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Vector Databases, Inside and Out

Lesson 15 of 17

Lessons

  1. 1. The similarity search problem
  2. 2. Distance metrics and vector spaces
  3. 3. Embedding models — choosing and evaluating
  4. 4. HNSW: the production default
  5. 5. IVF: cluster-based search
  6. 6. Quantization: compressing vectors for scale
  7. 7. DiskANN and beyond: billion-scale search
  8. 8. Architecture of a vector database
  9. 9. Metadata filtering and hybrid search
  10. 10. Multi-vector search and late interaction
  11. 11. pgvector: the Postgres-native vector store
  12. 12. Choosing a vector database
  13. 13. Ingestion pipelines and corpus management
  14. 14. Scaling and multi-tenancy
  15. 15. Security and compliance
  16. 16. Operations and observability
  17. 17. Building a production vector search service

Security and compliance

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