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@@ -8,82 +8,155 @@ 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.