FastAPI Project Setup

To set up a FastAPI backend for Retrieval-Augmented Generation, clone the repository, create a Python 3.11+ virtual environment, install dependencies, configure your .env file, initialize the database, and run the server with uvicorn.

The difference between launch-rag and launch-agentic-rag is project scope. Choose launch-rag for basic retrieval needs, while launch-agentic-rag suits advanced agentic workflows and tool integrations.

Yes, you need Supabase and OpenAI keys to bootstrap a FastAPI RAG project. The setup requires a Supabase database for pgvector integration and OpenAI API keys, with Anthropic keys as an optional addition.

Yes, you can use Docker to deploy a FastAPI RAG application. The project bootstrap includes Docker-ready scaffolding and API documentation to accelerate deployment and testing.

To configure environment variables for a FastAPI vector search project, copy the .env.example file to .env and populate it with your OpenAI, Supabase, and optional Anthropic credentials before initializing the database.

Python 3.11 or higher is required for FastAPI RAG project setup to ensure compatibility with the launch-rag and launch-agentic-rag dependencies and tooling.