Milvus

What is Milvus?

Milvus is a cloud-native vector database built to handle massive amounts of unstructured data like text, images, and multi-modal content. It features a distributed architecture that separates compute and storage, enabling horizontal scalability and high availability. Milvus supports a wide range of vector indexing methods, hardware acceleration, and advanced search capabilities including approximate nearest neighbor (ANN), metadata filtering, and hybrid dense-sparse vector search. It is widely adopted for AI applications such as semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG). Milvus also offers robust data security with authentication, encryption, and fine-grained access control.

Key Features

• Distributed and Scalable Architecture

  Decouples storage and compute with modular microservices, allowing independent scaling of query and data nodes to handle large workloads efficiently.

• Rich Indexing Support

  Supports over 10 vector index types including HNSW, IVF, FLAT, SCANN, and GPU-accelerated indexes, enabling tailored performance and accuracy.

• Versatile Search Capabilities

  Offers top-K ANN, range search, metadata filtering, and hybrid dense and sparse vector search for flexible and precise retrieval.

• Hardware Acceleration

  Leverages CPU SIMD instructions and GPU indexing to optimize vector search speed and cost-efficiency.

• Multi-Tenancy and Hot/Cold Storage

  Supports isolation at multiple levels for multi-tenant environments and optimizes costs by separating frequently accessed hot data and less-accessed cold data.

• Data Security and Access Control

  Implements mandatory user authentication, TLS encryption, and role-based access control (RBAC) to protect sensitive data.

Use Cases

• Semantic Search : Enables efficient similarity search over large text, image, and multi-modal datasets for applications like document retrieval and image recognition.
• Recommendation Systems : Analyzes user behavior and product features to deliver personalized recommendations in e-commerce and content platforms.
• Retrieval-Augmented Generation (RAG) : Enhances AI Q&A and chatbot systems by sourcing relevant information from large unstructured data collections.
• Fraud Detection : Detects anomalous patterns in transactions by comparing vectorized data against known fraud signatures.
• Visual and Object Recognition : Supports manufacturing and quality control by enabling defect detection and image-based object search.
• Real-Time Search and Matching : Facilitates real-time matching in recruitment, avatar customization, and video content recommendation with scalable vector search.

FAQs