Artificial Intelligence • Interactive Module

Module 10: Embeddings, Vector Databases & Semantic Search

Master dense embeddings, cosine similarity, Euclidean distance, FAISS indexing (Flat, IVF, HNSW, PQ), ChromaDB collections and metadata filtering, document chunking strategies, hybrid BM25 search, and cross-encoder rerankers.

Concept Overview & Direct Technical Answer

Direct Definition: Master dense embeddings, cosine similarity, Euclidean distance, FAISS indexing (Flat, IVF, HNSW, PQ), ChromaDB collections and metadata filtering, document chunking strategies, hybrid BM25 search, and cross-encoder rerankers.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/ai/vector-databases. Free, open curriculum for software engineers, systems practitioners, and interview candidates.