Qdrant offers purpose-built vector similarity search. HNSW indexing enables fast approximate search. Payload filtering combines vector and attribute search. Rust implementation provides performance.
Collection Setup
Create collections with vector configuration. Define payload schemas for filtering. Configure distance metrics matching embeddings. Plan sharding for large datasets.
- Configure vector dimensions matching your embeddings
- Define payload indexes for filtering performance
- Choose appropriate distance metrics
- Use batch operations for efficient ingestion
- Monitor collection statistics and performance
Query Optimization
Combine vector search with payload filters. Use score thresholds for quality control. Implement pagination for large result sets. Cache frequent queries where appropriate.