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 large language model (LLM) workflows. For more information about LLMs, their use cases, and the specific models and services that Google offers, see an overview of LLM concepts and services in Google Cloud.
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 AlloyDB
Learn how to use LangChain with the LangChain Quickstart for AlloyDB. This quickstart creates an application that accesses a Netflix Movie dataset so that users can interact with movie data.
Vector store for AlloyDB
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 AlloyDB, use the
AlloyDBVectorStore
class.
For more information, see the LangChain vector stores product documentation.
Vector store procedure guide
The AlloyDB guide for vector store shows you how to do the following:
- Install the integration package and LangChain
- Create an
AlloyDBEngine
object and configure a connection pool to your AlloyDB database - Initialize a table for the vector store
- Set up an embedding service using
VertexAIEmbeddings
- Initialize
AlloyDBVectorStore
- Add and delete documents
- Search for similar Documents
- Add a vector index to improve search performance
- Create a custom vector store to connect to a pre-existing AlloyDB for PostgreSQL database that has a table with vector embeddings
Document loader for AlloyDB
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 AlloyDB, use the
AlloyDBLoader
class. AlloyDBLoader
returns a list of documents from a table
using the first column for page content and all other columns for metadata. The
default table has the first column as page content and the second column as
JSON metadata. Each row becomes a document. Instructions for customizing these
settings are in the procedure guide.
Use the AlloyDBSaver
class to save and delete documents.
For more information, see the LangChain Document loaders topic.
Document loader procedure guide
The AlloyDB 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
AlloyDBSaver
to store and delete documents
Chat message history for AlloyDB
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 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.
AlloyDB extends this class with AlloyDBChatMessageHistory
.
Chat message history procedure guide
The AlloyDB guide for chat message history shows you how to do the following:
- Install the integration package and LangChain
- Create an
AlloyDBEngine
object and configure a connection pool to your AlloyDB database - Initialize a table
- Initialize the
AlloyDBChatMessageHistory
class to add and delete messages - Create a chain for message history using the LangChain Expression Language (LCEL)