qdrant-vector-search

# 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 ```bash # Python client pip install qdrant-client # Docker (recommended for development) docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant # Docker with persistent storage docker run -p 6333:6333 -p 6334:6334 \ -v $(pwd)/qdrant_storage:/qdrant/storage \ qdrant/qdrant ``` ### Basic usage ```python from qdrant_client import QdrantClient from qd...