Qdrant

Description

Qdrant is an open-source vector database and similarity search engine purpose-built for AI applications. It stores embeddings alongside rich payload data and serves approximate nearest-neighbor queries at massive scale, powering semantic search, recommendation systems, and retrieval-augmented generation pipelines.

Features

• Vector Similarity Search: Finds nearest neighbors across millions of embeddings in milliseconds.
• Hybrid Filtering: Combines vector search with structured metadata filters in a single query.
• HNSW Indexing: Uses a tunable graph-based index for high recall and low latency.
• Quantization: Compresses vectors to cut memory usage without sacrificing accuracy.
• Horizontal Scaling: Shards and replicates collections across a distributed cluster.
• Multiple SDKs: Offers official clients for Python, Rust, Go, TypeScript, and a gRPC/REST API.

Technology Stack

Core engine written in Rust for memory safety and speed. Exposes gRPC and REST APIs, integrates with LangChain, LlamaIndex, and Haystack, and deploys via Docker, Kubernetes, or Qdrant's managed cloud.

Requirements

Linux, macOS, or Windows (via Docker); minimum 2 GB RAM for small collections, scaling with vector count and dimensionality; x86_64 or ARM64 architecture; persistent disk storage for collection snapshots.