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Milvus — The World’s Most Popular Open-source Vector Database
The most mature distributed vector database in the world, honed over 6 years with over 5,000 enterprise users.
Reduced costs and increased efficiency - Milvus has a significant cost advantage over other vector databases
Development Cost
Reduce the database level functionality development work, more focused on business code implementation.
· Research, Selection
· Deployment and Installation
· Enterprise-level Features
· Multi-Cloud Adaptation
Hardware Cost
Zilliz self-developed engine, professional indexing algorithms, pooling technology, significantly reducing user costs.
· Index Building
· Insertion/Query Performance
· To cope with the waste of resources caused by business peaks
Maintenance Cost
Lightweight maintenance work and greatly increased business reliability
· Capacity expansion and contraction
· Performance Tuning
· Discover, diagnose, and resolve
· Version Upgrade
Superior Performance - Self-developed Cardinal Vector Search Engine
Cardinal is a multi-threaded, C++-based vector search engine developed by Zilliz Cloud, which is deeply optimized for the mainstream ANNS method to achieve efficient computing resource usage. Through SIMD optimization, concurrency optimization, index data structure adjustment, quantization strategy and cache strategy adjustment, Cardinal achieves more than three times the performance improvement of the open source solution ScaNN/DiskANN/HNSW.
Zilliz Cloud Architecture
Zilliz Cloud is a cloud-native vector database that helps you easily manage and scale Milvus clusters, store vectors, and perform vector similarity searches. It has a functional architecture that is organized into three layers: an interface layer, a functional layer, and a cloud infrastructure layer. The interface layer provides the vector database API and the management API. The Vector Database API acts as a bridge, allowing user applications to easily interact with Zilliz Cloud for storing, querying, and other operations on vectors. The Governance API is used to manage and control Zilliz Cloud, such as setting permissions, monitoring performance, and so on.
Common Use Cases: Cloud Documents, Document Retrieval and Enterprise Knowledge Base
Common Use Cases: Cloud Documents, Document Retrieval and Enterprise Knowledge Base
1. Support semantic search and keyword querying across PDF, DOC, TXT, CSV, JSON documents for both toB and toC scenarios.
2. Large traffic during peak usage leads to great operation and maintenance pressure, and latency and stability severely degrade user experience.
3. Integrate embedding models for direct vectorization of inserted and queried documents, supporting dictionary trees, inverted indexes, among others.
4. Per-tenant data isolation is needed. With 100,000+ tenants per table, the system needs to support tenant-specific vector queries.
Increasing Text Conversation Records and Standard Q&A Sessions
1. Intelligent voice assistants and Q&A robots require both long and short-term memory
2. High recall rate in combination with sparse vector models enables more precise search results
3. Support frequent data modification, and allow per-tenant data isolation and periodic expiration policies
4. Certain scenarios require embedded clients to support offline operation on low-power hardware
Retrieval Augmented Generation (RAG) Application
Expand LLM knowledge by integrating external data sources into LLMs and AI applications.
Recommender System
Match user behavior or content features with other similar behaviors or features to make effective recommendations.
Text/Semantic Search
Search for semantically similar texts across vast amounts of natural language documents.
Image Similarity Search
Identify and search for visually similar images or objects from a vast collection of image libraries.
Video Similarity Search
Search for similar videos, scenes, or objects from extensive collections of video libraries.
Audio Similarity Search
Find similar audios from massive amounts of audio data to perform tasks such as genre classification, or speech recognition.
Molecular Similarity Search
Search for similar substructures, superstructures, and other structures for a specific molecule.
Anomaly Detection
Detect data points, events, and observations that deviate significantly from the usual pattern in a dataset.
Autonomous Driving Data Preparation
Mass sample data storage, efficient search, accurate matching, and optimization of decision-making and navigation
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