AI Quick Reference
Practical references for vector data lakes, Milvus components, retrieval indexes, AI agents, data curation, and production systems. Browse questions by topic.
- What is a vector database, and when do you need one?
- What is vector search?
- What are embeddings in vector search?
- How do you choose an embedding model for retrieval?
- How do cosine similarity, inner product, and L2 differ?
- What are dense and sparse embeddings?
- What are multimodal embeddings?
- 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?
- What is retrieval-augmented generation (RAG)?
- How do you choose chunk size for RAG?
- How do you evaluate retrieval quality?
- How should you structure agent memory in Milvus?
- What is Graph RAG, and where does Milvus fit?
- How does ETL prepare data for vector search?
- How does data quality affect vector retrieval?