Tech Stack: Python 3.x , FastAPI , Pydantic , Uvicorn
The Problem
Traditional APIs return static data , but RAG systems combine semantic search with generative models to provide contextually accurate answers based on a collection of documents. This is useful when building:
- AI assistants
- Smart knowledge-based search systems
- Document Q&A backends
This project demonstrates how to set up such a system in Python.
Key Achievements
- FastAPI backend - high-performance, production-ready API using modern Python.
- Document collection handling - endpoints to upload and manage docs for retrieval.
- Vector-based retrieval logic - retrieve relevant context using vector similarity (semantic search).
- LLM integration (extendable) - foundation to plug in language models (OpenAI, LangChain, etc.) for generative responses.
- Modular and extensible structure - clear app layout (
app,data,tests, config) for easy growth and experiments.
- Basic test setup - test suite included for software quality and reliability.
Made with Bullet