All 83 Dependencies
Every package Qdrant depends on, ranked by repo health score.
Qdrant is an open-source, high-performance vector database and similarity search engine written in Rust. It is designed to store, index, and search vector embeddings produced by neural networks at production scale — powering use cases such as semantic search, retrieval-augmented generation (RAG), recommendation engines, image similarity, and anomaly detection. Its core innovation is a custom storage engine called Gridstore and a highly optimized HNSW (Hierarchical Navigable Small World) graph implementation accelerated by SIMD instructions and, optionally, NVIDIA and AMD GPUs via Vulkan.
Qdrant supports three vector modes: dense vectors for semantic similarity, sparse vectors (BM25, SPLADE++) for keyword-based retrieval, and multivectors (e.g. ColBERT-style late interaction). These can be combined in a single hybrid query with configurable fusion strategies like Reciprocal Rank Fusion (RRF) or Distribution-Based Score Fusion (DBSF). Rich JSON payload filtering — keyword matching, full-text, numeric ranges, geo-location, boolean conditions — is applied natively during HNSW graph traversal, not as a post-processing step.
For scale-out workloads, Qdrant offers horizontal sharding and replication with zero-downtime rolling upgrades, a Raft-based consensus layer for distributed consistency, and granular resource budgets for CPU and I/O. Built-in quantization (scalar, binary, product) cuts memory usage by up to 97% while preserving search accuracy. The platform also ships Qdrant Edge, a lightweight in-process version for edge devices and offline-capable applications.
Qdrant integrates with major AI frameworks and providers including LangChain, LlamaIndex, Haystack, OpenAI, Cohere, and Microsoft Semantic Kernel. Its API surface covers REST (OpenAPI 3.0) and gRPC for high-throughput production use, supplemented by a built-in Web UI for collection management, data exploration, and optimization monitoring.