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🎬Video•30 min

Vector Database Fundamentals

Understand vector databases and when to use them.

Vector Databases

What is a Vector Database?

Specialized database for storing and searching vectors (embeddings).

Optimized for:

  • High-dimensional vectors
  • Similarity search
  • Approximate nearest neighbor (ANN)

    • Key Operations

    1. Insert Store vectors with metadata.

    2. Search (Query) Find most similar vectors to query.

    3. Filter Combine vector search with metadata filters.

    4. Update/Delete Manage vector lifecycle.

    Popular Options

    Managed:

  • Pinecone: Easy to use, fully managed
  • Weaviate Cloud: GraphQL interface
  • Qdrant Cloud: Open source + cloud

    • Self-Hosted:

  • Chroma: Simple, local-first
  • Milvus: Scalable, enterprise
  • Qdrant: Rust-based, fast
  • pgvector: PostgreSQL extension

    • When to Use

  • RAG applications
  • Semantic search
  • Recommendation systems
  • Duplicate detection
  • Clustering at scale

    • 🎯 Key Takeaways

    • ✓Vector DBs optimize for similarity search
    • ✓Use ANN algorithms for speed at scale
    • ✓Choose managed vs self-hosted based on needs
    • ✓Combine vector search with metadata filtering

    📚 Additional Resources

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    Modern Embedding Approaches
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    Pinecone Deep Dive