Build LLM-powered applications using LangChain

This page introduces how to build LLM-powered applications using LangChain. The overviews on this page link to procedure guides in GitHub.

What is LangChain?

LangChain is an LLM orchestration framework that helps developers build generative AI applications or retrieval-augmented generation (RAG) workflows. It provides the structure, tools, and components to streamline complex LLM workflows.

For more information about LangChain, see the Google LangChain page. For more information about the LangChain framework, see the LangChain product documentation.

LangChain components for Spanner

Spanner offers the following LangChain interfaces:

Learn how to use these components in an application with the LangChain Quickstart for Spanner.

Vector store for Spanner

Vector store retrieves and stores documents and metadata from a vector database. Vector store gives an application the ability to perform semantic searches that interpret the meaning of a user query. This type of search is a called a vector search, and it can find topics that match the query conceptually. At query time, vector store retrieves the embedding vectors that are most similar to the embedding of the search request. In LangChain, a vector store takes care of storing embedded data and performing the vector search for you.

To work with vector store in Spanner, use the SpannerVectorStore class.

For more information, see the LangChain Vector Stores product documentation.

Vector store procedure guide

The Spanner guide for vector store shows you how to do the following:

  • Install the integration package and LangChain
  • Initialize a table for the vector store
  • Set up an embedding service using VertexAIEmbeddings
  • Initialize SpannerVectorStore
  • Add and delete documents
  • Search for similar documents
  • Create a custom vector store to connect to a pre-existing Spanner database that has a table with vector embeddings

Document loader for Spanner

The document loader saves, loads, and deletes a LangChain Document objects. For example, you can load data for processing into embeddings and either store it in vector store or use it as a tool to provide specific context to chains.

To load documents from Spanner, use the SpannerLoader class. Use the SpannerDocumentSaver class to save and delete documents.

For more information, see the LangChain Document loaders topic.

Document loader procedure guide

The Spanner guide for document loader shows you how to do the following:

  • Install the integration package and LangChain
  • Load documents from a table
  • Add a filter to the loader
  • Customize the connection and authentication
  • Customize document construction by specifying customer content and metadata
  • How to use and customize a SpannerDocumentSaver to store and delete documents

Chat message history for Spanner

Question and answer applications require a history of the things said in the conversation to give the application context to answer further questions from the user. The LangChain ChatMessageHistory class lets the application save messages to a database and retrieve them when needed to formulate further answers. A message can be a question, an answer, a statement, a greeting or any other piece of text that the user or application gives during the conversation. ChatMessageHistory stores each message and chains messages together for each conversation.

Spanner extends this class with SpannerChatMessageHistory.

Chat message history procedure guide

The Spanner guide for chat message history shows you how to do the following:

  • Install LangChain and authenticate to Google Cloud
  • Initialize a table
  • Initialize the SpannerChatMessageHistory class to add and delete messages
  • Use a client to customize the connection and authentication
  • Delete the SpannerChatMessageHistory session