This directory provides code samples to help you get started with LangChain and AlloyDB.
| Sample | Description |
|---|---|
| Langchain Quick Start | This codelab demonstrates how to create a powerful interactive GenAI application using Retrieval Augmented Generation (RAG) powered by AlloyDB for PostgreSQL and LangChain. |
Get Started: AlloyDBVectorStore |
This notebook goes over how to use AlloyDB to store vector embeddings with the AlloyDBVectorStore class. |
Get Started: AlloyDBLoader |
This notebook goes over how to use AlloyDB to load Documents with the AlloyDBLoader class. |
Get Started: AlloyDBChatMessageHistory |
This notebook goes over how to use AlloyDB to store chat message history with the AlloyDBChatMessageHistory class. |
| How to Choose a Nearest-Neighbor Index Guide | This guide outlines different indexing strategies for approximate nearest neighbor (ANN) search. |
| Index Tuning Sample | This guide demonstrates how to fine-tune your LangChain PostgreSQL index for better vector similarity search results. |
| Langchain on VertexAI | This guide explains how to build and deploy LangChain apps to a managed Reasoning Engine runtime using LangChain on Vertex AI. |
| Migrate from PG vectorstore class to AlloyDB vectorstore class | This guide explains how to migrate your vector data from a PGVector-style database to an AlloyDB-style database for improved performance and manageability. |
| Migrate a Vector Store to AlloyDB | This guide provides step-by-step instructions on migrating data from existing vector stores to AlloyDB. |