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2026-08-01 - 2026-08-31
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Qdrant 核心功能
High-Performance Vector Search — Qdrant is built for speed, delivering sub-millisecond query times even at scale, making it ideal for real-time AI applications.
Advanced Filtering — Combine vector similarity search with structured filtering (e.g., metadata, tags) to achieve precise and context-aware results.
Payload Support — Store and retrieve arbitrary JSON payloads alongside vectors, enabling rich data management without external databases.
Distributed Deployment — Scale horizontally with built-in sharding and replication, ensuring high availability and fault tolerance for production workloads.
Multiple APIs — Access via RESTful, gRPC, and Python/TypeScript client libraries, with support for popular frameworks like LangChain and LlamaIndex.
Hybrid Search — Combine dense and sparse vectors (e.g., BM25) for hybrid retrieval, improving relevance in RAG and semantic search systems.
Built-in Dashboard — Monitor cluster health, performance metrics, and collections through an intuitive web-based UI.