {},
   "outputs": [
@@ -91,7 +91,7 @@
       "Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7)"
      ]
     },
-     "execution_count": 2,
+     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -147,7 +147,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
   "id": "70d82891-42e8-424a-919e-07d83bcfec61",
   "metadata": {},
   "outputs": [
@@ -159,7 +159,7 @@
       " 'rating': 7}"
      ]
     },
-     "execution_count": 3,
+     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -199,7 +199,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
   "id": "6700994a",
   "metadata": {},
   "outputs": [
@@ -207,10 +207,11 @@
     "data": {
      "text/plain": [
       "{'setup': 'Why was the cat sitting on the computer?',\n",
-       " 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
+       " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+       " 'rating': 7}"
      ]
     },
-     "execution_count": 4,
+     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -249,14 +250,12 @@
   "source": [
    "### Choosing between multiple schemas\n",
    "\n",
-    "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute.\n",
-    "\n",
-    "#### Using Pydantic"
+    "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute:"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
   "id": "9194bcf2",
   "metadata": {},
   "outputs": [
@@ -266,7 +265,7 @@
       "FinalResponse(final_output=Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7))"
      ]
     },
-     "execution_count": 7,
+     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -275,6 +274,7 @@
    "from typing import Union\n",
    "\n",
    "\n",
+    "# Pydantic\n",
    "class Joke(BaseModel):\n",
    "    \"\"\"Joke to tell user.\"\"\"\n",
    "\n",
@@ -302,17 +302,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 20,
   "id": "84d86132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with whatever you need!\"))"
+       "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a bunch of code, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?\"))"
      ]
     },
-     "execution_count": 8,
+     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -321,91 +321,6 @@
    "structured_llm.invoke(\"How are you today?\")"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "8b087112c23bafcd",
-   "metadata": {},
-   "source": [
-    "#### Using TypedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "eb0d5855-feba-48fb-84ea-9acb0edb238b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'final_output': {'setup': 'Why was the cat sitting on the computer?',\n",
-       "  'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
-       "  'rating': 7}}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from typing import Optional, Union\n",
-    "\n",
-    "from typing_extensions import Annotated, TypedDict\n",
-    "\n",
-    "\n",
-    "class Joke(TypedDict):\n",
-    "    \"\"\"Joke to tell user.\"\"\"\n",
-    "\n",
-    "    setup: Annotated[str, ..., \"The setup of the joke\"]\n",
-    "    punchline: Annotated[str, ..., \"The punchline of the joke\"]\n",
-    "    rating: Annotated[Optional[int], None, \"How funny the joke is, from 1 to 10\"]\n",
-    "\n",
-    "\n",
-    "class ConversationalResponse(TypedDict):\n",
-    "    \"\"\"Respond in a conversational manner. Be kind and helpful.\"\"\"\n",
-    "\n",
-    "    response: Annotated[str, ..., \"A conversational response to the user's query\"]\n",
-    "\n",
-    "\n",
-    "class FinalResponse(TypedDict):\n",
-    "    final_output: Union[Joke, ConversationalResponse]\n",
-    "\n",
-    "\n",
-    "structured_llm = llm.with_structured_output(FinalResponse)\n",
-    "\n",
-    "structured_llm.invoke(\"Tell me a joke about cats\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ec753809-c2c1-41c0-a3c5-69855d65475b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'final_output': {'response': \"I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with whatever you need!\"}}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "structured_llm.invoke(\"How are you today?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd22149ac9d41d57",
-   "metadata": {},
-   "source": [
-    "Responses shall be identical to the ones shown in the Pydantic example."
-   ]
-  },
  {
   "cell_type": "markdown",
   "id": "e28c14d3",
@@ -432,7 +347,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
   "id": "aff89877-28a3-472f-a1aa-eff893fe7736",
   "metadata": {},
   "outputs": [
@@ -500,7 +415,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
   "id": "283ba784-2072-47ee-9b2c-1119e3c69e8e",
   "metadata": {},
   "outputs": [
@@ -508,11 +423,11 @@
     "data": {
      "text/plain": [
       "{'setup': 'Woodpecker',\n",
-       " 'punchline': \"Woodpecker you a joke, but I'm afraid it might be too 'hole-some'!\",\n",
+       " 'punchline': \"Woodpecker who? Woodpecker who can't find a tree is just a bird with a headache!\",\n",
       " 'rating': 7}"
      ]
     },
-     "execution_count": 12,
+     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -550,7 +465,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
   "id": "d7381cb0-b2c3-4302-a319-ed72d0b9e43f",
   "metadata": {},
   "outputs": [
@@ -559,10 +474,10 @@
      "text/plain": [
       "{'setup': 'Crocodile',\n",
       " 'punchline': 'Crocodile be seeing you later, alligator!',\n",
-       " 'rating': 6}"
+       " 'rating': 7}"
      ]
     },
-     "execution_count": 13,
+     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -664,7 +579,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
   "id": "df0370e3",
   "metadata": {},
   "outputs": [
@@ -675,7 +590,7 @@
       " 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
      ]
     },
-     "execution_count": 14,
+     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -818,7 +733,7 @@
   "source": [
    "query = \"Anna is 23 years old and she is 6 feet tall\"\n",
    "\n",
-    "print(prompt.invoke({\"query\": query}).to_string())"
+    "print(prompt.invoke(query).to_string())"
   ]
  },
  {
@@ -998,9 +913,9 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-2",
   "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-2"
  },
  "language_info": {
   "codemirror_mode": {
@@ -1012,7 +927,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
@@ -17,7 +17,7 @@
   "source": [
    "# ChatCloudflareWorkersAI\n",
    "\n",
-    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
+    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
    "\n",
    "\n",
    "## Overview\n",
--- a/docs/docs/integrations/chat/xai.ipynb
+++ b/docs/docs/integrations/chat/xai.ipynb
@@ -1,332 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: xAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatXAI\n",
-    "\n",
-    "\n",
-    "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
-    "\n",
-    "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"XAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain xAI integration lives in the `langchain-xai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-xai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_xai import ChatXAI\n",
-    "\n",
-    "llm = ChatXAI(\n",
-    "    model=\"grok-beta\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e074bce1-0994-4b83-b393-ae7aa7e21750",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
-    "\n",
-    "### ChatXAI.bind_tools()\n",
-    "\n",
-    "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
-    "```\n",
-    "{\n",
-    "    \"name\": \"...\",\n",
-    "    \"description\": \"...\",\n",
-    "    \"parameters\": {...}  # JSONSchema\n",
-    "}\n",
-    "```\n",
-    "and passed in every model invocation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class GetWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a given location\"\"\"\n",
-    "\n",
-    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in San Francisco\",\n",
-    ")\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb
+++ b/docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb
@@ -34,7 +34,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -328,7 +328,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MSSQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#load-documents-with-customized-document-page-content--metadata)."
+    "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MSSQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#Load-documents-with-customized-document-page-content-&-metadata)."
   ]
  },
  {
@@ -633,7 +633,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.6"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb
+++ b/docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb
@@ -317,7 +317,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MySQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#load-documents-with-customized-document-page-content--metadata)."
+    "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MySQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#Load-documents-with-customized-document-page-content-&-metadata)."
   ]
  },
  {
@@ -619,7 +619,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.6"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/llms/aleph_alpha.ipynb
+++ b/docs/docs/integrations/llms/aleph_alpha.ipynb
@@ -7,7 +7,7 @@
   "source": [
    "# Aleph Alpha\n",
    "\n",
-    "[The Luminous series](https://docs.aleph-alpha.com/docs/category/luminous/) is a family of large language models.\n",
+    "[The Luminous series](https://docs.aleph-alpha.com/docs/introduction/luminous/) is a family of large language models.\n",
    "\n",
    "This example goes over how to use LangChain to interact with Aleph Alpha models"
   ]
--- a/docs/docs/integrations/llms/azure_openai.ipynb
+++ b/docs/docs/integrations/llms/azure_openai.ipynb
@@ -85,7 +85,7 @@
    "```python\n",
    "import openai\n",
    "\n",
-    "client = openai.AzureOpenAI(\n",
+    "client = AzureOpenAI(\n",
    "    api_version=\"2023-12-01-preview\",\n",
    ")\n",
    "\n",
--- a/docs/docs/integrations/llms/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/llms/cloudflare_workersai.ipynb
@@ -7,7 +7,7 @@
   "source": [
    "# Cloudflare Workers AI\n",
    "\n",
-    "[Cloudflare AI documentation](https://developers.cloudflare.com/workers-ai/models/) listed all generative text models available.\n",
+    "[Cloudflare AI documentation](https://developers.cloudflare.com/workers-ai/models/text-generation/) listed all generative text models available.\n",
    "\n",
    "Both Cloudflare account ID and API token are required. Find how to obtain them from [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/)."
   ]
--- a/docs/docs/integrations/llms/forefrontai.ipynb
+++ b/docs/docs/integrations/llms/forefrontai.ipynb
@@ -7,7 +7,7 @@
    "# ForefrontAI\n",
    "\n",
    "\n",
-    "The `Forefront` platform gives you the ability to fine-tune and use [open-source large language models](https://docs.forefront.ai/get-started/models).\n",
+    "The `Forefront` platform gives you the ability to fine-tune and use [open-source large language models](https://docs.forefront.ai/forefront/master/models).\n",
    "\n",
    "This notebook goes over how to use Langchain with [ForefrontAI](https://www.forefront.ai/).\n"
   ]
--- a/docs/docs/integrations/llms/vllm.ipynb
+++ b/docs/docs/integrations/llms/vllm.ipynb
@@ -266,18 +266,8 @@
    "from langchain_community.llms import VLLM\n",
    "from vllm.lora.request import LoRARequest\n",
    "\n",
-    "llm = VLLM(\n",
-    "    model=\"meta-llama/Llama-3.2-3B-Instruct\",\n",
-    "    max_new_tokens=300,\n",
-    "    top_k=1,\n",
-    "    top_p=0.90,\n",
-    "    temperature=0.1,\n",
-    "    vllm_kwargs={\n",
-    "        \"gpu_memory_utilization\": 0.5,\n",
-    "        \"enable_lora\": True,\n",
-    "        \"max_model_len\": 350,\n",
-    "    },\n",
-    ")\n",
+    "llm = VLLM(model=\"meta-llama/Llama-2-7b-hf\", enable_lora=True)\n",
+    "\n",
    "LoRA_ADAPTER_PATH = \"path/to/adapter\"\n",
    "lora_adapter = LoRARequest(\"lora_adapter\", 1, LoRA_ADAPTER_PATH)\n",
    "\n",
--- a/docs/docs/integrations/providers/astradb.mdx
+++ b/docs/docs/integrations/providers/astradb.mdx
@@ -133,7 +133,7 @@ store = AstraDBStore(
 )
 ```

-See the API Reference for the [AstraDBStore](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBStore.html).
+Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbstore).

 ## Byte Store

@@ -147,4 +147,4 @@ store = AstraDBByteStore(
 )
 ```

-See the API reference for the [AstraDBByteStore](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html).
+Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbbytestore).
--- a/docs/docs/integrations/providers/nvidia.mdx
+++ b/docs/docs/integrations/providers/nvidia.mdx
@@ -51,7 +51,7 @@ result = llm.invoke("Write a ballad about LangChain.")
 print(result.content)
 ```

-Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM which is part of NVIDIA AI Enterprise, shown in the next section [Working with NVIDIA NIMs](#working-with-nvidia-nims).
+Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM which is part of NVIDIA AI Enterprise, shown in the next section [Working with NVIDIA NIMs](##working-with-nvidia-nims). 

 ## Working with NVIDIA NIMs
 When ready to deploy, you can self-host models with NVIDIA NIM—which is included with the NVIDIA AI Enterprise software license—and run them anywhere, giving you ownership of your customizations and full control of your intellectual property (IP) and AI applications.
--- a/docs/docs/integrations/providers/unstructured.mdx
+++ b/docs/docs/integrations/providers/unstructured.mdx
@@ -164,7 +164,7 @@ from langchain_community.document_loaders import UnstructuredOrgModeLoader

 ### UnstructuredPDFLoader

-See a [usage example](/docs/how_to/document_loader_pdf/#layout-analysis-and-extraction-of-text-from-images).
+See a [usage example](/docs/how_to/document_loader_pdf#using-unstructured).

 ```python
 from langchain_community.document_loaders import UnstructuredPDFLoader
--- a/docs/docs/integrations/providers/xai.ipynb
+++ b/docs/docs/integrations/providers/xai.ipynb
@@ -1,97 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2970dd75-8ebf-4b51-8282-9b454b8f356d",
-   "metadata": {},
-   "source": [
-    "# xAI\n",
-    "\n",
-    "[xAI](https://console.x.ai) offers an API to interact with Grok models.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with xAI models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c47fc36",
-   "metadata": {},
-   "source": [
-    "## Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ecdb29d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade langchain-xai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89883202",
-   "metadata": {},
-   "source": [
-    "## Environment\n",
-    "\n",
-    "To use xAI, you'll need to [create an API key](https://console.x.ai/). The API key can be passed in as an init param ``xai_api_key`` or set as environment variable ``XAI_API_KEY``."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8304b4d9",
-   "metadata": {},
-   "source": [
-    "## Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "637bb53f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Querying chat models with xAI\n",
-    "\n",
-    "from langchain_xai import ChatXAI\n",
-    "\n",
-    "chat = ChatXAI(\n",
-    "    # xai_api_key=\"YOUR_API_KEY\",\n",
-    "    model=\"grok-beta\",\n",
-    ")\n",
-    "\n",
-    "# stream the response back from the model\n",
-    "for m in chat.stream(\"Tell me fun things to do in NYC\"):\n",
-    "    print(m.content, end=\"\", flush=True)\n",
-    "\n",
-    "# if you don't want to do streaming, you can use the invoke method\n",
-    "# chat.invoke(\"Tell me fun things to do in NYC\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/docs/docs/integrations/tools/google_books.ipynb
+++ b/docs/docs/integrations/tools/google_books.ipynb
@@ -139,7 +139,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### [Invoke directly with args](/docs/concepts/tools)\n",
+    "### [Invoke directly with args](/docs/concepts/#invoke-with-just-the-arguments)\n",
    "\n",
    "See below for an direct invocation example."
   ]
@@ -165,7 +165,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### [Invoke with ToolCall](/docs/concepts/tools)\n",
+    "### [Invoke with ToolCall](/docs/concepts/#invoke-with-toolcall)\n",
    "\n",
    "See below for a tool call example."
   ]
--- a/docs/docs/integrations/tools/tavily_search.ipynb
+++ b/docs/docs/integrations/tools/tavily_search.ipynb
@@ -186,7 +186,7 @@
    }
   ],
   "source": [
-    "# This is usually generated by a model, but we'll create a tool call directly for demo purposes.\n",
+    "# This is usually generated by a model, but we'll create a tool call directly for demon purposes.\n",
    "model_generated_tool_call = {\n",
    "    \"args\": {\"query\": \"euro 2024 host nation\"},\n",
    "    \"id\": \"1\",\n",
--- a/docs/docs/integrations/tools/zapier.ipynb
+++ b/docs/docs/integrations/tools/zapier.ipynb
@@ -110,19 +110,19 @@
     "text": [
      "\n",
      "\n",
-      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
-      "\u001B[32;1m\u001B[1;3m I need to find the email and summarize it.\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to find the email and summarize it.\n",
      "Action: Gmail: Find Email\n",
-      "Action Input: Find the latest email from Silicon Valley Bank\u001B[0m\n",
-      "Observation: \u001B[31;1m\u001B[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I need to summarize the email and send it to the #test-zapier channel in Slack.\n",
+      "Action Input: Find the latest email from Silicon Valley Bank\u001b[0m\n",
+      "Observation: \u001b[31;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I need to summarize the email and send it to the #test-zapier channel in Slack.\n",
      "Action: Slack: Send Channel Message\n",
-      "Action Input: Send a slack message to the #test-zapier channel with the text \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\"\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3m{\"message__text\": \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\", \"message__permalink\": \"https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259\", \"channel\": \"C04TSGU0RA7\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:58:52Z\", \"message__bot_profile__icons__image_36\": \"https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png\", \"message__blocks[]block_id\": \"kdZZ\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer.\n",
-      "Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.\u001B[0m\n",
+      "Action Input: Send a slack message to the #test-zapier channel with the text \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\"\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m{\"message__text\": \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\", \"message__permalink\": \"https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259\", \"channel\": \"C04TSGU0RA7\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:58:52Z\", \"message__bot_profile__icons__image_36\": \"https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png\", \"message__blocks[]block_id\": \"kdZZ\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
+      "Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.\u001b[0m\n",
      "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
@@ -286,18 +286,18 @@
     "text": [
      "\n",
      "\n",
-      "\u001B[1m> Entering new SimpleSequentialChain chain...\u001B[0m\n",
-      "\u001B[36;1m\u001B[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001B[0m\n",
-      "\u001B[33;1m\u001B[1;3m\n",
+      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m\n",
      "Dear Silicon Valley Bridge Bank, \n",
      "\n",
      "Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n",
      "\n",
      "Best regards, \n",
-      "[Your Name]\u001B[0m\n",
-      "\u001B[38;5;200m\u001B[1;3m{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[['text']]\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001B[0m\n",
+      "[Your Name]\u001b[0m\n",
+      "\u001b[38;5;200m\u001b[1;3m{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[['text']]\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
      "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
@@ -325,7 +325,7 @@
   "id": "09ff954e-45f2-4595-92ea-91627abde4a0",
   "metadata": {},
   "source": [
-    "## Example Using OAuth Access Token{#oauth}\n",
+    "## <a id=\"oauth\">Example Using OAuth Access Token</a>\n",
    "The below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the [authentication docs](https://nla.zapier.com/docs/authentication/#oauth-credentials) for full user-facing oauth developer support.\n",
    "\n",
    "The developer is tasked with handling the OAuth handshaking to procure and refresh the access token."
--- a/docs/docs/integrations/vectorstores/kinetica.ipynb
+++ b/docs/docs/integrations/vectorstores/kinetica.ipynb
@@ -33,13 +33,35 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Requirement already satisfied: gpudb==7.2.0.0b in /home/anindyam/kinetica/kinetica-github/langchain/libs/langchain/.venv/lib/python3.8/site-packages (7.2.0.0b0)\n",
+      "Requirement already satisfied: future in /home/anindyam/kinetica/kinetica-github/langchain/libs/langchain/.venv/lib/python3.8/site-packages (from gpudb==7.2.0.0b) (0.18.3)\n",
+      "Requirement already satisfied: pyzmq in /home/anindyam/kinetica/kinetica-github/langchain/libs/langchain/.venv/lib/python3.8/site-packages (from gpudb==7.2.0.0b) (25.1.2)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
   "source": [
    "# Pip install necessary package\n",
    "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
-    "%pip install gpudb>=7.2.2.0 \n",
+    "%pip install gpudb==7.2.0.9\n",
    "%pip install --upgrade --quiet  tiktoken"
   ]
  },
@@ -52,7 +74,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -65,7 +87,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
@@ -74,7 +96,7 @@
       "False"
      ]
     },
-     "execution_count": 3,
+     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -88,30 +110,38 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain_community.vectorstores import (\n",
+    "    DistanceStrategy,\n",
    "    Kinetica,\n",
    "    KineticaSettings,\n",
    ")\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_text_splitters import CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
-    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
+    "loader = TextLoader(\"../../how_to/state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -127,81 +157,6 @@
    "    return KineticaSettings(host=HOST, username=USERNAME, password=PASSWORD)"
   ]
  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from uuid import uuid4\n",
-    "\n",
-    "from langchain_core.documents import Document\n",
-    "\n",
-    "document_1 = Document(\n",
-    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_2 = Document(\n",
-    "    page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
-    "    metadata={\"source\": \"news\"},\n",
-    ")\n",
-    "\n",
-    "document_3 = Document(\n",
-    "    page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_4 = Document(\n",
-    "    page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
-    "    metadata={\"source\": \"news\"},\n",
-    ")\n",
-    "\n",
-    "document_5 = Document(\n",
-    "    page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_6 = Document(\n",
-    "    page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
-    "    metadata={\"source\": \"website\"},\n",
-    ")\n",
-    "\n",
-    "document_7 = Document(\n",
-    "    page_content=\"The top 10 soccer players in the world right now.\",\n",
-    "    metadata={\"source\": \"website\"},\n",
-    ")\n",
-    "\n",
-    "document_8 = Document(\n",
-    "    page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "document_9 = Document(\n",
-    "    page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
-    "    metadata={\"source\": \"news\"},\n",
-    ")\n",
-    "\n",
-    "document_10 = Document(\n",
-    "    page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
-    "    metadata={\"source\": \"tweet\"},\n",
-    ")\n",
-    "\n",
-    "documents = [\n",
-    "    document_1,\n",
-    "    document_2,\n",
-    "    document_3,\n",
-    "    document_4,\n",
-    "    document_5,\n",
-    "    document_6,\n",
-    "    document_7,\n",
-    "    document_8,\n",
-    "    document_9,\n",
-    "    document_10,\n",
-    "]\n",
-    "uuids = [str(uuid4()) for _ in range(len(documents))]"
-   ]
-  },
  {
   "cell_type": "markdown",
   "metadata": {},
@@ -211,92 +166,207 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['05e5a484-0273-49d1-90eb-1276baca31de',\n",
-       " 'd98b808f-dc0b-4328-bdbf-88f6b2ab6040',\n",
-       " 'ba0968d4-e344-4285-ae0f-f5199b56f9d6',\n",
-       " 'a25393b8-6539-45b5-993e-ea16d01941ec',\n",
-       " '804a37e3-1278-4b60-8b02-36b159ee8c1a',\n",
-       " '9688b594-3dc6-41d2-a937-babf8ff24c2f',\n",
-       " '40f7b8fe-67c7-489a-a5a5-7d3965e33bba',\n",
-       " 'b4fc1376-c113-41e9-8f16-f9320517bedd',\n",
-       " '4d94d089-fdde-442b-84ab-36d9fe0670c8',\n",
-       " '66fdb79d-49ce-4b06-901a-fda6271baf2a']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "# The Kinetica Module will try to create a table with the name of the collection.\n",
    "# So, make sure that the collection name is unique and the user has the permission to create a table.\n",
    "\n",
-    "COLLECTION_NAME = \"langchain_example\"\n",
+    "COLLECTION_NAME = \"state_of_the_union_test\"\n",
    "connection = create_config()\n",
    "\n",
-    "db = Kinetica(\n",
-    "    connection,\n",
-    "    embeddings,\n",
+    "db = Kinetica.from_documents(\n",
+    "    embedding=embeddings,\n",
+    "    documents=docs,\n",
    "    collection_name=COLLECTION_NAME,\n",
-    ")\n",
-    "\n",
-    "db.add_documents(documents=documents, ids=uuids)"
+    "    config=connection,\n",
+    ")"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
-    "# query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "# docs_with_score = db.similarity_search_with_score(query)"
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs_with_score = db.similarity_search_with_score(query)"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6077010035514832\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
-      "Similarity Search\n",
-      "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
-      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
      "\n",
-      "Similarity search with score\n",
-      "* [SIM=0.945397] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6077010035514832\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6596046090126038\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6597143411636353\n",
+      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "\n",
+      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "\n",
+      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
+      "\n",
+      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
+      "\n",
+      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
-    "print()\n",
-    "print(\"Similarity Search\")\n",
-    "results = db.similarity_search(\n",
-    "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
-    "    k=2,\n",
-    "    filter={\"source\": \"tweet\"},\n",
-    ")\n",
-    "for res in results:\n",
-    "    print(f\"* {res.page_content} [{res.metadata}]\")\n",
-    "\n",
-    "print()\n",
-    "print(\"Similarity search with score\")\n",
-    "results = db.similarity_search_with_score(\n",
-    "    \"Will it be hot tomorrow?\", k=1, filter={\"source\": \"news\"}\n",
-    ")\n",
-    "for res, score in results:\n",
-    "    print(f\"* [SIM={score:3f}] {res.page_content} [{res.metadata}]\")"
+    "for doc, score in docs_with_score:\n",
+    "    print(\"-\" * 80)\n",
+    "    print(\"Score: \", score)\n",
+    "    print(doc.page_content)\n",
+    "    print(\"-\" * 80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Maximal Marginal Relevance Search (MMR)\n",
+    "Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_with_score = db.max_marginal_relevance_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6077010035514832\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6852865219116211\n",
+      "It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China.  \n",
+      "\n",
+      "As I’ve told Xi Jinping, it is never a good bet to bet against the American people. \n",
+      "\n",
+      "We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. \n",
+      "\n",
+      "And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice. \n",
+      "\n",
+      "We’ll build a national network of 500,000 electric vehicle charging stations, begin to replace poisonous lead pipes—so every child—and every American—has clean water to drink at home and at school, provide affordable high-speed internet for every American—urban, suburban, rural, and tribal communities. \n",
+      "\n",
+      "4,000 projects have already been announced. \n",
+      "\n",
+      "And tonight, I’m announcing that this year we will start fixing over 65,000 miles of highway and 1,500 bridges in disrepair.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6866700053215027\n",
+      "We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n",
+      "\n",
+      "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n",
+      "\n",
+      "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n",
+      "\n",
+      "Officer Mora was 27 years old. \n",
+      "\n",
+      "Officer Rivera was 22. \n",
+      "\n",
+      "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n",
+      "\n",
+      "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
+      "\n",
+      "I’ve worked on these issues a long time. \n",
+      "\n",
+      "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n",
+      "--------------------------------------------------------------------------------\n",
+      "--------------------------------------------------------------------------------\n",
+      "Score:  0.6936529278755188\n",
+      "But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body. \n",
+      "\n",
+      "Danielle says Heath was a fighter to the very end. \n",
+      "\n",
+      "He didn’t know how to stop fighting, and neither did she. \n",
+      "\n",
+      "Through her pain she found purpose to demand we do better. \n",
+      "\n",
+      "Tonight, Danielle—we are. \n",
+      "\n",
+      "The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits. \n",
+      "\n",
+      "And tonight, I’m announcing we’re expanding eligibility to veterans suffering from nine respiratory cancers. \n",
+      "\n",
+      "I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve. \n",
+      "\n",
+      "And fourth, let’s end cancer as we know it. \n",
+      "\n",
+      "This is personal to me and Jill, to Kamala, and to so many of you. \n",
+      "\n",
+      "Cancer is the #2 cause of death in America–second only to heart disease.\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc, score in docs_with_score:\n",
+    "    print(\"-\" * 80)\n",
+    "    print(\"Score: \", score)\n",
+    "    print(doc.page_content)\n",
+    "    print(\"-\" * 80)"
   ]
  },
  {
@@ -311,7 +381,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -332,16 +402,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "['68c4c679-c4d9-4f2d-bf01-f6c4f2181503']"
+       "['b94dc67c-ce7e-11ee-b8cb-b940b0e45762']"
      ]
     },
-     "execution_count": 12,
+     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -352,7 +422,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -361,16 +431,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "(Document(metadata={}, page_content='foo'), 0.0015394920483231544)"
+       "(Document(page_content='foo'), 0.0)"
      ]
     },
-     "execution_count": 14,
+     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -381,17 +451,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "(Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!'),\n",
-       " 1.2609431743621826)"
+       "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../how_to/state_of_the_union.txt'}),\n",
+       " 0.6946534514427185)"
      ]
     },
-     "execution_count": 15,
+     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -411,12 +481,12 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = Kinetica.from_documents(\n",
-    "    documents=documents,\n",
+    "    documents=docs,\n",
    "    embedding=embeddings,\n",
    "    collection_name=COLLECTION_NAME,\n",
    "    config=connection,\n",
@@ -426,7 +496,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -435,17 +505,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "(Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!'),\n",
-       " 1.260920763015747)"
+       "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../how_to/state_of_the_union.txt'}),\n",
+       " 0.6946534514427185)"
      ]
     },
-     "execution_count": 18,
+     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -463,7 +533,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -472,14 +542,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "tags=['Kinetica', 'OpenAIEmbeddings'] vectorstore=<langchain_community.vectorstores.kinetica.Kinetica object at 0x7a48142b2230> search_kwargs={}\n"
+      "tags=['Kinetica', 'OpenAIEmbeddings'] vectorstore=<langchain_community.vectorstores.kinetica.Kinetica object at 0x7f1644375e20>\n"
     ]
    }
   ],
@@ -504,7 +574,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.8.10"
  }
 },
 "nbformat": 4,
--- a/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
+++ b/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
@@ -6,7 +6,7 @@
   "source": [
    "# INVALID_TOOL_RESULTS\n",
    "\n",
-    "You are passing too many, too few, or mismatched [`ToolMessages`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html#toolmessage) to a model.\n",
+    "You are passing too many, too few, or mismatched [`ToolMessages`](https://api.js.langchain.com/classes/_langchain_core.messages_tool.ToolMessage.html) to a model.\n",
    "\n",
    "When [using a model to call tools](/docs/concepts/tool_calling), the [`AIMessage`](https://api.js.langchain.com/classes/_langchain_core.messages.AIMessage.html)\n",
    "the model responds with will contain a `tool_calls` array. To continue the flow, the next messages you pass back to the model must\n",
--- a/docs/docs/tutorials/sql_qa.ipynb
+++ b/docs/docs/tutorials/sql_qa.ipynb
@@ -28,7 +28,7 @@
    "\n",
    "At a high-level, the steps of these systems are:\n",
    "\n",
-    "1. **Convert question to SQL query**: Model converts user input to a SQL query.\n",
+    "1. **Convert question to DSL query**: Model converts user input to a SQL query.\n",
    "2. **Execute SQL query**: Execute the query.\n",
    "3. **Answer the question**: Model responds to user input using the query results.\n",
    "\n",
--- a/docs/docs/versions/migrating_memory/index.mdx
+++ b/docs/docs/versions/migrating_memory/index.mdx
@@ -108,8 +108,9 @@ And specific backend implementations of abstractions:
 | `SQLiteEntityStore`       | A specific implementation of `BaseEntityStore` that uses SQLite as the backend.                          |
 | `UpstashRedisEntityStore` | A specific implementation of `BaseEntityStore` that uses Upstash as the backend.                         |

-These abstractions have received limited development since their initial release. This is because they generally require significant customization for a specific application to be effective, making
-them less widely used than the conversation history management abstractions.
+These abstractions have not received much development since their initial release. The reason
+is that for these abstractions to be useful they typically require a lot of specialization for a particular application, so these
+abstractions are not as widely used as the conversation history management abstractions.

 For this reason, there are no migration guides for these abstractions. If you're struggling to migrate an application
 that relies on these abstractions, please:
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -26,7 +26,6 @@ const config = {
  trailingSlash: true,
  onBrokenLinks: "throw",
  onBrokenMarkdownLinks: "throw",
-  onBrokenAnchors: "throw",

  themes: ["@docusaurus/theme-mermaid"],
  markdown: {
--- a/docs/src/theme/FeatureTables.js
+++ b/docs/src/theme/FeatureTables.js
@@ -227,17 +227,6 @@ const FEATURE_TABLES = {
                "local": false,
                "apiLink": "https://python.langchain.com/api_reference/ibm/chat_models/langchain_ibm.chat_models.ChatWatsonx.html"
            },
-            {
-                "name": "ChatXAI",
-                "package": "langchain-xai",
-                "link": "xai",
-                "structured_output": true,
-                "tool_calling": true,
-                "json_mode": false,
-                "multimodal": false,
-                "local": false,
-                "apiLink": "https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
-            },
        ],
    },
    llms: {
--- a/libs/community/langchain_community/callbacks/tracers/wandb.py
+++ b/libs/community/langchain_community/callbacks/tracers/wandb.py
@@ -16,7 +16,6 @@ from typing import (
    Union,
 )

-from langchain_core._api import warn_deprecated
 from langchain_core.output_parsers.pydantic import PydanticBaseModel
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.schemas import Run
@@ -326,22 +325,6 @@ class WandbTracer(BaseTracer):
        self._run_args = run_args
        self._ensure_run(should_print_url=(wandb.run is None))
        self._io_serializer = io_serializer
-        warn_deprecated(
-            "0.3.8",
-            pending=False,
-            message=(
-                "Please use the `WeaveTracer` from the `weave` package instead of this."
-                "The `WeaveTracer` is a more flexible and powerful tool for logging "
-                "and tracing your LangChain callables."
-                "Find more information at https://weave-docs.wandb.ai/guides/integrations/langchain"
-            ),
-            alternative=(
-                "Please instantiate the WeaveTracer from "
-                "`weave.integrations.langchain import WeaveTracer` ."
-                "For autologging simply use `weave.init()` and log all traces "
-                "from your LangChain callables."
-            ),
-        )

    def finish(self) -> None:
        """Waits for all asynchronous processes to finish and data to upload.
--- a/libs/community/langchain_community/chat_models/anyscale.py
+++ b/libs/community/langchain_community/chat_models/anyscale.py
@@ -220,9 +220,7 @@ class ChatAnyscale(ChatOpenAI):
        Official documentation: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
        """
        if tools is not None:
-            warnings.warn(
-                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
-            )
+            warnings.warn("Counting tokens in tool schemas is not yet supported.")
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
--- a/libs/community/langchain_community/chat_models/everlyai.py
+++ b/libs/community/langchain_community/chat_models/everlyai.py
@@ -162,9 +162,7 @@ class ChatEverlyAI(ChatOpenAI):
        Official documentation: https://github.com/openai/openai-cookbook/blob/
        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
        if tools is not None:
-            warnings.warn(
-                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
-            )
+            warnings.warn("Counting tokens in tool schemas is not yet supported.")
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
--- a/libs/community/langchain_community/chat_models/openai.py
+++ b/libs/community/langchain_community/chat_models/openai.py
@@ -657,9 +657,7 @@ class ChatOpenAI(BaseChatModel):
        Official documentation: https://github.com/openai/openai-cookbook/blob/
        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
        if tools is not None:
-            warnings.warn(
-                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
-            )
+            warnings.warn("Counting tokens in tool schemas is not yet supported.")
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
--- a/libs/community/langchain_community/embeddings/baichuan.py
+++ b/libs/community/langchain_community/embeddings/baichuan.py
@@ -15,7 +15,7 @@ from pydantic import (
 from requests import RequestException
 from typing_extensions import Self

-BAICHUAN_API_URL: str = "https://api.baichuan-ai.com/v1/embeddings"
+BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings"

 # BaichuanTextEmbeddings is an embedding model provided by Baichuan Inc. (https://www.baichuan-ai.com/home).
 # As of today (Jan 25th, 2024) BaichuanTextEmbeddings ranks #1 in C-MTEB
@@ -25,7 +25,7 @@ BAICHUAN_API_URL: str = "https://api.baichuan-ai.com/v1/embeddings"
 # Official Website: https://platform.baichuan-ai.com/docs/text-Embedding
 # An API-key is required to use this embedding model. You can get one by registering
 # at https://platform.baichuan-ai.com/docs/text-Embedding.
-# BaichuanTextEmbeddings support 512 token window and produces vectors with
+# BaichuanTextEmbeddings support 512 token window and preduces vectors with
 # 1024 dimensions.


--- a/libs/community/langchain_community/embeddings/dashscope.py
+++ b/libs/community/langchain_community/embeddings/dashscope.py
@@ -143,6 +143,8 @@ class DashScopeEmbeddings(BaseModel, Embeddings):

        Args:
            texts: The list of texts to embed.
+            chunk_size: The chunk size of embeddings. If None, will use the chunk size
+                specified by the class.

        Returns:
            List of embeddings, one for each text.
--- a/libs/community/langchain_community/embeddings/fake.py
+++ b/libs/community/langchain_community/embeddings/fake.py
@@ -36,8 +36,7 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
        np.random.seed(seed)
        return list(np.random.normal(size=self.size))

-    @staticmethod
-    def _get_seed(text: str) -> int:
+    def _get_seed(self, text: str) -> int:
        """
        Get a seed for the random generator, using the hash of the text.
        """
--- a/libs/community/langchain_community/vectorstores/kinetica.py
+++ b/libs/community/langchain_community/vectorstores/kinetica.py
@@ -93,7 +93,7 @@ class Kinetica(VectorStore):
    To use, you should have the ``gpudb`` python package installed.

    Args:
-        config: Kinetica connection settings class.
+        kinetica_settings: Kinetica connection settings class.
        embedding_function: Any embedding function implementing
            `langchain.embeddings.base.Embeddings` interface.
        collection_name: The name of the collection to use. (default: langchain)
@@ -170,7 +170,7 @@ class Kinetica(VectorStore):
        except ImportError:
            raise ImportError(
                "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb>=7.2.2.0`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
            )

        self.dimensions = dimensions
@@ -199,7 +199,7 @@ class Kinetica(VectorStore):
        except ImportError:
            raise ImportError(
                "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb>=7.2.2.0`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
            )

        options = GPUdb.Options()
@@ -290,7 +290,7 @@ class Kinetica(VectorStore):
        except ImportError:
            raise ImportError(
                "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb>=7.2.2.0`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
            )
        return GPUdbTable(
            _type=self.table_schema,
@@ -428,7 +428,7 @@ class Kinetica(VectorStore):
        k: int = 4,
        filter: Optional[dict] = None,
    ) -> List[Tuple[Document, float]]:
-        # from gpudb import GPUdbException
+        from gpudb import GPUdbException

        resp: Dict = self.__query_collection(embedding, k, filter)
        if resp and resp["status_info"]["status"] == "OK" and "records" in resp:
@@ -436,10 +436,9 @@ class Kinetica(VectorStore):
            results = list(zip(*list(records.values())))

            return self._results_to_docs_and_scores(results)
-
-        self.logger.error(resp["status_info"]["message"])
-        # raise GPUdbException(resp["status_info"]["message"])
-        return []
+        else:
+            self.logger.error(resp["status_info"]["message"])
+            raise GPUdbException(resp["status_info"]["message"])

    def similarity_search_by_vector(
        self,
@@ -465,20 +464,16 @@ class Kinetica(VectorStore):

    def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]:
        """Return docs and scores from results."""
-        docs = (
-            [
-                (
-                    Document(
-                        page_content=result[0],
-                        metadata=json.loads(result[1]),
-                    ),
-                    result[2] if self.embedding_function is not None else None,
-                )
-                for result in results
-            ]
-            if len(results) > 0
-            else []
-        )
+        docs = [
+            (
+                Document(
+                    page_content=result[0],
+                    metadata=json.loads(result[1]),
+                ),
+                result[2] if self.embedding_function is not None else None,
+            )
+            for result in results
+        ]
        return docs

    def _select_relevance_score_fn(self) -> Callable[[float], float]:
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.

 [[package]]
 name = "aiohappyeyeballs"
@@ -842,37 +842,37 @@ typing-inspect = ">=0.4.0,<1"

 [[package]]
 name = "debugpy"
-version = "1.8.8"
+version = "1.8.7"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "debugpy-1.8.8-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:e59b1607c51b71545cb3496876544f7186a7a27c00b436a62f285603cc68d1c6"},
-    {file = "debugpy-1.8.8-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6531d952b565b7cb2fbd1ef5df3d333cf160b44f37547a4e7cf73666aca5d8d"},
-    {file = "debugpy-1.8.8-cp310-cp310-win32.whl", hash = "sha256:b01f4a5e5c5fb1d34f4ccba99a20ed01eabc45a4684f4948b5db17a319dfb23f"},
-    {file = "debugpy-1.8.8-cp310-cp310-win_amd64.whl", hash = "sha256:535f4fb1c024ddca5913bb0eb17880c8f24ba28aa2c225059db145ee557035e9"},
-    {file = "debugpy-1.8.8-cp311-cp311-macosx_14_0_universal2.whl", hash = "sha256:c399023146e40ae373753a58d1be0a98bf6397fadc737b97ad612886b53df318"},
-    {file = "debugpy-1.8.8-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:09cc7b162586ea2171eea055985da2702b0723f6f907a423c9b2da5996ad67ba"},
-    {file = "debugpy-1.8.8-cp311-cp311-win32.whl", hash = "sha256:eea8821d998ebeb02f0625dd0d76839ddde8cbf8152ebbe289dd7acf2cdc6b98"},
-    {file = "debugpy-1.8.8-cp311-cp311-win_amd64.whl", hash = "sha256:d4483836da2a533f4b1454dffc9f668096ac0433de855f0c22cdce8c9f7e10c4"},
-    {file = "debugpy-1.8.8-cp312-cp312-macosx_14_0_universal2.whl", hash = "sha256:0cc94186340be87b9ac5a707184ec8f36547fb66636d1029ff4f1cc020e53996"},
-    {file = "debugpy-1.8.8-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64674e95916e53c2e9540a056e5f489e0ad4872645399d778f7c598eacb7b7f9"},
-    {file = "debugpy-1.8.8-cp312-cp312-win32.whl", hash = "sha256:5c6e885dbf12015aed73770f29dec7023cb310d0dc2ba8bfbeb5c8e43f80edc9"},
-    {file = "debugpy-1.8.8-cp312-cp312-win_amd64.whl", hash = "sha256:19ffbd84e757a6ca0113574d1bf5a2298b3947320a3e9d7d8dc3377f02d9f864"},
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 [[package]]
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 [[package]]
 name = "jedi"
-version = "0.19.2"
+version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
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 [package.extras]
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-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]

 [[package]]
 name = "jinja2"
@@ -1487,18 +1487,15 @@ i18n = ["Babel (>=2.7)"]

 [[package]]
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 description = "A Python implementation of the JSON5 data format."
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-
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1833,7 +1830,7 @@ files = [

 [[package]]
 name = "langchain"
-version = "0.3.7"
+version = "0.3.6"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1862,7 +1859,7 @@ url = "../langchain"

 [[package]]
 name = "langchain-core"
-version = "0.3.17"
+version = "0.3.15"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1887,7 +1884,7 @@ url = "../core"

 [[package]]
 name = "langchain-standard-tests"
-version = "0.3.0"
+version = "0.1.1"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1906,7 +1903,7 @@ url = "../standard-tests"

 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.2"
+version = "0.3.1"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1922,13 +1919,13 @@ url = "../text-splitters"

 [[package]]
 name = "langsmith"
-version = "0.1.142"
+version = "0.1.138"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
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 [package.dependencies]
@@ -2030,13 +2027,13 @@ files = [

 [[package]]
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+version = "3.23.0"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.9"
 files = [
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 [package.dependencies]
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 [package.extras]
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 tests = ["pytest", "simplejson"]

 [[package]]
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 [[package]]
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 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [package.extras]
@@ -4301,15 +4331,19 @@ files = [

 [[package]]
 name = "webcolors"
-version = "24.11.1"
+version = "24.8.0"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
-    {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
+    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
+    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
 ]

+[package.extras]
+docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
+tests = ["coverage[toml]"]
+
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -4525,13 +4559,13 @@ propcache = ">=0.2.0"

 [[package]]
 name = "zipp"
-version = "3.21.0"
+version = "3.20.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
-    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
 ]

 [package.extras]
@@ -4545,4 +4579,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "a02b86c4746a93545fe99f8a30ea9becedead16f881254d7763a052ec50c9648"
+content-hash = "0179027f05f5159e95267551c013cac474abd3e9f137399f715835b3070d9c33"
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-community"
-version = "0.3.7"
+version = "0.3.5"
 description = "Community contributed LangChain integrations."
 authors = []
 license = "MIT"
@@ -30,8 +30,8 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy

 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.17"
-langchain = "^0.3.7"
+langchain-core = "^0.3.15"
+langchain = "^0.3.6"
 SQLAlchemy = ">=1.4,<2.0.36"
 requests = "^2"
 PyYAML = ">=5.3"
--- a/libs/core/langchain_core/callbacks/file.py
+++ b/libs/core/langchain_core/callbacks/file.py
@@ -13,8 +13,7 @@ class FileCallbackHandler(BaseCallbackHandler):
    """Callback Handler that writes to a file.

    Parameters:
-        filename: The file to write to.
-        mode: The mode to open the file in. Defaults to "a".
+        file: The file to write to.
        color: The color to use for the text.
    """

--- a/libs/core/langchain_core/callbacks/manager.py
+++ b/libs/core/langchain_core/callbacks/manager.py
@@ -1298,7 +1298,7 @@ class CallbackManager(BaseCallbackManager):
        run_id: Optional[UUID] = None,
        **kwargs: Any,
    ) -> list[CallbackManagerForLLMRun]:
-        """Run when chat model starts running.
+        """Run when LLM starts running.

        Args:
            serialized (Dict[str, Any]): The serialized LLM.
--- a/libs/core/langchain_core/indexing/init.py
+++ b/libs/core/langchain_core/indexing/init.py
@@ -7,7 +7,6 @@ if it's unchanged.

 from langchain_core.indexing.api import IndexingResult, aindex, index
 from langchain_core.indexing.base import (
-    DeleteResponse,
    DocumentIndex,
    InMemoryRecordManager,
    RecordManager,
@@ -16,7 +15,6 @@ from langchain_core.indexing.base import (

 __all__ = [
    "aindex",
-    "DeleteResponse",
    "DocumentIndex",
    "index",
    "IndexingResult",
--- a/libs/core/langchain_core/language_models/base.py
+++ b/libs/core/langchain_core/language_models/base.py
@@ -1,6 +1,5 @@
 from __future__ import annotations

-import warnings
 from abc import ABC, abstractmethod
 from collections.abc import Mapping, Sequence
 from functools import cache
@@ -385,11 +384,6 @@ class BaseLanguageModel(
        Returns:
            The sum of the number of tokens across the messages.
        """
-        if tools is not None:
-            warnings.warn(
-                "Counting tokens in tool schemas is not yet supported. Ignoring tools.",
-                stacklevel=2,
-            )
        return sum([self.get_num_tokens(get_buffer_string([m])) for m in messages])

    @classmethod
--- a/libs/core/langchain_core/load/load.py
+++ b/libs/core/langchain_core/load/load.py
@@ -24,7 +24,6 @@ DEFAULT_NAMESPACES = [
    "langchain_google_vertexai",
    "langchain_mistralai",
    "langchain_fireworks",
-    "langchain_xai",
 ]
 # Namespaces for which only deserializing via the SERIALIZABLE_MAPPING is allowed.
 # Load by path is not allowed.
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -208,20 +208,21 @@ lxml = ["lxml"]

 [[package]]
 name = "bleach"
-version = "6.2.0"
+version = "6.1.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
-    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
+    {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
+    {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
 ]

 [package.dependencies]
+six = ">=1.9.0"
 webencodings = "*"

 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.5)"]
+css = ["tinycss2 (>=1.1.0,<1.3)"]

 [[package]]
 name = "certifi"
@@ -457,37 +458,37 @@ test = ["pytest"]

 [[package]]
 name = "debugpy"
-version = "1.8.8"
+version = "1.8.7"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [[package]]
@@ -808,22 +809,22 @@ arrow = ">=0.15.0"

 [[package]]
 name = "jedi"
-version = "0.19.2"
+version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]

 [package.dependencies]
-parso = ">=0.8.4,<0.9.0"
+parso = ">=0.8.3,<0.9.0"

 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]

 [[package]]
 name = "jinja2"
@@ -844,18 +845,15 @@ i18n = ["Babel (>=2.7)"]

 [[package]]
 name = "json5"
-version = "0.9.28"
+version = "0.9.25"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.8"
 files = [
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+    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
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-[package.extras]
-dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
-
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1107,13 +1105,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>

 [[package]]
 name = "jupyterlab"
-version = "4.3.0"
+version = "4.2.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -1134,9 +1132,9 @@ tornado = ">=6.2.0"
 traitlets = "*"

 [package.extras]
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+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
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@@ -1190,7 +1188,7 @@ files = [

 [[package]]
 name = "langchain-standard-tests"
-version = "0.3.0"
+version = "0.1.1"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1199,7 +1197,7 @@ develop = true

 [package.dependencies]
 httpx = "^0.27.0"
-langchain-core = "^0.3.15"
+langchain-core = "^0.3.0"
 pytest = ">=7,<9"
 syrupy = "^4"

@@ -1209,7 +1207,7 @@ url = "../standard-tests"

 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.2"
+version = "0.3.0"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1217,7 +1215,7 @@ files = []
 develop = true

 [package.dependencies]
-langchain-core = "^0.3.15"
+langchain-core = "^0.3.0"

 [package.source]
 type = "directory"
@@ -1225,13 +1223,13 @@ url = "../text-splitters"

 [[package]]
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-version = "0.1.142"
+version = "0.1.137"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"

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 [[package]]
 name = "notebook-shim"
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+    {file = "watchdog-5.0.3.tar.gz", hash = "sha256:108f42a7f0345042a854d4d0ad0834b741d421330d5f575b81cb27b883500176"},
 ]

 [package.extras]
@@ -2892,15 +2903,19 @@ files = [

 [[package]]
 name = "webcolors"
-version = "24.11.1"
+version = "24.8.0"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
-    {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
+    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
+    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
 ]

+[package.extras]
+docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
+tests = ["coverage[toml]"]
+
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -2941,13 +2956,13 @@ files = [

 [[package]]
 name = "zipp"
-version = "3.21.0"
+version = "3.20.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
-    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
 ]

 [package.extras]
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-core"
-version = "0.3.18"
+version = "0.3.15"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -84,20 +84,17 @@ classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_ini
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.5"

-
 [tool.poetry.group.typing.dependencies]
 mypy = ">=1.10,<1.11"
 types-pyyaml = "^6.0.12.2"
 types-requests = "^2.28.11.5"
 types-jinja2 = "^2.11.9"

-
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
 setuptools = "^67.6.1"
 grandalf = "^0.8"

-
 [tool.poetry.group.test.dependencies]
 pytest = "^8"
 freezegun = "^1.2.2"
@@ -116,15 +113,12 @@ python = "<3.12"
 version = "^1.26.0"
 python = ">=3.12"

-
 [tool.poetry.group.test_integration.dependencies]

-
 [tool.poetry.group.typing.dependencies.langchain-text-splitters]
 path = "../text-splitters"
 develop = true

-
 [tool.poetry.group.test.dependencies.langchain-standard-tests]
 path = "../standard-tests"
 develop = true
--- a/libs/core/tests/unit_tests/indexing/test_public_api.py
+++ b/libs/core/tests/unit_tests/indexing/test_public_api.py
@@ -6,7 +6,6 @@ def test_all() -> None:
    assert __all__ == sorted(__all__, key=str.lower)
    assert set(__all__) == {
        "aindex",
-        "DeleteResponse",
        "DocumentIndex",
        "index",
        "IndexingResult",
--- a/libs/core/tests/unit_tests/runnables/snapshots/test_runnable.ambr
+++ b/libs/core/tests/unit_tests/runnables/snapshots/test_runnable.ambr
--- a/libs/partners/anthropic/langchain_anthropic/chat_models.py
+++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py
@@ -1128,52 +1128,7 @@ class ChatAnthropic(BaseChatModel):
            tools: If provided, sequence of dict, BaseModel, function, or BaseTools
                to be converted to tool schemas.

-        Basic usage:
-            .. code-block:: python
-
-                from langchain_anthropic import ChatAnthropic
-                from langchain_core.messages import HumanMessage, SystemMessage
-
-                llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
-
-                messages = [
-                    SystemMessage(content="You are a scientist"),
-                    HumanMessage(content="Hello, Claude"),
-                ]
-                llm.get_num_tokens_from_messages(messages)
-
-            .. code-block:: none
-
-                14
-
-        Pass tool schemas:
-            .. code-block:: python
-
-                from langchain_anthropic import ChatAnthropic
-                from langchain_core.messages import HumanMessage
-                from langchain_core.tools import tool
-
-                llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
-
-                @tool(parse_docstring=True)
-                def get_weather(location: str) -> str:
-                    \"\"\"Get the current weather in a given location
-
-                    Args:
-                        location: The city and state, e.g. San Francisco, CA
-                    \"\"\"
-                    return "Sunny"
-
-                messages = [
-                    HumanMessage(content="What's the weather like in San Francisco?"),
-                ]
-                llm.get_num_tokens_from_messages(messages, tools=[get_weather])
-
-            .. code-block:: none
-
-                403
-
-        .. versionchanged:: 0.3.0
+        .. versionchanged:: 0.2.5

                Uses Anthropic's token counting API to count tokens in messages. See:
                https://docs.anthropic.com/en/docs/build-with-claude/token-counting
--- a/libs/partners/anthropic/langchain_anthropic/llms.py
+++ b/libs/partners/anthropic/langchain_anthropic/llms.py
@@ -376,7 +376,7 @@ class AnthropicLLM(LLM, _AnthropicCommon):
        """Calculate number of tokens."""
        raise NotImplementedError(
            "Anthropic's legacy count_tokens method was removed in anthropic 0.39.0 "
-            "and langchain-anthropic 0.3.0. Please use "
+            "and langchain-anthropic 0.2.5. Please use "
            "ChatAnthropic.get_num_tokens_from_messages instead."
        )

--- a/libs/partners/anthropic/poetry.lock
+++ b/libs/partners/anthropic/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.

 [[package]]
 name = "annotated-types"
@@ -451,7 +451,7 @@ files = [

 [[package]]
 name = "langchain-core"
-version = "0.3.17"
+version = "0.3.15"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -1140,4 +1140,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "c646c08244b1b08c334f6ba7baca5e6fa78d801eae4f957f2770a190c3b9f2ac"
+content-hash = "e419b3bdf2ccff2715d43d8f64a0bf08e84d3cb748524bb6954af07628b86bbb"
--- a/libs/partners/anthropic/pyproject.toml
+++ b/libs/partners/anthropic/pyproject.toml
@@ -21,7 +21,7 @@ disallow_untyped_defs = "True"
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 anthropic = ">=0.39.0,<1"
-langchain-core = "^0.3.17"
+langchain-core = "^0.3.15"
 pydantic = "^2.7.4"

 [tool.ruff.lint]
--- a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
+++ b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
@@ -336,6 +336,7 @@ def test_anthropic_multimodal() -> None:
    assert isinstance(response.content, str)
    num_tokens = chat.get_num_tokens_from_messages(messages)
    assert num_tokens > 0
+    import pdb; pdb.set_trace()


 def test_streaming() -> None:
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -911,9 +911,7 @@ class BaseChatOpenAI(BaseChatModel):
        """
        # TODO: Count bound tools as part of input.
        if tools is not None:
-            warnings.warn(
-                "Counting tokens in tool schemas is not yet supported. Ignoring tools."
-            )
+            warnings.warn("Counting tokens in tool schemas is not yet supported.")
        if sys.version_info[1] <= 7:
            return super().get_num_tokens_from_messages(messages)
        model, encoding = self._get_encoding_model()
@@ -993,7 +991,7 @@ class BaseChatOpenAI(BaseChatModel):
            # TODO: Add support for streaming with Pydantic response_format.
            warnings.warn("Streaming with Pydantic response_format not yet supported.")
            return False
-        if self.model_name is not None and self.model_name.startswith("o1"):
+        if self.model_name.startswith("o1"):
            # TODO: Add support for streaming with o1 once supported.
            return False

--- a/libs/partners/openai/poetry.lock
+++ b/libs/partners/openai/poetry.lock
@@ -386,84 +386,84 @@ files = [

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 ]

 [[package]]
@@ -1448,24 +1448,24 @@ blobfile = ["blobfile (>=2)"]

 [[package]]
 name = "tomli"
-version = "2.1.0"
+version = "2.0.2"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]

 [[package]]
 name = "tqdm"
-version = "4.67.0"
+version = "4.66.5"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]

 [package.dependencies]
@@ -1473,7 +1473,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}

 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
-discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -1519,41 +1518,41 @@ zstd = ["zstandard (>=0.18.0)"]

 [[package]]
 name = "watchdog"
-version = "6.0.0"
+version = "5.0.3"
 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "watchdog-5.0.3.tar.gz", hash = "sha256:108f42a7f0345042a854d4d0ad0834b741d421330d5f575b81cb27b883500176"},
 ]

 [package.extras]
@@ -1562,4 +1561,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "193b209048042991c70a06289c41e67d44cc104c970a70857beacc8a51f0459b"
+content-hash = "77af861c052decd1c194936575c15491a606fd763556b427e2c659d5ea7aae72"
--- a/libs/partners/openai/pyproject.toml
+++ b/libs/partners/openai/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "langchain-openai"
-version = "0.2.8"
+version = "0.2.6"
 description = "An integration package connecting OpenAI and LangChain"
 authors = []
 readme = "README.md"
@@ -23,7 +23,7 @@ ignore_missing_imports = true

 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = "^0.3.17"
+langchain-core = "^0.3.15"
 openai = "^1.54.0"
 tiktoken = ">=0.7,<1"

--- a/libs/partners/xai/LICENSE
+++ b/libs/partners/xai/LICENSE
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2024 LangChain, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
--- a/libs/partners/xai/Makefile
+++ b/libs/partners/xai/Makefile
@@ -1,56 +0,0 @@
-.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
-
-# Default target executed when no arguments are given to make.
-all: help
-
-# Define a variable for the test file path.
-TEST_FILE ?= tests/unit_tests/
-
-integration_test integration_tests: TEST_FILE=tests/integration_tests/
-
-test tests integration_test integration_tests:
-	poetry run pytest $(TEST_FILE)
-
-######################
-# LINTING AND FORMATTING
-######################
-
-# Define a variable for Python and notebook files.
-PYTHON_FILES=.
-MYPY_CACHE=.mypy_cache
-lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/xai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
-lint_package: PYTHON_FILES=langchain_xai
-lint_tests: PYTHON_FILES=tests
-lint_tests: MYPY_CACHE=.mypy_cache_test
-
-lint lint_diff lint_package lint_tests:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
-
-format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
-
-spell_check:
-	poetry run codespell --toml pyproject.toml
-
-spell_fix:
-	poetry run codespell --toml pyproject.toml -w
-
-check_imports: $(shell find langchain_xai -name '*.py')
-	poetry run python ./scripts/check_imports.py $^
-
-######################
-# HELP
-######################
-
-help:
-	@echo '----'
-	@echo 'check_imports				- check imports'
-	@echo 'format                       - run code formatters'
-	@echo 'lint                         - run linters'
-	@echo 'test                         - run unit tests'
-	@echo 'tests                        - run unit tests'
-	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
--- a/libs/partners/xai/README.md
+++ b/libs/partners/xai/README.md
@@ -1,17 +0,0 @@
-# langchain-xai
-
-This package contains the LangChain integrations for [xAI](https://x.ai/) through their [APIs](https://console.x.ai).
-
-## Installation and Setup
-
- Install the LangChain partner package
-
-```bash
-pip install -U langchain-xai
-```
-
- Get your xAI api key from the [xAI Dashboard](https://console.x.ai) and set it as an environment variable (`XAI_API_KEY`)
-
-## Chat Completions
-
-This package contains the `ChatXAI` class, which is the recommended way to interface with xAI chat models.
--- a/libs/partners/xai/langchain_xai/init.py
+++ b/libs/partners/xai/langchain_xai/init.py
@@ -1,5 +0,0 @@
-"""This package provides the xAI integration for LangChain."""
-
-from langchain_xai.chat_models import ChatXAI
-
-__all__ = ["ChatXAI"]
--- a/libs/partners/xai/langchain_xai/chat_models.py
+++ b/libs/partners/xai/langchain_xai/chat_models.py
@@ -1,355 +0,0 @@
-"""Wrapper around xAI's Chat Completions API."""
-
-from typing import (
-    Any,
-    Dict,
-    List,
-    Optional,
-)
-
-import openai
-from langchain_core.language_models.chat_models import LangSmithParams
-from langchain_core.utils import secret_from_env
-from langchain_openai.chat_models.base import BaseChatOpenAI
-from pydantic import ConfigDict, Field, SecretStr, model_validator
-from typing_extensions import Self
-
-
-class ChatXAI(BaseChatOpenAI):  # type: ignore[override]
-    r"""ChatXAI chat model.
-
-    Setup:
-        Install ``langchain-xai`` and set environment variable ``XAI_API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U langchain-xai
-            export XAI_API_KEY="your-api-key"
-
-
-    Key init args — completion params:
-        model: str
-            Name of model to use.
-        temperature: float
-            Sampling temperature.
-        max_tokens: Optional[int]
-            Max number of tokens to generate.
-        logprobs: Optional[bool]
-            Whether to return logprobs.
-
-    Key init args — client params:
-        timeout: Union[float, Tuple[float, float], Any, None]
-            Timeout for requests.
-        max_retries: int
-            Max number of retries.
-        api_key: Optional[str]
-            xAI API key. If not passed in will be read from env var `XAI_API_KEY`.
-
-    Instantiate:
-        .. code-block:: python
-
-            from langchain_xai import ChatXAI
-
-            llm = ChatXAI(
-                model="grok-beta",
-                temperature=0,
-                max_tokens=None,
-                timeout=None,
-                max_retries=2,
-                # api_key="...",
-                # other params...
-            )
-
-    Invoke:
-        .. code-block:: python
-
-            messages = [
-                (
-                    "system",
-                    "You are a helpful translator. Translate the user sentence to French.",
-                ),
-                ("human", "I love programming."),
-            ]
-            llm.invoke(messages)
-
-        .. code-block:: python
-
-            AIMessage(
-                content="J'adore la programmation.",
-                response_metadata={
-                    'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41},
-                    'model_name': 'grok-beta',
-                    'system_fingerprint': None,
-                    'finish_reason': 'stop',
-                    'logprobs': None
-                },
-                id='run-168dceca-3b8b-4283-94e3-4c739dbc1525-0',
-                usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})
-
-    Stream:
-        .. code-block:: python
-
-            for chunk in llm.stream(messages):
-                print(chunk)
-
-        .. code-block:: python
-
-            content='J' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content="'" id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content='ad' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content='ore' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content=' la' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content=' programm' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content='ation' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content='.' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-            content='' response_metadata={'finish_reason': 'stop', 'model_name': 'grok-beta'} id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
-
-
-    Async:
-        .. code-block:: python
-
-            await llm.ainvoke(messages)
-
-            # stream:
-            # async for chunk in (await llm.astream(messages))
-
-            # batch:
-            # await llm.abatch([messages])
-
-        .. code-block:: python
-
-            AIMessage(
-                content="J'adore la programmation.",
-                response_metadata={
-                    'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41},
-                    'model_name': 'grok-beta',
-                    'system_fingerprint': None,
-                    'finish_reason': 'stop',
-                    'logprobs': None
-                },
-                id='run-09371a11-7f72-4c53-8e7c-9de5c238b34c-0',
-                usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})
-
-    Tool calling:
-        .. code-block:: python
-
-            from pydantic import BaseModel, Field
-
-            llm = ChatXAI(model="grok-beta")
-
-            class GetWeather(BaseModel):
-                '''Get the current weather in a given location'''
-
-                location: str = Field(
-                    ..., description="The city and state, e.g. San Francisco, CA"
-                )
-
-            class GetPopulation(BaseModel):
-                '''Get the current population in a given location'''
-
-                location: str = Field(
-                    ..., description="The city and state, e.g. San Francisco, CA"
-                )
-
-            llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
-            ai_msg = llm_with_tools.invoke(
-                "Which city is bigger: LA or NY?"
-            )
-            ai_msg.tool_calls
-
-
-        .. code-block:: python
-
-            [
-                {
-                    'name': 'GetPopulation',
-                    'args': {'location': 'NY'},
-                    'id': 'call_m5tstyn2004pre9bfuxvom8x',
-                    'type': 'tool_call'
-                },
-                {
-                    'name': 'GetPopulation',
-                    'args': {'location': 'LA'},
-                    'id': 'call_0vjgq455gq1av5sp9eb1pw6a',
-                    'type': 'tool_call'
-                }
-            ]
-
-    Structured output:
-        .. code-block:: python
-
-            from typing import Optional
-
-            from pydantic import BaseModel, Field
-
-
-            class Joke(BaseModel):
-                '''Joke to tell user.'''
-
-                setup: str = Field(description="The setup of the joke")
-                punchline: str = Field(description="The punchline to the joke")
-                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
-
-
-            structured_llm = llm.with_structured_output(Joke)
-            structured_llm.invoke("Tell me a joke about cats")
-
-        .. code-block:: python
-
-            Joke(
-                setup='Why was the cat sitting on the computer?',
-                punchline='To keep an eye on the mouse!',
-                rating=7
-            )
-
-    Token usage:
-        .. code-block:: python
-
-            ai_msg = llm.invoke(messages)
-            ai_msg.usage_metadata
-
-        .. code-block:: python
-
-            {'input_tokens': 37, 'output_tokens': 6, 'total_tokens': 43}
-
-    Logprobs:
-        .. code-block:: python
-
-            logprobs_llm = llm.bind(logprobs=True)
-            messages=[("human","Say Hello World! Do not return anything else.")]
-            ai_msg = logprobs_llm.invoke(messages)
-            ai_msg.response_metadata["logprobs"]
-
-        .. code-block:: python
-
-            {
-                'content': None,
-                'token_ids': [22557, 3304, 28808, 2],
-                'tokens': [' Hello', ' World', '!', '</s>'],
-                'token_logprobs': [-4.7683716e-06, -5.9604645e-07, 0, -0.057373047]
-            }
-
-
-    Response metadata
-        .. code-block:: python
-
-            ai_msg = llm.invoke(messages)
-            ai_msg.response_metadata
-
-        .. code-block:: python
-
-            {
-                'token_usage': {
-                    'completion_tokens': 4,
-                    'prompt_tokens': 19,
-                    'total_tokens': 23
-                    },
-                'model_name': 'grok-beta',
-                'system_fingerprint': None,
-                'finish_reason': 'stop',
-                'logprobs': None
-            }
-
-    """  # noqa: E501
-
-    model_name: str = Field(alias="model")
-    """Model name to use."""
-    xai_api_key: Optional[SecretStr] = Field(
-        alias="api_key",
-        default_factory=secret_from_env("XAI_API_KEY", default=None),
-    )
-    """xAI API key.
-
-    Automatically read from env variable `XAI_API_KEY` if not provided.
-    """
-    xai_api_base: str = Field(default="https://api.x.ai/v1/")
-    """Base URL path for API requests."""
-
-    openai_api_key: Optional[SecretStr] = None
-    openai_api_base: Optional[str] = None
-
-    model_config = ConfigDict(
-        populate_by_name=True,
-    )
-
-    @property
-    def lc_secrets(self) -> Dict[str, str]:
-        """A map of constructor argument names to secret ids.
-
-        For example,
-            {"xai_api_key": "XAI_API_KEY"}
-        """
-        return {"xai_api_key": "XAI_API_KEY"}
-
-    @classmethod
-    def get_lc_namespace(cls) -> List[str]:
-        """Get the namespace of the langchain object."""
-        return ["langchain_xai", "chat_models"]
-
-    @property
-    def lc_attributes(self) -> Dict[str, Any]:
-        """List of attribute names that should be included in the serialized kwargs.
-
-        These attributes must be accepted by the constructor.
-        """
-        attributes: Dict[str, Any] = {}
-
-        if self.xai_api_base:
-            attributes["xai_api_base"] = self.xai_api_base
-
-        return attributes
-
-    @classmethod
-    def is_lc_serializable(cls) -> bool:
-        """Return whether this model can be serialized by Langchain."""
-        return True
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of chat model."""
-        return "xai-chat"
-
-    def _get_ls_params(
-        self, stop: Optional[List[str]] = None, **kwargs: Any
-    ) -> LangSmithParams:
-        """Get the parameters used to invoke the model."""
-        params = super()._get_ls_params(stop=stop, **kwargs)
-        params["ls_provider"] = "xai"
-        return params
-
-    @model_validator(mode="after")
-    def validate_environment(self) -> Self:
-        """Validate that api key and python package exists in environment."""
-        if self.n < 1:
-            raise ValueError("n must be at least 1.")
-        if self.n > 1 and self.streaming:
-            raise ValueError("n must be 1 when streaming.")
-
-        client_params: dict = {
-            "api_key": (
-                self.xai_api_key.get_secret_value() if self.xai_api_key else None
-            ),
-            "base_url": self.xai_api_base,
-            "timeout": self.request_timeout,
-            "max_retries": self.max_retries,
-            "default_headers": self.default_headers,
-            "default_query": self.default_query,
-        }
-
-        if client_params["api_key"] is None:
-            raise ValueError(
-                "xAI API key is not set. Please set it in the `xai_api_key` field or "
-                "in the `XAI_API_KEY` environment variable."
-            )
-
-        if not (self.client or None):
-            sync_specific: dict = {"http_client": self.http_client}
-            self.client = openai.OpenAI(
-                **client_params, **sync_specific
-            ).chat.completions
-        if not (self.async_client or None):
-            async_specific: dict = {"http_client": self.http_async_client}
-            self.async_client = openai.AsyncOpenAI(
-                **client_params, **async_specific
-            ).chat.completions
-        return self
--- a/libs/partners/xai/langchain_xai/py.typed
+++ b/libs/partners/xai/langchain_xai/py.typed
--- a/libs/partners/xai/poetry.lock
+++ b/libs/partners/xai/poetry.lock
--- a/libs/partners/xai/pyproject.toml
+++ b/libs/partners/xai/pyproject.toml
@@ -1,100 +0,0 @@
-[build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.poetry]
-name = "langchain-xai"
-version = "0.1.0"
-description = "An integration package connecting xAI and LangChain"
-authors = []
-readme = "README.md"
-repository = "https://github.com/langchain-ai/langchain"
-license = "MIT"
-
-[tool.mypy]
-disallow_untyped_defs = "True"
-
-[tool.poetry.urls]
-"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/xai"
-"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-xai%3D%3D0%22&expanded=true"
-
-[tool.poetry.dependencies]
-python = ">=3.9,<4.0"
-langchain-openai = ">=0.2,<0.3"
-langchain-core = ">=0.3.18,<0.4"
-requests = ">=2,<3"
-aiohttp = ">=3.9.1,<4"
-
-[tool.ruff.lint]
-select = ["E", "F", "I", "D"]
-
-[tool.coverage.run]
-omit = ["tests/*"]
-
-[tool.pytest.ini_options]
-addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
-markers = [
-    "requires: mark tests as requiring a specific library",
-    "asyncio: mark tests as requiring asyncio",
-    "compile: mark placeholder test used to compile integration tests without running them",
-]
-asyncio_mode = "auto"
-
-[tool.poetry.group.test]
-optional = true
-
-[tool.poetry.group.codespell]
-optional = true
-
-[tool.poetry.group.test_integration]
-optional = true
-
-[tool.poetry.group.lint]
-optional = true
-
-[tool.poetry.group.typing]
-optional = true
-
-[tool.poetry.group.dev]
-optional = true
-
-[tool.ruff.lint.pydocstyle]
-convention = "google"
-
-[tool.ruff.lint.per-file-ignores]
-"tests/**" = ["D"]
-
-[tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
-freezegun = "^1.2.2"
-pytest-mock = "^3.10.0"
-syrupy = "^4.0.2"
-pytest-watcher = "^0.3.4"
-pytest-asyncio = "^0.21.1"
-docarray = "^0.32.1"
-langchain-openai = { path = "../openai", develop = true }
-langchain-core = { path = "../../core", develop = true }
-langchain-standard-tests = { path = "../../standard-tests", develop = true }
-
-[tool.poetry.group.codespell.dependencies]
-codespell = "^2.2.0"
-
-[tool.poetry.group.test_integration.dependencies]
-[[tool.poetry.group.test_integration.dependencies.numpy]]
-version = "^1"
-python = "<3.12"
-
-[[tool.poetry.group.test_integration.dependencies.numpy]]
-version = "^1.26.0"
-python = ">=3.12"
-
-[tool.poetry.group.lint.dependencies]
-ruff = "^0.5"
-
-[tool.poetry.group.typing.dependencies]
-mypy = "^1.10"
-types-requests = "^2"
-langchain-core = { path = "../../core", develop = true }
-
-[tool.poetry.group.dev.dependencies]
-langchain-core = { path = "../../core", develop = true }
--- a/libs/partners/xai/scripts/check_imports.py
+++ b/libs/partners/xai/scripts/check_imports.py
@@ -1,19 +0,0 @@
-"""This module checks if the given python files can be imported without error."""
-
-import sys
-import traceback
-from importlib.machinery import SourceFileLoader
-
-if __name__ == "__main__":
-    files = sys.argv[1:]
-    has_failure = False
-    for file in files:
-        try:
-            SourceFileLoader("x", file).load_module()
-        except Exception:
-            has_failure = True
-            print(file)
-            traceback.print_exc()
-            print()
-
-    sys.exit(1 if has_failure else 0)
--- a/libs/partners/xai/scripts/lint_imports.sh
+++ b/libs/partners/xai/scripts/lint_imports.sh
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-set -eu
-
-# Initialize a variable to keep track of errors
-errors=0
-
-# make sure not importing from langchain or langchain_experimental
-git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
-git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
-
-# Decide on an exit status based on the errors
-if [ "$errors" -gt 0 ]; then
-    exit 1
-else
-    exit 0
-fi
--- a/libs/partners/xai/tests/init.py
+++ b/libs/partners/xai/tests/init.py
--- a/libs/partners/xai/tests/integration_tests/init.py
+++ b/libs/partners/xai/tests/integration_tests/init.py
--- a/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py
+++ b/libs/partners/xai/tests/integration_tests/test_chat_models_standard.py
@@ -1,52 +0,0 @@
-"""Standard LangChain interface tests"""
-
-from typing import Optional, Type
-
-import pytest  # type: ignore[import-not-found]
-from langchain_core.language_models import BaseChatModel
-from langchain_core.rate_limiters import InMemoryRateLimiter
-from langchain_standard_tests.integration_tests import (  # type: ignore[import-not-found]
-    ChatModelIntegrationTests,  # type: ignore[import-not-found]
-)
-
-from langchain_xai import ChatXAI
-
-# Initialize the rate limiter in global scope, so it can be re-used
-# across tests.
-rate_limiter = InMemoryRateLimiter(
-    requests_per_second=0.5,
-)
-
-
-class TestXAIStandard(ChatModelIntegrationTests):
-    @property
-    def chat_model_class(self) -> Type[BaseChatModel]:
-        return ChatXAI
-
-    @property
-    def chat_model_params(self) -> dict:
-        return {
-            "model": "grok-beta",
-            "rate_limiter": rate_limiter,
-        }
-
-    @property
-    def tool_choice_value(self) -> Optional[str]:
-        """Value to use for tool choice when used in tests."""
-        return "tool_name"
-
-    @pytest.mark.xfail(reason="Not yet supported.")
-    def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
-        super().test_usage_metadata_streaming(model)
-
-    @pytest.mark.xfail(reason="Can't handle AIMessage with empty content.")
-    def test_tool_message_error_status(self, model: BaseChatModel) -> None:
-        super().test_tool_message_error_status(model)
-
-    @pytest.mark.xfail(reason="Can't handle AIMessage with empty content.")
-    def test_structured_few_shot_examples(self, model: BaseChatModel) -> None:
-        super().test_structured_few_shot_examples(model)
-
-    @pytest.mark.xfail(reason="Can't handle AIMessage with empty content.")
-    def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None:
-        super().test_tool_message_histories_string_content(model)
--- a/libs/partners/xai/tests/integration_tests/test_compile.py
+++ b/libs/partners/xai/tests/integration_tests/test_compile.py
@@ -1,7 +0,0 @@
-import pytest  # type: ignore[import-not-found]
-
-
-@pytest.mark.compile
-def test_placeholder() -> None:
-    """Used for compiling integration tests without running any real tests."""
-    pass
--- a/libs/partners/xai/tests/unit_tests/init.py
+++ b/libs/partners/xai/tests/unit_tests/init.py
@@ -1,3 +0,0 @@
-import os
-
-os.environ["XAI_API_KEY"] = "test"
--- a/libs/partners/xai/tests/unit_tests/snapshots/test_chat_models_standard.ambr
+++ b/libs/partners/xai/tests/unit_tests/snapshots/test_chat_models_standard.ambr
@@ -1,31 +0,0 @@
-# serializer version: 1
-# name: TestXAIStandard.test_serdes[serialized]
-  dict({
-    'id': list([
-      'langchain_xai',
-      'chat_models',
-      'ChatXAI',
-    ]),
-    'kwargs': dict({
-      'max_retries': 2,
-      'max_tokens': 100,
-      'model_name': 'grok-beta',
-      'n': 1,
-      'request_timeout': 60.0,
-      'stop': list([
-      ]),
-      'temperature': 0.0,
-      'xai_api_base': 'https://api.x.ai/v1/',
-      'xai_api_key': dict({
-        'id': list([
-          'XAI_API_KEY',
-        ]),
-        'lc': 1,
-        'type': 'secret',
-      }),
-    }),
-    'lc': 1,
-    'name': 'ChatXAI',
-    'type': 'constructor',
-  })
-# ---
--- a/libs/partners/xai/tests/unit_tests/test_chat_models.py
+++ b/libs/partners/xai/tests/unit_tests/test_chat_models.py
@@ -1,129 +0,0 @@
-import json
-
-import pytest  # type: ignore[import-not-found]
-from langchain_core.messages import (
-    AIMessage,
-    FunctionMessage,
-    HumanMessage,
-    SystemMessage,
-    ToolMessage,
-)
-from langchain_openai.chat_models.base import (
-    _convert_dict_to_message,
-    _convert_message_to_dict,
-)
-
-from langchain_xai import ChatXAI
-
-
-def test_initialization() -> None:
-    """Test chat model initialization."""
-    ChatXAI(model="grok-beta")
-
-
-def test_xai_model_param() -> None:
-    llm = ChatXAI(model="foo")
-    assert llm.model_name == "foo"
-    llm = ChatXAI(model_name="foo")  # type: ignore[call-arg]
-    assert llm.model_name == "foo"
-    ls_params = llm._get_ls_params()
-    assert ls_params["ls_provider"] == "xai"
-
-
-def test_chat_xai_invalid_streaming_params() -> None:
-    """Test that streaming correctly invokes on_llm_new_token callback."""
-    with pytest.raises(ValueError):
-        ChatXAI(
-            model="grok-beta",
-            max_tokens=10,
-            streaming=True,
-            temperature=0,
-            n=5,
-        )
-
-
-def test_chat_xai_extra_kwargs() -> None:
-    """Test extra kwargs to chat xai."""
-    # Check that foo is saved in extra_kwargs.
-    llm = ChatXAI(model="grok-beta", foo=3, max_tokens=10)  # type: ignore[call-arg]
-    assert llm.max_tokens == 10
-    assert llm.model_kwargs == {"foo": 3}
-
-    # Test that if extra_kwargs are provided, they are added to it.
-    llm = ChatXAI(model="grok-beta", foo=3, model_kwargs={"bar": 2})  # type: ignore[call-arg]
-    assert llm.model_kwargs == {"foo": 3, "bar": 2}
-
-    # Test that if provided twice it errors
-    with pytest.raises(ValueError):
-        ChatXAI(model="grok-beta", foo=3, model_kwargs={"foo": 2})  # type: ignore[call-arg]
-
-
-def test_function_dict_to_message_function_message() -> None:
-    content = json.dumps({"result": "Example #1"})
-    name = "test_function"
-    result = _convert_dict_to_message(
-        {
-            "role": "function",
-            "name": name,
-            "content": content,
-        }
-    )
-    assert isinstance(result, FunctionMessage)
-    assert result.name == name
-    assert result.content == content
-
-
-def test_convert_dict_to_message_human() -> None:
-    message = {"role": "user", "content": "foo"}
-    result = _convert_dict_to_message(message)
-    expected_output = HumanMessage(content="foo")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
-
-
-def test__convert_dict_to_message_human_with_name() -> None:
-    message = {"role": "user", "content": "foo", "name": "test"}
-    result = _convert_dict_to_message(message)
-    expected_output = HumanMessage(content="foo", name="test")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
-
-
-def test_convert_dict_to_message_ai() -> None:
-    message = {"role": "assistant", "content": "foo"}
-    result = _convert_dict_to_message(message)
-    expected_output = AIMessage(content="foo")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
-
-
-def test_convert_dict_to_message_ai_with_name() -> None:
-    message = {"role": "assistant", "content": "foo", "name": "test"}
-    result = _convert_dict_to_message(message)
-    expected_output = AIMessage(content="foo", name="test")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
-
-
-def test_convert_dict_to_message_system() -> None:
-    message = {"role": "system", "content": "foo"}
-    result = _convert_dict_to_message(message)
-    expected_output = SystemMessage(content="foo")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
-
-
-def test_convert_dict_to_message_system_with_name() -> None:
-    message = {"role": "system", "content": "foo", "name": "test"}
-    result = _convert_dict_to_message(message)
-    expected_output = SystemMessage(content="foo", name="test")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
-
-
-def test_convert_dict_to_message_tool() -> None:
-    message = {"role": "tool", "content": "foo", "tool_call_id": "bar"}
-    result = _convert_dict_to_message(message)
-    expected_output = ToolMessage(content="foo", tool_call_id="bar")
-    assert result == expected_output
-    assert _convert_message_to_dict(expected_output) == message
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
ccurme	36f5bfb552	Merge branch 'cc/anthropic_313' into cc/tool_attr	2024-11-12 11:04:42 -05:00
Chester Curme	29ba5fd1f1	community	2024-11-12 10:29:56 -05:00
Chester Curme	62a5f993f2	openai	2024-11-12 10:29:51 -05:00
Chester Curme	db7b518308	update anthropic	2024-11-12 10:29:43 -05:00
Chester Curme	e4bfc84d6e	update signature in core	2024-11-12 10:29:34 -05:00
Chester Curme	a4857bf09b	update openai	2024-11-12 10:16:54 -05:00
Chester Curme	7de02c5997	update anthropic	2024-11-12 10:16:47 -05:00
Chester Curme	76971094ab	update community	2024-11-12 10:16:34 -05:00
Chester Curme	42042abd82	update core	2024-11-12 09:56:35 -05:00
Chester Curme	668e4c68ec	pass tools into get_num_tokens	2024-11-09 14:48:40 -05:00
Chester Curme	077199c5de	Merge branch 'cc/anthropic_313' into cc/tool_attr	2024-11-09 14:33:57 -05:00
Chester Curme	826040f8b8	Merge branch 'master' into cc/anthropic_313	2024-11-09 14:33:46 -05:00
Chester Curme	1bf1ab7986	fix TypedDict import	2024-11-06 10:13:48 -05:00
Chester Curme	c8d96ad346	update test	2024-11-06 10:02:00 -05:00
Chester Curme	8662fd8c7d	add formatted_tools field	2024-11-06 09:54:57 -05:00
Chester Curme	a72e9d14f0	bump to 0.3	2024-11-05 12:57:24 -05:00
Chester Curme	7f5c21dc0c	bump anthropic dep	2024-11-05 12:57:13 -05:00
Chester Curme	ffb26b3298	lint	2024-11-05 11:56:37 -05:00
Chester Curme	84b4ea2198	update extended test	2024-11-05 11:51:39 -05:00
Chester Curme	c54b676a04	update docstring	2024-11-05 11:36:57 -05:00
Chester Curme	a3350f4174	test with 3.13 in CI	2024-11-05 11:24:30 -05:00
Chester Curme	5fc4ec6bc9	lint	2024-11-05 11:17:44 -05:00
Chester Curme	caed4e4ce8	implement ChatAnthropic.get_num_tokens_from_messages	2024-11-05 11:09:07 -05:00
Chester Curme	ff2ef48b35	drop support for Anthropic.get_num_tokens	2024-11-05 11:08:05 -05:00
Chester Curme	b2e8df4cea	lock	2024-11-05 11:07:35 -05:00
				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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