Build a Full-Stack GenAI Project in 4 Hours (FastAPI, React, Supabase)
This tutorial provides an end-to-end guide for building a production-grade Retrieval Augmented Generation (RAG) application—a Document Copilot. The project uses SEC filings data and demonstrates the complete AI engineering workflow: from initial client brief analysis to setting up the full stack (FastAPI, React/TypeScript, Supabase Postgres with pgvector). Key phases covered include database schema design using SQLAlchemy/Alembic, implementing user authentication via Supabase Auth, building a front-end chat interface, and establishing a robust document ingestion pipeline that converts messy HTM files into structured Markdown chunks for vector embedding.
Key takeaways
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Full Stack GenAI Architecture
30:45
The system is designed as a mono repo using FastAPI (backend) and React/TypeScript (frontend), connected via Supabase Postgres, which utilizes the pgvector extension for efficient vector storage and retrieval.
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Data Ingestion Pipeline
3:44:09
Raw SEC filings (HTM format) are processed using Dockling to convert them into clean Markdown. This structured data is then chunked, embedded via OpenAI, and stored in the database for RAG retrieval.
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Database Management
The project utilizes SQLAlchemy and Alembic for defining models (Users, Documents, Chunks, Messages) and managing schema migrations, ensuring a structured development process.