| Guide | Description | Topics Covered |
|---|---|---|
| Embedding Data | Complete reference for LanceDB’s embedding API | • Embedding Function Registry • Available Providers (OpenAI, Cohere, Sentence Transformers) • Multi-modal Embeddings • Custom Embedding Functions • Schema Configuration |
| Reranking Results | Improve search relevance by re-ordering results | • Built-in Rerankers (Cohere, CrossEncoder, ColBERT) • Multi-vector Reranking • Custom Reranker Implementation • Performance Optimization |
| Query Optimization | Analyze and optimize query performance | • explain_plan for query analysis• analyze_plan for performance tuning• Execution plan interpretation • Index optimization strategies • Performance metrics and debugging |
Quick Navigation
Embedding & Vectorization
- Quickstart Guide - Get started with embeddings in minutes
- Complete Reference - Full embedding API documentation
Search & Reranking
- Reranking Overview - Improve search relevance with rerankers
- Custom Rerankers - Build your own reranking models
Performance & Optimization
- Query Optimization - Analyze and tune query performance
- Execution Plans - Understand query execution flow
- Performance Metrics - Monitor and debug performance issues
What’s Next?
After exploring these guides, you might want to:
- Build Applications - Check out our tutorials for end-to-end examples
- Explore Integrations - See how LanceDB works with various frameworks
- Learn Core Concepts - Deep dive into search concepts and indexing