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Milvus - 高性能云原生向量数据库
Milvus is a high-performance, cloud-native vector database built for scalable vector similarity search and AI applications. Designed to handle massive datasets, it empowers developers to build next-generation search, recommendation, and anomaly detection systems using vector embeddings.
Go
Added on 2025年6月11日35,271
Stars3,244
ForksGo
LanguageProject Introduction
Summary
Milvus is an open-source vector database system specifically designed for storing, indexing, and managing large-scale vector data generated by deep learning models and other embedding techniques. It is optimized for performing efficient vector similarity search queries.
Problem Solved
Traditional databases are not optimized for searching high-dimensional vector data based on similarity. Milvus provides a specialized database solution that overcomes the performance and scalability limitations of general-purpose databases for vector similarity search tasks.
Core Features
Diverse Index Types
Supports various Approximate Nearest Neighbor (ANN) algorithms like IVF_FLAT, HNSW, NSG, enabling efficient similarity search on large vector datasets.
Cloud-Native Architecture
Designed for cloud-native environments with Kubernetes support, offering high availability, elasticity, and fault tolerance.
Hybrid Search Capabilities
Provides features for filtering search results based on scalar fields combined with vector similarity, enhancing query precision.
Tech Stack
C++
Go
Python
gRPC
Prometheus
Grafana
Docker
Kubernetes
etcd
Pulsar
使用场景
Milvus is suitable for a wide range of AI and data-intensive applications that rely on finding similar items or data points based on their vector representations. Common use cases include:
大规模图像/视频搜索
Details
Store image embeddings and quickly find visually similar images based on features extracted by CNNs.
User Value
Enables building robust reverse image search engines and content moderation systems.
自然语言处理 (NLP) 应用
Details
Index text embeddings from language models (e.g., BERT, GPT) to power semantic search, question answering, and document retrieval systems.
User Value
Improves search relevance and powers intelligent conversational AI applications.
推荐系统
Details
Store user/item embeddings to recommend similar items or content, enhancing user engagement.
User Value
Provides efficient similarity matching needed for personalized recommendations at scale.
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