0170-similarity-search-patterns

What problem does it solve?

Similarity search systems struggle to balance relevance, latency, and cost when retrieving the nearest vectors from large embedding collections.

Core Features & Use Cases

• Distance & Metric Selection: Choose cosine, L2, dot product, or L1 based on embedding characteristics and desired scoring behavior.
• Index Strategy: Select the right index type (flat exact, HNSW graph-based, IVF+PQ quantized) to trade off recall, speed, and memory.
• Operational Best Practices: Tune retrieval parameters, implement hybrid (vector + keyword) search, pre-filter candidates, and continuously monitor recall and tail latency (P99).
• Use Case: Implement semantic search for RAG by retrieving top-k relevant chunks, optionally with reranking to improve final answer quality.

Quick Start

Ask an AI to generate a production-ready similarity search module using the included Pinecone, Qdrant, pgvector, or Weaviate templates, tuned for cosine distance and hybrid retrieval.