Build LLM-powered applications using LangChain

Preview — LangChain

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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 Bigtable

Bigtable offers the following LangChain interfaces:

• Document loader
• Chat message history

Learn how to use LangChain with the LangChain Quickstart for Bigtable. This quickstart creates an application that accesses a Netflix Movie dataset so that users can interact with movie data.

Document loader for Bigtable

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 document loader in Bigtable, use the BigtableLoader class. BigtableLoader methods return one or more documents from a table. Use the BigtableSaver class to save and delete documents.

For more information, see the LangChain Document loaders topic.

Document loader procedure guide

The Bigtable 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 BigtableSaver to store and delete documents

Chat message history for Bigtable

Question and answer applications require a history of the things said in the conversation to give the application context for answering further questions from the user. The LangChain ChatMessageHistory class lets the application save messages 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.

Bigtable extends this class with BigtableChatMessageHistory.

Chat message history procedure guide

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

• Install LangChain and authenticate to Google Cloud
• Initialize Bigtable schema
• Initialize the BigtableChatMessageHistory class to add and delete messages
• Use a client to customize the connection and authentication