030101 lancedb search
Multi-domain agent skills collection for AI coding agents (Claude, Cursor, Copilot, OpenCode, and more). Covers programming, biology, cooking, and future domains. Installable via npx skills add.
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LanceDB vector search fundamentals — distance metrics, ANN indexing, embeddings, similarity search patterns, and performance tuning.
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SKILL.md
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Vector Search Core
Overview
Core concepts for vector search with LanceDB: distance metrics, ANN indexing, embedding models, and search patterns.
Quick Reference
Distance Metrics
| Metric | Use Case | Behavior |
|---|---|---|
l2 | General purpose | Smaller = more similar |
cosine | Text/document similarity | Smaller = more similar |
dot | Normalized vectors | Larger = more similar |
hamming | Binary vectors | Smaller = more similar |
Search Modes
- Exact search: Brute force, 100% recall, no index needed
- ANN search: Approximate, fast, requires index, configurable recall
- Hybrid: Vector + FTS combined with reranking
Embedding Functions
- OpenAI:
text-embedding-ada-002,text-embedding-3-small,text-embedding-3-large - Sentence Transformers: local models via
sentence-transformers - Custom: create your own embedding function
References
- Vector Fundamentals — Distance metrics, ANN vs exact, nprobes
- Vector Search Patterns — Prefiltering, binary search, batch, brute-force
- Embeddings — Embedding function registry, dimension selection