加载中
正在获取最新内容，请稍候...
Qdrant - 高性能向量数据库与向量搜索引擎
Qdrant is a high-performance, massive-scale Vector Database and Vector Search Engine designed for the next generation of AI applications. It allows storing, searching, and managing vector embeddings with associated payload.
Rust
Added on 2025年5月30日23,842
Stars1,645
ForksRust
LanguageProject Introduction
Summary
Qdrant is an open-source vector database built in Rust, providing low-latency vector search and storage. It's designed to power AI applications that rely on vector embeddings, offering advanced features like payload filtering and horizontal scalability.
Problem Solved
Building AI applications like semantic search, recommendation engines, and image retrieval requires efficiently storing and searching high-dimensional vector data. Traditional databases are not optimized for this, leading to performance bottlenecks and complexity. Qdrant provides a dedicated solution.
Core Features
High Performance
Achieve millisecond response times even with billions of vectors.
Scalability
Scales horizontally to handle massive datasets and high query throughput.
Payload Filtering
Filter search results based on structured payload data associated with vectors.
Flexible Search
Supports various vector similarity search algorithms.
Tech Stack
Rust
gRPC
REST API
Docker
Kubernetes
使用场景
Qdrant is ideal for a wide range of AI-native applications requiring efficient vector similarity search and filtering, including:
Semantic Search & Question Answering
Details
Store embeddings of text chunks and use similarity search to find relevant documents or passages based on meaning, not just keywords.
User Value
Provides highly relevant search results based on natural language understanding.
Recommendation Systems
Details
Represent users and items as vectors and find similar items or users based on embeddings, filtered by categories, price, etc.
User Value
Offers personalized recommendations at scale and in real-time.
Image, Video & Audio Retrieval
Details
Search for similar images, videos, or audio clips based on their feature vectors.
User Value
Enables content-based search and media asset management.
Recommended Projects
You might be interested in these projects
go-restyresty
A powerful yet simple HTTP, REST, and SSE client library for Go, designed for ease of use, testability, and flexibility in making API requests and consuming network resources.
Go
10947755
Asabeneh30-Days-Of-Python
A comprehensive, step-by-step guide designed to help beginners learn the Python programming language over 30 days. While structured for 30 days, the challenge can be completed at your own pace.
Python
467458920
mit-han-labnunchaku
Official implementation of SVDQuant, a novel 4-bit quantization method designed for diffusion models that effectively handles outliers using Singular Value Decomposition (SVD) to maintain high performance with significantly reduced memory and computation.
Python
1946100