AlloyDB AI use cases

This page describes some AI use cases for AlloyDB for PostgreSQL, with links to codelabs and tutorials that you can use to explore approaches or to help you develop your application.

Create a chatbot to answer questions about movies

This tutorial shows you how to build a generative AI chatbot that uses Gemini, Vertex AI, and the AlloyDB LangChain integration. You learn how to extract structured data from your database , generate embeddings, and format your data so that you can perform vector search in a Retrieval-Augmented Generation (RAG) application.

Use a movie database to ground your LLM with information about the most popular films. Grounding helps to ensure that LLM output is accurate and relevant.

Tutorial: AlloyDB LangChain integration

Deploy a RAG application with LangChain on Vertex AI

This tutorial shows you how to build and deploy an agent using the Vertex AI SDK for Python and the AlloyDB LangChain integration.

Learn how to use agents and vectors with LangChain to perform a similarity search and retrieve related data to ground LLM responses.

Tutorial: Deploying a RAG Application with AlloyDB to LangChain on Vertex AI

Use a similarity search using a vector index to find relevant products

This codelab shows you how to use AlloyDB AI features like model endpoint management and vector search to help you find relevant products.

Learn how to generate embeddings using model endpoint management on your database data and use your operational data to perform vector similarity searches. This tutorial uses a Vertex AI embedding model in AlloyDB and Vertex AI generative AI models.

Codelab: Getting started with Vector Embeddings with AlloyDB AI

Accelerate patent searches and search precision

This codelab shows you how to improve patent research by using vector search along with AlloyDB, the pgvector extension, embeddings, and Gemini 1.5 Pro.

Codelab: Build a Patent Search App with AlloyDB, Vector Search & Vertex AI

Build and deploy a personalized fashion styling assistant

The following codelabs show you how to build and deploy a personalized style assistant with Gemini, model endpoint management, vector search, Vertex AI, and agents.