x

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

@@ -22,7 +22,7 @@ LangChain provides a consistent interface for working with chat models from diff
 * 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](#cache) and more.
+* Additional features like standardized [token usage](/docs/concepts/messages/#aimessage), [rate limiting](#rate-limiting), [caching](#caching) and more.
 
 ## Integrations
 

docs/docs/concepts/index.mdx

@@ -54,7 +54,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - **[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.
+- **[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.
@@ -70,7 +70,7 @@ 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/#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.

docs/docs/concepts/lcel.mdx

@@ -20,7 +20,7 @@ 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/#optimized-parallel-execution-batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
+- **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).
 

docs/docs/concepts/runnables.mdx

@@ -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

docs/docs/concepts/why_langchain.mdx

@@ -102,7 +102,7 @@ See our video playlist on [LangSmith tracing and evaluations](https://youtube.co
 
 LangChain offers standard interfaces for components that are central to many AI applications, which offers a few specific advantages:
 - **Ease of swapping providers:** It allows you to swap out different component providers without having to change the underlying code.
-- **Advanced features:** It provides common methods for more advanced features, such as [streaming](/docs/concepts/runnables/#streaming) and [tool calling](/docs/concepts/tool_calling/).
+- **Advanced features:** It provides common methods for more advanced features, such as [streaming](/docs/concepts/streaming) and [tool calling](/docs/concepts/tool_calling/).
 
 [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/) makes it possible to orchestrate complex applications (e.g., [agents](/docs/concepts/agents/)) and provide features like including [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), or [memory](https://langchain-ai.github.io/langgraph/concepts/memory/).