> [!WARNING]
>
> **Preview
> --- LangChain**
>
>
> This feature is
>
> subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
> [Service Specific
> Terms](https://docs.cloud.google.com/terms/service-terms#1).
>
> Pre-GA features are available "as is" and might have limited support.
>
> For more information, see the
> [launch stage descriptions](https://cloud.google.com/products/#product-launch-stages).

This page introduces how to build LLM-powered applications using
[LangChain](https://www.langchain.com/). 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](https://python.langchain.com/docs/integrations/platforms/google)
page. For more information about the LangChain framework, see the [LangChain](https://python.langchain.com/docs/get_started/introduction)
product documentation.

## LangChain components for Cloud SQL for MySQL

Cloud SQL for MySQL offers the following LangChain interfaces:

- [Vector store](https://docs.cloud.google.com/sql/docs/mysql/langchain.md.txt#vector-store)
- [Document loader](https://docs.cloud.google.com/sql/docs/mysql/langchain.md.txt#document-loader)
- [Chat message history](https://docs.cloud.google.com/sql/docs/mysql/langchain.md.txt#chat-message-history)

Learn how to use LangChain with the
[LangChain Quickstart for Cloud SQL for MySQL](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/samples/langchain_quick_start.ipynb).

## Vector store for Cloud SQL for MySQL

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 Cloud SQL for MySQL, use the
`MySQLVectorStore` class.

For more information, see the [LangChain Vector
Stores](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
product documentation.

### Vector store procedure guide

The [Cloud SQL for MySQL guide for vector
store](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/docs/vector_store.ipynb)
shows you how to do the following:

- Install the integration package and LangChain
- Create a `MySQLEngine` object and configure a connection pool to your Cloud SQL for MySQL database
- Initialize a table
- Create an embedding object using `VertexAIEmbeddings`
- Initialize a default `MySQLVectorStore`
- Add texts
- Delete texts
- Search for documents
- Search for documents by vector
- Add an index to accelerate vector search queries
- Remove an index
- Create a custom vector store
- Search for documents with a metadata filter

## Document loader for Cloud SQL for MySQL

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 MySQL, use the
`MySQLLoader` class. `MySQLLoader` methods return one or more documents from a
table. Use the `MySQLDocumentSaver` class to save and delete documents.

For more information, see the [LangChain Document
loaders](https://python.langchain.com/docs/modules/data_connection/document_loaders/) topic.

### Document loader procedure guide

The [Cloud SQL for MySQL guide for document
loader](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/docs/document_loader.ipynb) 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 `MySQLDocumentSaver` to store and delete documents

## Chat message history for Cloud SQL for MySQL

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 MySQL extends this class with `MySQLChatMessageHistory`.

### Chat message history procedure guide

The [Cloud SQL for MySQL guide for chat message
history](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/docs/chat_message_history.ipynb) shows you how to do the following:

- Install LangChain and authenticate to Google Cloud
- Create a `MySQLEngine` object and configure a connection pool to your Cloud SQL for MySQL database
- Initialize a table
- Initialize the `MySQLChatMessageHistory` 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