similarity-search-patterns
Efficient similarity search patterns for vector databases and semantic retrieval systems.
- Covers four major vector database implementations: Pinecone, Qdrant, pgvector with PostgreSQL, and Weaviate, each with production-ready code templates
- Explains three index types (Flat, HNSW, IVF+PQ) with trade-offs between search speed, recall accuracy, and data scale
- Includes four distance metrics (Cosine, Euclidean, Dot Product, Manhattan) and guidance on when to use each
- Demonstrates hybrid search combining dense vectors with keyword search, reranking, and metadata filtering patterns
- Provides best practices for index tuning, recall evaluation, and latency optimization
Similarity Search Patterns
Patterns for implementing efficient similarity search in production systems.
When to Use This Skill
- Building semantic search systems
- Implementing RAG retrieval
- Creating recommendation engines
- Optimizing search latency
- Scaling to millions of vectors
- Combining semantic and keyword search
Core Concepts
1. Distance Metrics
| Metric | Formula | Best For | | ------------------ | ------------------ | --------------------- | --- | -------------- |
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