qdrant-sparse

What problem does it solve?

This skill provides a structured approach to using Qdrant's sparse vector features for lexical retrieval, enabling efficient storage, indexing, and querying of sparse embeddings to improve relevance and latency.

Core Features & Use Cases

• Sparse-only collections with IDF-weighted sparse vectors
• Hybrid collections combining dense vectors with sparse vectors
• Multiple sparse vector fields and batch upserts with payloads
• Sparse search with optional payload filtering and score thresholds
• Hybrid search using prefetch and fusion strategies (RRF and DBSF)
• Performance tuning guidance for production workloads

Quick Start

Create a sparse-only collection named 'docs', upsert a batch of points with sparse vectors, then perform a sparse search using the 'text' field.