RAG System Overview
A production-grade Retrieval-Augmented Generation system for document processing, semantic search, and AI-powered Q&A
RAG System
A production-grade Retrieval-Augmented Generation (RAG) system built with Python and FastAPI. It ingests documents from multiple sources, processes and embeds them into a vector store, and provides semantic search with LLM-powered question answering.
What It Does
The RAG system enables you to:
- Ingest documents from URLs (web scraping) or file uploads (PDF, DOCX, PPTX, HTML, images, CSV, XLSX)
- Process and chunk text with intelligent splitting, deduplication, and language detection
- Store embeddings in a vector db with metadata in PostgreSQL
- Search semantically using hybrid vector + keyword search with cross-encoder reranking
- Generate answers using an LLM grounded in your document knowledge base
Key Capabilities
| Capability | Details |
|---|---|
| Document Formats | PDF, DOCX, PPTX, HTML, Markdown, CSV, XLSX, PNG, JPG, TIFF |
| OCR Support | for scanned documents and images |
| Languages | 50+ languages with specialized Arabic/RTL support |
| Search | Hybrid search (vector + BM25) with RRF fusion |
| Reranking | Cross-encoder reranking |
| Storage | vector db + PostgreSQL (metadata) |
| API | FastAPI REST API with OpenAPI/Swagger docs |
| Async | Full async pipeline with background task support |