Documentation Index
Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-srimpr-1771619406-31dcf4f.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Integration packages are Python packages that users can install for use in their projects. They implement one or more components that adhere to the LangChain interface standards.
LangChain components are subclasses of base classes in langchain-core. Examples include chat models, tools, retrievers, and more.
Your integration package will typically implement a subclass of at least one of these components. Expand the tabs below to see details on each.
Chat Models
Tools
Retrievers
Vector Stores
Embeddings
Chat models are subclasses of the BaseChatModel class. They implement methods for generating chat completions, handling message formatting, and managing model parameters.The chat model integration guide is currently WIP. In the meantime, read the chat model conceptual guide for details on how LangChain chat models function. Tools are used in 2 main ways:
- To define an “input schema” or “args schema” to pass to a chat model’s tool calling feature along with a text request, such that the chat model can generate a “tool call”, or parameters to call the tool with.
- To take a “tool call” as generated above, and take some action and return a response that can be passed back to the chat model as a ToolMessage.
The Tools class must inherit from the BaseTool base class. This interface has 3 properties and 2 methods that should be implemented in a subclass.The tools integration guide is currently WIP. In the meantime, read the tools conceptual guide for details on how LangChain tools function. Retrievers are used to retrieve documents from APIs, databases, or other sources based on a query. The Retriever class must inherit from the BaseRetriever base class.The retriever integration guide is currently WIP. In the meantime, read the retriever conceptual guide for details on how LangChain retrievers function. All vector stores must inherit from the VectorStore base class. This interface consists of methods for writing, deleting and searching for documents in the vector store.See the vector store integration guide for details on implementing a vector store integration.The vector store integration guide is currently WIP. In the meantime, read the vector store conceptual guide for details on how LangChain vector stores function. Embedding models are subclasses of the Embeddings class.The embedding model integration guide is currently WIP. In the meantime, read the embedding model conceptual guide for details on how LangChain embedding models function.
