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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Indexing, Filtering & Hybrid Search

Make vector search fast and precise at scale by understanding index types, combining metadata filters with similarity, and blending keyword and vector retrieval with hybrid search.

The Scale Problem

Comparing a query against millions of vectors one by one is too slow for production. Vector databases use indexes to find near neighbors quickly without scanning everything.

Approximate Nearest Neighbors

Most vector indexes are approximate (ANN): they trade a tiny bit of accuracy for huge speed gains. For RAG, near-perfect recall at fast speed is a great deal.

All lessons in this course

  1. The Necessity of Vector Databases
  2. Vector Embeddings and Similarity Search
  3. Integrating with a Vector Database
  4. Indexing, Filtering & Hybrid Search
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