Stop building basic RAG apps that fail in production. Learn how to combine BM25 Keyword Search with FAISS Vector Search and layer on a Cross-Encoder Reranker for the most accurate AI answers.
The Summary:
In this tutorial, we dive deep into building a professional Retrieval-Augmented Generation (RAG) system using FastAPI and Ollama. We don't just stop at vector search; we implement Hybrid Search and Reranking to ensure your LLM gets the absolute best context every single time.
Key Features Covered:
🚀 FastAPI Integration: Build a real-time API for document ingestion.
🔍 Hybrid Search: Combining BM25 (Sparse) and FAISS (Dense) retrieval.
🎯 Reranking: Using Cross-Encoders to re-score candidates for precision.
🧠 Local LLM: Running Phi-3 via Ollama for private, local generation.
SOCIAL SHARE CARD GENERATOR