docs: fix more links (#27809)

Fix more broken links

docs/docs/concepts/async.mdx

@@ -73,7 +73,7 @@ in certain scenarios.
 
 If you are experiencing issues with streaming, callbacks or tracing in async code and are using Python 3.9 or 3.10, this is a likely cause.
 
-Please read [Propagation RunnableConfig](/docs/concepts/runnables#propagation-RunnableConfig) for more details to learn how to propagate the `RunnableConfig` down the call chain manually (or upgrade to Python 3.11 where this is no longer an issue).
+Please read [Propagation RunnableConfig](/docs/concepts/runnables/#propagation-of-runnableconfig) for more details to learn how to propagate the `RunnableConfig` down the call chain manually (or upgrade to Python 3.11 where this is no longer an issue).
 
 ## How to use in ipython and jupyter notebooks
 

docs/docs/concepts/chat_history.mdx

@@ -24,7 +24,7 @@ So a full conversation often involves a combination of two patterns of alternati
 
 ## Managing chat history
 
-Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models#context_window).
+Since chat models have a maximum limit on input size, it's important to manage chat history and trim it as needed to avoid exceeding the [context window](/docs/concepts/chat_models/#context-window).
 
 While processing chat history, it's essential to preserve a correct conversation structure. 
 

docs/docs/concepts/chat_models.mdx

@@ -8,7 +8,7 @@ Modern LLMs are typically accessed through a chat model interface that takes a l
 
 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 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.
+* [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.
 
@@ -18,11 +18,11 @@ 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 [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) via the `with_structured_output` method.
-* Provides support for [async programming](/docs/concepts/async), [efficient batching](/docs/concepts/runnables#batch), [a rich streaming API](/docs/concepts/streaming).
+* 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#token_usage), [rate limiting](#rate-limiting), [caching](#cache) and more.
+* Additional features like standardized [token usage](/docs/concepts/messages/#aimessage), [rate limiting](#rate-limiting), [caching](#caching) and more.
 
 ## Integrations
 
@@ -44,7 +44,7 @@ Models that do **not** include the prefix "Chat" in their name or include "LLM"
 
 ## Interface
 
-LangChain chat models implement the [BaseChatModel](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface. Because [BaseChatModel] also implements the [Runnable Interface](/docs/concepts/runnables), chat models support a [standard streaming interface](/docs/concepts/streaming), [async programming](/docs/concepts/async), optimized [batching](/docs/concepts/runnables#batch), and more. Please see the [Runnable Interface](/docs/concepts/runnables) for more details.
+LangChain chat models implement the [BaseChatModel](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface. Because [BaseChatModel] also implements the [Runnable Interface](/docs/concepts/runnables), chat models support a [standard streaming interface](/docs/concepts/streaming), [async programming](/docs/concepts/async), optimized [batching](/docs/concepts/runnables/#optimized-parallel-execution-batch), and more. Please see the [Runnable Interface](/docs/concepts/runnables) for more details.
 
 Many of the key methods of chat models operate on [messages](/docs/concepts/messages) as input and return messages as output.
 
@@ -65,7 +65,7 @@ The key methods of a chat model are:
 2. **stream**: A method that allows you to stream the output of a chat model as it is generated.
 3. **batch**: A method that allows you to batch multiple requests to a chat model together for more efficient processing.
 4. **bind_tools**: A method that allows you to bind a tool to a chat model for use in the model's execution context.
-5. **with_structured_output**: A wrapper around the `invoke` method for models that natively support [structured output](/docs/concepts#structured_output).
+5. **with_structured_output**: A wrapper around the `invoke` method for models that natively support [structured output](/docs/concepts/structured_outputs).
 
 Other important methods can be found in the [BaseChatModel API Reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html).
 
@@ -104,13 +104,13 @@ ChatModels also accept other parameters that are specific to that integration. T
 ## Tool calling
 
 Chat models can call [tools](/docs/concepts/tools) to perform tasks such as fetching data from a database, making API requests, or running custom code. Please
-see the [tool calling](/docs/concepts#tool-calling) guide for more information.
+see the [tool calling](/docs/concepts/tool_calling) guide for more information.
 
 ## Structured outputs
 
 Chat models can be requested to respond in a particular format (e.g., JSON or matching a particular schema). This feature is extremely
 useful for information extraction tasks. Please read more about
-the technique in the [structured outputs](/docs/concepts#structured_output) guide.
+the technique in the [structured outputs](/docs/concepts/structured_outputs) guide.
 
 ## Multimodality
 
@@ -162,7 +162,7 @@ Please see the [how to cache chat model responses](/docs/how_to/chat_model_cachi
 ### Conceptual guides
 
 * [Messages](/docs/concepts/messages)
-* [Tool calling](/docs/concepts#tool-calling)
+* [Tool calling](/docs/concepts/tool_calling)
 * [Multimodality](/docs/concepts/multimodality)
-* [Structured outputs](/docs/concepts#structured_output)
+* [Structured outputs](/docs/concepts/structured_outputs)
 * [Tokens](/docs/concepts/tokens)

docs/docs/concepts/index.mdx

@@ -45,22 +45,22 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[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#basetool)**: The base class for all tools in LangChain.
+- **[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/chat_models#bind-tools)**: Allows models to interact with tools.
+- **[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#chat-models)**: Chat models that handle multiple data modalities.
-- **[Configurable runnables](/docs/concepts/runnables#configurable-Runnables)**: Creating configurable Runnables.
+- **[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#embedding-models)**: Models that generate vector embeddings for various data types.
+- **[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#partner-packages)**: Third-party packages that integrate with LangChain.
+- **[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.
@@ -70,20 +70,20 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[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-RunnableConfig)**: Propagating configuration through Runnables. Read if working with python 3.9, 3.10 and async.
+- **[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#remove-message)**: An abstraction used to remove a message from chat history, used primarily in LangGraph.
+- **[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`).
+- **[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#@tool)**: Decorator for creating tools in LangChain.
+- **[@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/chat_models#with-structured-output)**: 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_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.

docs/docs/concepts/lcel.mdx

@@ -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:
 
-- **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#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#async-api). 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:

docs/docs/concepts/runnables.mdx

@@ -46,7 +46,7 @@ The async versions of `abatch` and `abatch_as_completed` these rely on asyncio's
 :::
 
 :::tip
-When processing a large number of inputs using `batch` or `batch_as_completed`, users may want to control the maximum number of parallel calls. This can be done by setting the `max_concurrency` attribute in the `RunnableConfig` dictionary. See the [RunnableConfig](/docs/concepts/runnables#RunnableConfig) for more information.
+When processing a large number of inputs using `batch` or `batch_as_completed`, users may want to control the maximum number of parallel calls. This can be done by setting the `max_concurrency` attribute in the `RunnableConfig` dictionary. See the [RunnableConfig](/docs/concepts/runnables/#runnableconfig) for more information.
 
 Chat Models also have a built-in [rate limiter](/docs/concepts/chat_models#rate-limiting) that can be used to control the rate at which requests are made.
 :::
@@ -312,7 +312,7 @@ Please read the [Callbacks Conceptual Guide](/docs/concepts/callbacks) for more
 :::important
 If you're using Python 3.9 or 3.10 in an async environment, you must propagate
 the `RunnableConfig` manually to sub-calls in some cases. Please see the
-[Propagating RunnableConfig](#propagation-of-RunnableConfig) section for more information.
+[Propagating RunnableConfig](#propagation-of-runnableconfig) section for more information.
 :::
 
 ## Creating a runnable from a function

docs/docs/concepts/tools.mdx

@@ -160,7 +160,7 @@ The `config` will not be part of the tool's schema and will be injected at runti
 :::note
 You may need to access the `config` object to manually propagate it to subclass. This happens if you're working with python 3.9 / 3.10 in an [async](/docs/concepts/async) environment and need to manually propagate the `config` object to sub-calls.
 
-Please read [Propagation RunnableConfig](/docs/concepts/runnables#propagation-RunnableConfig) for more details to learn how to propagate the `RunnableConfig` down the call chain manually (or upgrade to Python 3.11 where this is no longer an issue).
+Please read [Propagation RunnableConfig](/docs/concepts/runnables/#propagation-of-runnableconfig) for more details to learn how to propagate the `RunnableConfig` down the call chain manually (or upgrade to Python 3.11 where this is no longer an issue).
 :::
 
 ### InjectedState