Búsqueda semántica
Crea aplicaciones dinámicas de búsqueda con IA impulsadas por búsqueda semántica con la base de datos vectorial Zilliz Cloud (Milvus completamente gestionado)
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.
¿Cómo Zilliz Cloud impulsa la búsqueda semántica?
Un motor de búsqueda semántica impulsado por Zilliz funciona de las siguientes maneras:
- Los documentos se ingieren en Zilliz Cloud.
- El Pipeline de ingesta integrado de Zilliz Cloud convierte los documentos en embeddings vectoriales y los almacena en colecciones de Zilliz Cloud.
- El usuario realiza una consulta.
- El Pipeline de búsqueda integrado de Zilliz Cloud convierte las consultas de los usuarios en embeddings vectoriales y las envía a Zilliz Cloud para la recuperación por similitud.
- Zilliz Cloud recupera los resultados Top-K más relevantes comparando la distancia espacial entre la consulta del usuario y los vectores de los documentos.
- Zilliz Cloud devuelve los resultados Top-K a los usuarios.
Historias de Éxito
Líderes de la industria de todo el mundo usan Milvus o Zilliz (Milvus gestionado) para potenciar sus aplicaciones con capacidades avanzadas de búsqueda semántica.
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