Vector Databases

Vector Databases

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Vector Databases

Vector databases store and query high-dimensional vector embeddings. They're essential for similarity search, semantic search, and AI-powered applications requiring vector operations.

Key Concepts

  • Embedding storage
  • Approximate Nearest Neighbor (ANN)
  • Index types (HNSW, IVF, PQ)
  • Similarity metrics (cosine, dot product, euclidean)
  • Hybrid search capabilities

Common Use Cases

  • Semantic search
  • RAG applications
  • Recommendation systems
  • Duplicate detection
  • AI-powered chatbots

Best Practices

  • Choose index based on query patterns
  • Monitor recall vs latency trade-offs
  • Implement proper filtering
  • Consider hybrid search
  • Plan for scale early

Resources

  • Pinecone, Weaviate, Qdrant, Milvus
  • Related Skills: embeddings, rag, langchain

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Frequently asked questions

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