Semantic Search with Zilliz Cloud Vector Database
Semantic search uses NLP algorithms to understand user query intent and meaning. Easily craft a semantic search engine using Zilliz Cloud for streamlined data management, swift retrieval, system reliability, and more.
Built-in Vectorization Pipelines for AI-powered Search
Zilliz Cloud’s built-in pipelines convert documents into searchable vector embeddings, seamlessly navigating from data preparation to chunking, model selection, and transformation.
Robust Vector Retrieval for Optimal Search Experience
Achieve a 10x performance boost on Zilliz Cloud with advanced indexing algorithms, ensuring millisecond-level latency for prompt user interactions. Elevate your AI-bases search experience now!
Offer Real-Time Search Results with Unparalleled Precision
Ensure consistently up-to-date search results with unparalleled precision using range search, hybrid vector search and scalar filtering, seamless data management within Zilliz Cloud.
Effortless Scaling for Limitless Business Expansion
Say goodbye to data size concerns with Zilliz Cloud. The computing and storage resources seamlessly and independently scale to align with your budget and evolving needs.
Start with Simplicity and Amplified Productivity
Launch a large-scale similarity search service in minutes with user-friendly SDKs in multiple languages and streamlined data operations, ensuring an intuitive user experience.
Always On, Always Reliable
Build resilient AI search engines without interrupted services with multiple replicas, isolating system components, and robust backup and sync capabilities.
Zilliz Cloudはどのようにセマンティック検索を強化するか？
Zillizを搭載したセマンティック検索エンジンは、次のように機能する：
- ドキュメントはZilliz Cloudに取り込まれる。
- Zilliz Cloudに組み込まれたIngestion Pipelineがドキュメントをベクトル埋め込みに変換し、Zilliz Cloudのコレクションに保存する。
- ユーザーがクエリーを行う。
- Zilliz Cloudの組み込み検索パイプラインはユーザーのクエリをベクトル埋め込みに変換し、類似検索のためにZilliz Cloudに送信します。
- Zilliz Cloudはユーザークエリとドキュメントベクトル間の空間距離を比較することで、最も関連性の高い結果Top-Kを検索します。
- Zilliz CloudはTop-Kの結果をユーザーに返します。
成功事例
世界中の業界リーダーがMilvusまたはZilliz（マネージドMilvus）を使用して、高度なセマンティック検索機能でアプリケーションを強化しています。
Super Low Latency
in semantic search
Enhanced Scalability
in vector data storage
Faster and More Reliable
in image indexing
10x Smarter
search experience
Improved
user experience
Enhanced
scalability and reliability
<95 milliseconds
end-to-end query latency
<10 seconds
to ingest 3 million vectors
Enable
real-time threat detection
Flexible Scaled
with separated compute and storage
Enhanced
query precision with hybrid search
Accelerated feature deployment
with the leading open-source community
10x faster
in vector retrieval speed
> 95%
in news classification accuracy
Less memory consumption
for faster responses and reduced cost
Super Low Latency
in semantic search
Enhanced Scalability
in vector data storage
Faster and More Reliable
in image indexing
10x Smarter
search experience
Improved
user experience
Enhanced
scalability and reliability