Building Context-Aware Search in Python with LLM Embeddings + Metadata

A step-by-step guide to building a context-aware semantic search engine in Python using sentence embeddings and metadata filtering. Uses the all-MiniLM-L6-v2 model locally (no API key needed) to generate 384-dimensional vectors, applies metadata pre-filtering by team, status, priority, and date before cosine similarity scoring, and persists the index to disk to avoid re-encoding on restarts. Three example queries demonstrate semantic-only search, filtered search, and cross-team priority-scoped search. Extensions covered include adding documents incrementally, multi-value filters, scaling with FAISS, and hybrid keyword+semantic scoring.