• 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.

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.

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.

Python >= 3.9

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

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

Code samples and snippets live in the samples/ folder.

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.

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.

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.

Contributions to this library are always welcome and highly encouraged.

See CONTRIBUTING 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.

Apache 2.0 - See LICENSE for more information.

This is not an officially supported Google product.