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 Cloud가 시맨틱 검색을 지원하는 방법？
Zilliz 기반 시맨틱 검색 엔진은 다음과 같은 방식으로 작동합니다:
- 문서가 Zilliz Cloud로 수집됩니다.
- Zilliz Cloud의 내장 Ingestion Pipeline이 문서를 벡터 임베딩으로 변환하고 Zilliz Cloud 컬렉션에 저장합니다.
- 사용자가 쿼리를 입력합니다.
- Zilliz Cloud의 내장 Search Pipeline이 사용자 쿼리를 벡터 임베딩으로 변환하고 유사도 검색을 위해 Zilliz Cloud로 보냅니다.
- Zilliz Cloud는 사용자 쿼리와 문서 벡터 간의 공간적 거리를 비교하여 가장 관련성이 높은 Top-K 결과를 검색합니다.
- Zilliz Cloud는 Top-K 결과를 사용자에게 반환합니다.
성공 사례
전 세계 업계 선도 기업들은 Milvus 또는 Zilliz(관리형 Milvus)를 사용하여 고급 의미 기반 검색 기능으로 애플리케이션을 강화합니다.
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