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 Cloud SQL for SQL Server
Cloud SQL for SQL Server offers the following LangChain interfaces:
Learn how to use LangChain with the LangChain Quickstart for Cloud SQL for SQL Server.
Document loader for Cloud SQL for SQL Server
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 Cloud SQL for SQL Server, use the
MSSQLLoader
class. MSSQLLoader
methods return one or more documents from a
table. Use the MSSQLDocumentSaver
class to save and delete documents.
For more information, see the LangChain Document loaders topic.
Document loader procedure guide
The Cloud SQL for SQL Server 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
MSSQLDocumentSaver
to store and delete documents
Chat message history for Cloud SQL for SQL Server
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.
Cloud SQL for SQL Server extends this class with MSSQLChatMessageHistory
.
Chat message history procedure guide
The Cloud SQL for SQL Server guide for chat message history shows you how to do the following:
- Install LangChain and authenticate to Google Cloud
- Create a
MSSQLEngine
object and configure a connection pool to your Cloud SQL for SQL Server database - Initialize a table
- Initialize the
MSSQLChatMessageHistory
class to add and delete messages - Create a chain for message history using the LangChain Expression Language (LCEL) and Google's Vertex AI chat models