A state-of-the-art Retrieval-Augmented Generation (RAG) system leveraging modern generative AI and vector search technologies. This project demonstrates how to build a production-grade system that enables advanced question answering, document search, and contextual generation on your own infrastructure—private, scalable, and fast.
Project Overview
This project showcases how to combine large language models (LLMs), local vector databases, and a modern Python web API for secure, high-performance knowledge and document retrieval. All LLM operations run locally—no data leaves your machine.
Ideal for applications in internal research, enterprise QA, knowledge management, or compliance-sensitive AI tasks.
Features
- Local LLM Inference: Runs entirely on your machine using Ollama and open-source models (e.g., Llama 3.1).
- Vector Database Search: Uses Qdrant for fast, scalable semantic retrieval.
- Flexible Document Ingestion: Upload PDF, DOCX, or TXT files for indexing and search.
- FastAPI Back End: High-concurrency, type-safe REST API with automatic documentation.
- Modern Python Package Management: Built with
uvfor blazing-fast dependency resolution. - Modular, Extensible Codebase: Clean architecture, easy to extend and maintain.
- Privacy and Security: No cloud calls—ideal for regulated sectors.
- Fully Containerizable: Easily deploy with Docker.