Agent skill · software engineering · nousresearch
qdrant
Vector search engine for production RAG systems.
Why this skill is useful
Provides specific commands and configurations for using Qdrant in production RAG systems that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, qdrant-client installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 226,679 stars on the source repository.
What this skill does
Qdrant - Vector Similarity Search Engine High-performance vector database written in Rust for production RAG and semantic search. When to use Qdrant Use Qdrant when: Building production RAG systems requiring low latency Need hybrid search (vectors + metadata filtering) Require horizontal scaling with sharding/replication Want on-premise deployment with full data control Need multi-vector storage per record (dense + sparse) Building real-time recommendation systems Key features: Rust-powered: Memory-safe, high performance Rich filtering: Filter by any payload field during search Multiple vectors: Dense, sparse, multi-dense per point Quantization: Scalar, product, binary for memory efficiency Distributed: Raft consensus, sharding, replication REST + gRPC: Both APIs with full feature parity Use alternatives instead: Chroma: Simpler setup, embedded use cases FAISS: Maximum raw speed, research/batch processing Pinecone: Fully managed, zero ops preferred Weaviate: GraphQL preference, built-in vectorizers Quick start Installation Basic usage Core concepts Points - Basic data unit Collections - Vector containers Distance metrics Metric Use Case Range -------- ---------- ------- COSINE Text embeddings, normalized vectors 0 to 2 EUCLID Spatial data, image features 0 to ∞ DOT Recommendations, unnormalized -∞ to ∞ MANHATTAN Sparse features, discrete data 0 to ∞ Search operations Basic search Filtered search Batch search RAG integration With sentence-transformers With LangChain With LlamaIndex Multi-vector support Named vectors (different embedding models) Sparse vectors (BM25, SPLADE) Quantization (memory optimization) Payload indexing Production deployment Qdrant Cloud Performance tuning Best practices 1. Batch operations - Use batch upsert/search for efficiency 2. Payload indexing - Index fields used in filters 3. Quantization - Enable for large collections (>1M vectors) 4. Sharding - Use for collections >10M vectors 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/qdrant