Building a Retrieval-Augmented Generation System Using FastAPI

In this project, we'll build a complete retrieval-augmented generation (RAG) system using FastAPI, OpenAI's API, and PostgreSQL to create an intelligent document query application. RAG systems enhance large language model responses by retrieving relevant information from uploaded documents, enabling the AI to answer questions grounded in our specific knowledge base. We'll develop REST API endpoints for document upload, text parsing, PDF processing with OCR, database storage, and AI-powered question answering with context retrieval.

We'll start by creating FastAPI endpoints for basic operations and integrating OpenAI's GPT models for natural language processing. We'll write API testing scripts to validate functionality, then implement robust document handling with support for multiple file formats. Using OCR technology, we'll extract text from PDFs and other documents, parse the content, and prepare it for vector embedding. Next, we'll design and create a PostgreSQL database to store document content and metadata, implement background tasks for asynchronous document processing, and integrate the database with the FastAPI application for efficient data retrieval.

By the end, we'll have a production-ready RAG application demonstrating FastAPI backend development, OpenAI API integration, document parsing techniques, OCR implementation, PostgreSQL database management, asynchronous task processing, and AI model integration applicable to any knowledge base or intelligent search system.