qdrant-vector-search

Production-grade vector search for RAG requires a database that handles fast similarity search and metadata filtering. This solution provides Rust-based high-performance vector storage with REST and gRPC APIs to index embeddings and retrieve relevant documents quickly.

Hybrid search with metadata filtering combines vector similarity with payload constraints. This tool supports multi-vector storage and payload filtering directly, allowing you to narrow down nearest-neighbor searches using specific metadata conditions.

Yes, it is designed for large-scale real-time retrieval in production environments. It offers distributed deployment options and quantization settings to optimize memory usage and performance, ensuring low latency for semantic search workloads.

To reduce memory consumption during nearest-neighbor search, you can apply quantization options. This database provides built-in quantization configurations to compress embedding vectors, optimizing memory footprint while maintaining search performance.

Yes, you need to install the qdrant-client Python package version 1.12.0 or higher. This client provides the interface to connect to your local or distributed instance, index embeddings, and execute vector queries.

Yes, multi-vector support is a core feature. You can store and query multiple embeddings per document, enabling complex RAG workflows where different vector representations are used for hybrid search and semantic retrieval.