AlloyDB for PostgreSQL for LangChain

• Client Library Documentation
• Product Documentation

The AlloyDB for PostgreSQL for LangChain package provides a first class experience for connecting to AlloyDB 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.
• Better integration with AlloyDB: built-in methods to take advantage of AlloyDB's advanced indexing and scalability capabilities.

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 AlloyDB API.
4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it's possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.9

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install langchain-google-alloydb-pg

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install langchain-google-alloydb-pg

Vector Store Usage

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

from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBVectorStore
from langchain_google_vertexai import VertexAIEmbeddings


engine = AlloyDBEngine.from_instance("project-id", "region", "my-cluster", "my-instance", "my-database")
embeddings_service = VertexAIEmbeddings(model_name="textembedding-gecko@003")
vectorstore = AlloyDBVectorStore.create_sync(
    engine,
    table_name="my-table",
    embedding_service=embeddings_service
)

See the full Vector Store tutorial.

Document Loader Usage

Use a document loader to load data as LangChain Documents.

from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBLoader


engine = AlloyDBEngine.from_instance("project-id", "region", "my-cluster", "my-instance", "my-database")
loader = AlloyDBLoader.create_sync(
    engine,
    table_name="my-table-name"
)
docs = loader.lazy_load()

See the full Document Loader tutorial.

Chat Message History Usage

Use ChatMessageHistory to store messages and provide conversation history to LLMs.

from langchain_google_alloydb_pg import AlloyDBChatMessageHistory, AlloyDBEngine


engine = AlloyDBEngine.from_instance("project-id", "region", "my-cluster", "my-instance", "my-database")
history = AlloyDBChatMessageHistory.create_sync(
    engine,
    table_name="my-message-store",
    session_id="my-session-id"
)

See the full Chat Message History tutorial.

Example Usage

Code examples can be found in the samples/ 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. This package uses a native async Postgres driver, asyncpg, to optimize Python's async functionality.

LangChain supports async 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 provide both async and sync versions of all methods.

asyncio is a Python library used for concurrent programming and is used as the foundation for multiple Python asynchronous frameworks. asyncio uses async / await syntax to achieve concurrency for non-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 AlloyDBEngine.afrom_instance("project-id", "region", "my-cluster", "my-instance", "my-database")
await engine.ainit_vectorstore_table(table_name="my-table", vector_size=768)
vectorstore = await AlloyDBVectorStore.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.

See CONTRIBUTING and DEVELOPER for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Code of Conduct for more information.

License

Apache 2.0 - See LICENSE for more information.

Disclaimer

This is not an officially supported Google product.