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Version: User Guides (Cloud)

Search & Rerank

Zilliz Cloud implements vector-based similarity searches and provides multiple optimizations to improve search efficiency and performance. In this chapter, you will learn the principles and tutorials of various vector search methods available on Zilliz Cloud.

Hybrid Search [READ MORE]

In many applications, data is represented using various fields like title and description, or through different modalities such as text, images, and audio. Hybrid search enhances accuracy by combining searches across these diverse fields. Zilliz Cloud supports this feature with multiple vector fields, conducting several approximate nearest neighbor (ANN) searches simultaneously. Multi-vector hybrid search typically yields better results than a single search. This approach is especially valuable in domains that demand a complex understanding of content, such as e-commerce search, academic paper search, and recommendation systems.

Full Text Search [READ MORE]

Full text search is a feature that retrieves documents containing specific terms or phrases in text datasets, then ranking the results based on relevance. This feature overcomes semantic search limitations, which might overlook precise terms, ensuring you receive the most accurate and contextually relevant results. Additionally, it simplifies vector searches by accepting raw text input, automatically converting your text data into sparse embeddings without the need to manually generate vector embeddings.

Elasticsearch Queries to Milvus [READ MORE]

Elasticsearch, built on Apache Lucene, is a leading open-source search engine. However, it faces challenges in modern AI applications, including high update costs, poor real-time performance, inefficient shard management, a non-cloud-native design, and excessive resource demands. As a cloud-native vector database, Milvus overcomes these issues with decoupled storage and computing, efficient indexing for high-dimensional data, and seamless integration with modern infrastructures. It offers superior performance and scalability for AI workloads.