Hybrid Semantic Search Engine Keyword search

Hybrid Semantic Search Engine Keyword search misses results that mean the same thing in different words. Pure vector search misses exact matches.

I built a hybrid search engine that combines both. Text is embedded with the all-MiniLM-L6-v2 sentence model and indexed in FAISS, and each query blends vector similarity with fuzzy text matching (weighted 70/30), so results match on meaning and on exact terms.

Built as a FastAPI backend with a React frontend, packaged with Docker so it runs with one command.

What this shows: I understand how retrieval works under the hood, the same retrieval that powers RAG chatbots, and where it fails.