In this article, you will learn how to build a context-aware semantic search engine in Python that combines embedding-based similarity with structured metadata filtering. Topics we will cover include: How sentence embeddings and cosine similarity work together to find semantically relevant documents. How to build a metadata-aware search index that filters by team, status, priority, and date before scoring candidates. How to persist the index to disk so embeddings are computed only once and reloaded efficiently on subsequent runs. Building Context-Aware Search in Python with LLM Embeddings + Metadata Introduction Keyword search breaks the moment a user types something a document doesn’t literally say. A support engineer searching for “login keeps failing” won’t find a ticket titled “OAuth2 token refresh race condition”, even though that’s exactly what they need. This is the core problem that context-aware semantic search aims to solve.…