Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.

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## Recommender Systems with Zilliz Cloud Vector Database

Explore the key role of Zilliz Cloud in seamlessly retrieving unstructured data and ensuring personalized recommendations across diverse online applications.


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Mastering Recommender Systems with Zilliz Cloud 

## Turbocharge Recommendations with Unmatched Speed

Achieve a 10x performance boost on Zilliz Cloud with advanced indexing algorithms, ensuring millisecond-level latency for prompt user interactions. Elevate your experience now!

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 Turbocharge Recommendations with Unmatched Speed

## A Real-Time Personalized Recommender System

Ensure consistently up-to-date recommendation results with unparalleled precision using range search capability, seamless data flows, and streamlined data management within Zilliz Cloud.





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Revolutionize Personalized Recommendations in Real Time

## Effortless Scaling for Limitless Business Expansion

Say goodbye to data size concerns with Zilliz Cloud. Our clusters seamlessly scale with your budget and evolving needs. Independently scale computing and storage for effortlessly handling tens of billions of vectors.



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Effortless Scaling for Limitless Business Expansion

## 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 experience for developers.

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Start with Simplicity and Amplified Productivity

## Ensure Unwavering System Resilience

Building recommender systems with Zilliz Cloud ensures uninterrupted services even in unexpected events through multiple replicas, component isolation, and robust backup and sync capabilities.

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Why Zilliz for Your Recommender System? 

Recommend similar information or products based on user behaviors and preferences.


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A Zilliz-powered e-commerce recommendation engine works in the following way: 

-  Users’ purchase behaviors and product-related data are transformed into embeddings through an embedding model.
- These embeddings are ingested into Zilliz Cloud (the fully managed Milvus) for storage and retrieval. 
- Zilliz retrieves the Top-K most relevant results by comparing the spatial distance between the user and product vectors.
- After filtering, scoring, and ordering processes, the recommender system provides users with recommended products they are interested in.



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How Zilliz Powers an e-commerce recommendation engine

How Zilliz Powers Recommender Systems

Build an agile and reliable recommender system with Zilliz (managed Milvus) vector database for real-time personalized recommendations.

Vector Database Use Case: Recommender System - Zilliz

Recommender System

## Semantic Search with Zilliz Cloud Vector Database

Semantic search uses [NLP](https://zilliz.com/learn/introduction-to-natural-language-processing-tokens-ngrams-bag-of-words-models) 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.

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Semantic Search with Zilliz Cloud Vector Database

## Built-in Vectorization Pipelines for AI-powered Search

Zilliz Cloud’s built-in pipelines convert documents into searchable [vector embeddings](https://zilliz.com/glossary/vector-embeddings), seamlessly navigating from data preparation to chunking, model selection, and transformation. 

<a href='https://zilliz.com/zilliz-cloud-pipelines' target='_self' class='use-case-special-link'>Learn more about Zilliz Cloud Pipelines</a>


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Built-in Vectorization Pipelines for AI-powered Search

## 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!


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Robust Vector Retrieval for Optimal Search Experience 

## 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.



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Offer Real-Time Search Results with Unparalleled Precision

## 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](https://zilliz.com/learn/vector-similarity-search) service in minutes with user-friendly SDKs in multiple languages and streamlined data operations, ensuring an intuitive user experience.



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## Always On, Always Reliable 

Build resilient AI search engines without interrupted services with multiple replicas, isolating system components, and robust backup and sync capabilities.


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Why Zilliz for Semantic Search? 

Search for semantically similar texts across vast amounts of natural language documents.

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A Zilliz-powered semantic search engine works in the following ways: 

- Documents are ingested into Zilliz Cloud. 
- Zilliz Cloud’s built-in Ingestion Pipeline converts the documents into vector embeddings and stores them in Zilliz Cloud collections. 
- The user makes a query. 
- Zilliz Cloud’s built-in Search Pipeline converts user queries into vector embeddings and sends them to Zilliz Cloud for similarity retrieval. 
- Zilliz Cloud retrieves the Top-K most relevant results by comparing the spatial distance between the user query and document vectors. 
- Zilliz Cloud returns the Top-K results to users.

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A Zilliz-powered semantic search engine works in the following ways: 

How Zilliz Cloud Powers Semantic Search？

Building AI-powered semantic search engine with Zilliz vector database (fully managed Milvus)

Vector Database Use Case: Semantic Search - Zilliz

Semantic Search

## Image Similarity Search with Zilliz Cloud Vector Database

Discover visually similar images effortlessly with Zilliz Cloud-powered image similarity search engines, proving valuable in various fields like e-commerce and art.


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Efficient Image Discovery with Zilliz Cloud Vector Database

## Effortlessly Accommodate Your Spiking Image Data 

Say goodbye to data size concerns with Zilliz Cloud. Experience horizontal and independent scaling for computing and storage, aligning with your budget and evolving needs. Manage billions of image vectors within hours with efficient bulk inserts.


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Effortlessly Accommodate Your Spiking Image Data 

## High-Performant Image Similarity Search with Unparalleled Precision

Experience a 10x performance boost on Zilliz Cloud with advanced indexing algorithms, ensuring millisecond-level latency. Benefit from unparalleled precision using hybrid [vector search](https://zilliz.com/learn/vector-similarity-search) with scalar filtering and range search capability.

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 High-Performant Image Search with Unparalleled Precision

## Real-Time Image Search Results for Optimized User Experience

Zilliz Cloud guarantees consistently up-to-date search results through real-time data insertion, deletion, and seamless data flows, significantly optimizing the user experience.


Real-Time Image Results for Optimized User Experience

## Always On, Always Reliable

Zilliz Cloud builds resilient image search systems, guaranteeing uninterrupted service reliability even in unexpected events through multiple replicas, component isolation, and robust backup and sync capabilities.


## Start with Simplicity and Amplified Productivity

Deploy a large-scale image similarity search service in minutes with user-friendly SDKs in multiple languages and streamlined data operations, ensuring an intuitive user experience.


Search for visually similar images from a vast collection of image libraries.

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A Zilliz-powered image similarity search system works in the following ways: 

- Images are transformed into vector embeddings using an embedding model. 
- Image embeddings are ingested and stored in Zilliz Cloud. 
- Users search for images similar to their query images. 
- The image query is transformed into vector embeddings using an embedding model. 
- The query embeddings are sent to Zilliz Cloud for a similarity search. 
- Zilliz Cloud performs [approximate nearest neighbor (ANN) searches ](https://zilliz.com/glossary/anns)and retrieves the Top-K most relevant results by comparing the spatial distance between the query and image vectors. 
- Zilliz Cloud returns the Top-K results to the user. 


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How Zilliz Cloud Powers Image Similarity Search

Building agile and reliable image similarity search with Zilliz Cloud (fully managed Milvus). 

Vector Database Use Case: Image Similarity Search 

Image Similarity Search

## Audio Similarity Search With Zilliz Cloud Vector Database

Effortlessly discover similar audio clips through the Zilliz Cloud-powered audio search engine, benefiting apps like music recommendation and speech recognition with high efficiency.


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Audio Similarity Search With Zilliz Cloud Vector Database

## High-Performant Audio Search with Unparalleled Precision

Experience a 10x performance boost on Zilliz Cloud with advanced indexing algorithms, ensuring millisecond-level latency. Benefit from unparalleled precision using hybrid vector search with scalar filtering and range search capability.


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High-Performant Audio Search with Unparalleled Precision

Find similar audio results from massive amounts of audio data such as music, sound effects, and speeches.

A Zilliz-powered audio similarity search system works in the following ways: 

- Audio clips are transformed into vector embeddings using an embedding model. 
- Audio embeddings are ingested and stored in Zilliz Cloud. 
- Users request audio clips similar to their queried ones. 
- The audio query is transformed into vector embeddings using an embedding model. 
- The query embeddings are sent to Zilliz Cloud for a similarity search. 
- Zilliz Cloud performs [ANN](https://zilliz.com/glossary/anns) searches and retrieves the Top-K most relevant results.
- Zilliz Cloud returns the Top-K results to the user. 


How Zilliz Cloud Powers Audio Similarity Search

Building agile and reliable audio similarity search with Zilliz vector database (fully managed Milvus). 

Vector Database Use Cases

Popular Vector Database Use Cases
Zilliz Cloud Can Make a Difference

Retrieval Augmented Generation

Recommender System

Text/Semantic Search

Image Similarity Search

Audio Similarity Search

Video Similarity Search

Question Answering System

Molecular Similarity Search

Anomaly Detection

Multimodal Similarity Search

Share Your Story with Us!

Start to build your GenAl apps today with Zilliz Cloud Serverless

Vector Database Use Cases

Popular Vector Database Use CasesZilliz Cloud Can Make a Difference

Retrieval Augmented Generation

Recommender System

Text/Semantic Search

Image Similarity Search

Audio Similarity Search

Video Similarity Search

Question Answering System

Molecular Similarity Search

Anomaly Detection

Multimodal Similarity Search

Share Your Story with Us!

Start to build your GenAl apps today with Zilliz Cloud Serverless

Popular Vector Database Use Cases
Zilliz Cloud Can Make a Difference