AI Introduction

This guide collects resources for building AI applications with SerenDB Postgres. You'll find core concepts, starter applications, framework integrations, and deployment guides. Use these resources to build applications like RAG chatbots, semantic search engines, or custom AI tools.

Getting started

Learn the fundamentals of building AI applications with SerenDB:

AI concepts

pgvector extension

AI frameworks and integrations

Build AI applications faster with these popular frameworks, tools, and services:

LangChain

LlamaIndex

Semantic Kernel

Inngest

app.build

Starter applications

Hackable, fully-featured, pre-built starter apps to get you up and running:

AI chatbot (OpenAI + LllamIndex)

AI chatbot (OpenAI + LangChain)

RAG chatbot (OpenAI + LlamaIndex)

RAG chatbot (OpenAI + LangChain)

Semantic search (OpenAI + LlamaIndex)

Semantic search (OpenAI + LangChain)

Hybrid search (OpenAI)

Reverse image search (OpenAI + LlamaIndex)

Chat with PDF (OpenAI + LlamaIndex)

Chat with PDF (OpenAI + LangChain)

Scale your AI application

Scale with SerenDB

Optimize vector search

Featured examples

Real-world AI applications built with SerenDB that you can reference as code examples or inspiration.

Share your AI app on our [#showcase](https://discord.gg/neon) channel on Discord.

AI vector database per tenant

Guide: Build a RAG chatbot

Guide: Build a Reverse Image Search Engine

Ask SerenDB Chatbot

Vercel Postgres pgvector Starter

YCombinator Semantic Search App

Web-based AI SQL Playground

Jupyter Notebook for vector search with SerenDB

Image search with SerenDB and Vertex AI

Text-to-SQL conversion with Mistral + LangChain

Postgres GPT Expert

Vector search tools and notebooks

Optimize your vector search implementation and experiment with different approaches:

Vector search optimization

Vector search notebooks

Google Colab guide

Azure Data Studio Notebooks