Recherche sémantique
Créez des applications de recherche IA dynamiques propulsées par la recherche sémantique avec la base de données vectorielle Zilliz Cloud (Milvus entièrement géré)
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.
Comment Zilliz Cloud alimente la recherche sémantique？
Un moteur de recherche sémantique propulsé par Zilliz fonctionne de la manière suivante :
- Les documents sont ingérés dans Zilliz Cloud.
- Le pipeline d’ingestion intégré de Zilliz Cloud convertit les documents en embeddings vectoriels et les stocke dans des collections Zilliz Cloud.
- L’utilisateur effectue une requête.
- Le pipeline de recherche intégré de Zilliz Cloud convertit les requêtes des utilisateurs en embeddings vectoriels et les envoie à Zilliz Cloud pour une récupération par similarité.
- Zilliz Cloud récupère les Top-K résultats les plus pertinents en comparant la distance spatiale entre la requête de l’utilisateur et les vecteurs des documents.
- Zilliz Cloud renvoie les Top-K résultats aux utilisateurs.
Histoires de Succès
Les leaders de l’industrie du monde entier utilisent Milvus ou Zilliz (Milvus géré) pour doter leurs applications de capacités avancées de recherche sémantique.
Internet Services
HumanSignal Offers Faster Data Sourcing & Labeling with Milvus and AWS
Lire maintenantSuper 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
Internet Services
HumanSignal Offers Faster Data Sourcing & Labeling with Milvus and AWS
Lire maintenantSuper 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