rag-qdrant

Topics in the source

• Qdrant - Vector Similarity Search Engine
• When to use Qdrant
• Quick start
• Installation
• Basic usage
• Core concepts
• Points - Basic data unit
• Collections - Vector containers

High-performance vector database written in Rust for production RAG and semantic search. 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 | 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 featur

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