• Client Library Documentation

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TheCloud SQL for PostgreSQL for LangChain package provides a first class experience for connecting toCloud SQL instances from the LangChain ecosystem while providing the following benefits:

• Simplified & Secure Connections: easily and securely create shared connection pools to connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.

• Improved performance & Simplified management: use a single-table schema can lead to faster query execution, especially for large collections.

• Improved metadata handling: store metadata in columns instead of JSON, resulting in significant performance improvements.

• Clear separation: clearly separate table and extension creation, allowing for distinct permissions and streamlined workflows.

Quick Start

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.

2. Enable billing for your project.

3. Enable the Cloud SQL Admin API.

4. Setup Authentication.

Installation

Install this library in a virtual environment usingvenv.venv is a tool thatcreates isolated Python environments. These isolated environments can have separateversions of Python packages, which allows you to isolate one project’s dependenciesfrom the dependencies of other projects.

Withvenv, it’s possible to install this library without needing systeminstall permissions, and without clashing with the installed systemdependencies.

Supported Python Versions

Python >= 3.9

Mac/Linux

pip install virtualenvvirtualenv <your-env>source <your-env>/bin/activate<your-env>/bin/pip install langchain-google-cloud-sql-pg

Windows

pip install virtualenvvirtualenv <your-env><your-env>\Scripts\activate<your-env>\Scripts\pip.exe install langchain-google-cloud-sql-pg

Example Usage

Code samples and snippets live in thesamples/ folder.

Vector Store Usage

Use a Vector Store to store embedded data and perform vector search.

from langchain_google_cloud_sql_pg import PostgresVectorstore, PostgresEnginefrom langchain.embeddings import VertexAIEmbeddingsengine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")engine.init_vectorstore_table(    table_name="my-table",    vector_size=768,  # Vector size for `VertexAIEmbeddings()`)embeddings_service = VertexAIEmbeddings(model_name="textembedding-gecko@003")vectorstore = PostgresVectorStore.create_sync(    engine,    table_name="my-table",    embeddings=embedding_service)

See the fullVector Store tutorial.

Document Loader Usage

Use a document loader to load data as Documents.

from langchain_google_cloud_sql_pg import PostgresEngine, PostgresLoaderengine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")loader = PostgresSQLLoader.create_sync(    engine,    table_name="my-table-name")docs = loader.lazy_load()

See the fullDocument Loader tutorial.

Chat Message History Usage

Use Chat Message History to store messages and provide conversation history to LLMs.

from langchain_google_cloud_sql_pg import PostgresChatMessageHistory, PostgresEngineengine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")engine.init_chat_history_table(table_name="my-message-store")history = PostgresChatMessageHistory.create_sync(    engine,    table_name="my-message-store",    session_id="my-session_id")

See the fullChat Message History tutorial.

Langgraph Checkpoint Usage

UsePostgresSaver to save snapshots of the graph state at a given point in time.

from langchain_google_cloud_sql_pg import PostgresSaver, PostgresEngineengine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")checkpoint = PostgresSaver.create_sync(engine)

See the fullCheckpoint tutorial.

Example Usage

Code examples can be found in thesamples/ folder.

Converting between Sync & Async Usage

Async functionality improves the speed and efficiency of database connections through concurrency,which is key for providing enterprise quality performance and scaling in GenAI applications. Thispackage uses a native async Postgres driver,asyncpg, to optimize Python’s async functionality.

LangChain supportsasync programming, since LLM based application utilize many I/O-bound operations,such as making API calls to language models, databases, or other services. All components should provideboth async and sync versions of all methods.

asyncio is a Python library used for concurrent programming and is used as the foundation for multiplePython asynchronous frameworks. asyncio uses async / await syntax to achieve concurrency fornon-blocking I/O-bound tasks using one thread with cooperative multitasking instead of multi-threading.

Converting Sync to Async

Update sync methods to await async methods

engine = await PostgresEngine.afrom_instance("project-id", "region", "my-instance", "my-database")await engine.ainit_vectorstore_table(table_name="my-table", vector_size=768)vectorstore = await PostgresVectorStore.create(   engine,   table_name="my-table",   embedding_service=VertexAIEmbeddings(model_name="textembedding-gecko@003"))

Run the code: notebooks

ipython and jupyter notebooks support the use of the await keyword without any additional setup

Run the code: FastAPI

Update routes to use async def.

@app.get("/invoke/")async def invoke(query: str):   return await retriever.ainvoke(query)

Run the code: Local python file

It is recommend to create a top-level async method definition: async def to wrap multiple async methods.Then use asyncio.run() to run the the top-level entrypoint, e.g. “main()”

async def main():   response = await retriever.ainvoke(query)   print(response)asyncio.run(main())

Contributions

Contributions to this library are always welcome and highly encouraged.

SeeCONTRIBUTING for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating inthis project you agree to abide by its terms. SeeCode of Conduct for moreinformation.

License

Apache 2.0 - SeeLICENSEfor more information.

Disclaimer

This is not an officially supported Google product.