AI Quick Reference
Practical references for vector data lakes, Milvus components, retrieval indexes, AI agents, data curation, and production systems. Browse questions by topic.
- How does semantic search differ from keyword search?
- What is full-text search, and how does BM25 fit?
- What is approximate nearest-neighbor search?
- What is HNSW, and how should you tune it?
- How do IVF indexes use centroids to search vectors?
- What is vector quantization, and what does it trade off?
- How do you choose a vector index for your workload?
- How do metadata filters interact with vector search?
- What is hybrid search, and when should you use it?
- What is reranking, and when should you apply it?
- When should you use DISKANN in Milvus?
- What is GPU_CAGRA, and when does it help vector search?
- How do HNSW_SQ, HNSW_PQ, and HNSW_PRQ change the trade-offs?
- What is IVF_RABITQ, and how should you evaluate it?
- How does a sparse inverted index differ from dense ANN?
- How does MINHASH_LSH help find near-duplicate text?
- Why use a FLAT baseline when tuning an ANN index?
- When should you use range search instead of only top-k?
- What is late-interaction retrieval, and how can embedding lists help?
- How does mmap differ from a disk-oriented ANN index?
- What is FAISS, and how does it relate to vector indexes?