qdrant/qdrant

What it solves

Qdrant is a production-ready vector similarity search engine and database designed to store, search, and manage high-dimensional vectors (embeddings) along with their associated metadata (payloads). It enables developers to build applications for semantic search, recommendations, and image discovery by moving beyond simple keyword matching to meaning-based retrieval.

How it works

Written in Rust for high performance and reliability, Qdrant stores "points" consisting of a vector and a JSON payload. It supports multiple vector types—dense for semantic meaning, sparse for full-text search, and multi-vector for complex models like ColBERT. To optimize retrieval, it uses SIMD hardware acceleration and GPU support for indexing, while quantization techniques can reduce RAM usage by up to 97%.

Who it’s for

It is designed for AI engineers and developers building neural-network or semantic-based matching applications, such as e-commerce product categorization, visual search engines, and AI agents that require long-term memory or efficient data retrieval.

Highlights

• Hybrid Search: Combines semantic and keyword precision using fusion strategies like Reciprocal Rank Fusion (RRF).
• Filtering on Payload: Allows complex filtering of search results using JSON metadata with conditions like keyword matching and geo-locations.
• Distributed Deployment: Supports horizontal scaling through sharding and replication with zero-downtime updates.
• Qdrant Edge: A lightweight version that runs inside the application process for low-latency, offline functionality on edge devices.
• Developer Tooling: Provides a Web UI for data exploration and official client libraries for Go, Rust, JS/TS, Python, .NET, and Java.