FraiseQL Documentation

FraiseQL is a PostgreSQL-native GraphQL framework for Python. Build type-safe, production-ready APIs without boilerplate.

Getting Started

New to FraiseQL? Start here:

• 5-Minute Quickstart - Get running in minutes
• Installation - Setup instructions
• First Hour Guide - Learn the fundamentals
• Core Concepts - Essential mental models

Learn by Example

See FraiseQL in action:

• Blog API Tutorial - Build a complete API from scratch
• Filtering Examples - Query patterns and use cases
• RAG Tutorial - Build AI search with pgvector
• Error Handling Examples - Robust error management
• Production Deployment - Deploy safely

Core Features

FraiseQL provides everything you need for modern APIs:

pgvector Integration

Native PostgreSQL vector search for semantic search and RAG applications.

• Type-safe GraphQL integration with vector operators
• Query semantically similar documents with vector similarity
• Learn more →

GraphQL Cascade

Automatic, intelligent cache invalidation that works with your data relationships.

• Zero manual cache management
• Intelligent invalidation based on SQL relationships
• Learn more → | Best Practices →

LangChain Integration

Build AI-powered applications with document ingestion and semantic search.

• Production-ready patterns for RAG applications
• Seamless document embedding and vector storage
• Learn more →

LLM Integration

Use LLMs directly in your GraphQL resolvers.

• Type-safe LLM calling from Python
• Built-in streaming and error handling
• Learn more →

Guides

Common tasks and patterns:

• Decision Matrices - Choose the right patterns and architecture
• Filtering & Querying - Query syntax and patterns
• Mutations & Data Changes - Writing PostgreSQL functions
• Authentication - Securing your API
• Multi-Tenancy - Tenant isolation patterns
• Performance & Optimization - Make it fast
• Troubleshooting - Common issues and solutions

Reference

API documentation and configuration:

• Database API - Query execution and methods
• Types & Schema - Type system and schema definition
• Configuration - All configuration options
• Decorators - Python decorators reference
• CLI - Command-line tools
• Terminology Guide - Canonical term definitions and standards

Architecture

How FraiseQL works under the hood:

• Architecture Overview - System design
• Mutation Pipeline - How mutations execute
• Rust Pipeline - Performance optimizations
• Key Decisions - Design rationale

Deploy to Production

Get your API live:

• Deployment Guide - Deploying FraiseQL
• Monitoring - Track and debug
• Health Checks - Readiness and liveness
• Security - Secure your API
• Performance Tips - Optimize for production

Contributing

• Contributing Guide - How to contribute
• Development Style Guide - Code standards and best practices