Vector Databases: A Technical Primer

A vector database stores high-dimensional vectors, numeric representations of unstructured data like text, images, audio, and video. It powers semantic search, RAG, recommender systems, and AI agents by enabling fast similarity search using metrics like cosine or Euclidean distance.

Categories

  1. General-Purpose (Open Source):
    • Weaviate: GraphQL, hybrid search.
    • Milvus: Scalable, index-rich.
    • Chroma: Python-first, LLM-friendly.
    • Qdrant: Rust-fast, rich filters.
  2. Lightweight / Embedded:
    • Annoy: Efficient, edge-ready.
    • FAISS: Scalable, Facebook-built.
    • ScaNN: Google-optimized.
  3. Cloud-Hosted:
    • Pinecone: Fully managed, multi-tenant.
    • Zilliz Cloud: Milvus in the cloud.
    • Redis Vector: Redis-native.
  4. On-Prem / Custom:
    • Self-host Milvus, Weaviate, etc. for privacy or integration needs.

Core Use Cases

  • RAG for LLMs
  • Semantic & multimodal search
  • Chatbot memory
  • Recommender engines
  • Autonomous agents

Key Features

  • Embedding support (text, image, audio, video)
  • Metadata filtering
  • Real-time updates
  • LLM ecosystem integration (LangChain, OpenAI)
  • IVF, HNSW, PQ, Flat indexing
  • Scalable (sharding, cloud/on-prem)

Pro Tip: For RAG, pick vector DBs with fast ingestion, Python APIs & metadata filtering.

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