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.

Basic Vector Search [READ MORE]

Based on an index file recording the sorted order of vector embeddings, the Approximate Nearest Neighbor (ANN) search locates a subset of vector embeddings based on the query vector carried in a received search request, compares the query vector with those in the subgroup, and returns the most similar results. With ANN search, Zilliz Cloud provides an efficient search experience. This page helps you to learn how to conduct basic ANN searches.

Filtered Search [READ MORE]

An ANN search finds vector embeddings most similar to specified vector embeddings. However, the search results may not always be correct. You can include filtering conditions in a search request so that Zilliz Cloud conducts metadata filtering before conducting ANN searches, reducing the search scope from the whole collection to only the entities matching the specified filtering conditions.

Range Search [READ MORE]

A range search improves search result relevancy by restricting the distance or score of the returned entities within a specific range. This page helps you understand what range search is and the procedures to conduct a range search.

Grouping Search [READ MORE]

A grouping search allows Zilliz Cloud to group the search results by the values in a specified field to aggregate data at a higher level. For example, you can use a basic ANN search to find books similar to the one at hand, but you can use a grouping search to find the book categories that may involve the topics discussed in that book. This topic describes how to use Grouping Search along with key considerations.

Hybrid Search [READ MORE]

Hybrid Search refers to a search method that conducts multiple ANN searches simultaneously, reranks multiple sets of results from these ANN searches, and ultimately returns a single set of results. Using Hybrid Search can enhance the search accuracy. Zilliz Cloud supports conducting Hybrid Search on a collection with multiple vector fields.

Query [READ MORE]

In addition to ANN searches, Zilliz Cloud also supports metadata filtering through queries. This page introduces how to use Query, Get, and QueryIterators to perform metadata filtering.

Filtering [READ MORE]

This chapter explains how to perform filtering on scalar fields and applicable filtering operators.

Search Iterator [READ MORE]

The ANN Search has a maximum limit on the number of entities that can be recalled in a single query, and simply using basic ANN Search may not meet the demands of large-scale retrieval. For ANN Search requests where topK exceeds 16,384, it is advisable to consider using the SearchIterator. This section will introduce how to use the SearchIterator and related considerations.

Use Partition Key [READ MORE]

The Partition Key is a search optimization solution based on partitions. By designating a specific scalar field as the Partition Key and specifying filtering conditions based on the Partition Key during the search, the search scope can be narrowed down to several partitions, thereby improving search efficiency. This article will introduce how to use the Partition Key and related considerations.

Use mmap [READ MORE]

Memory mapping (Mmap) enables direct memory access to large files on disk, allowing Zilliz Cloud to store indexes and data in both memory and hard drives. This approach helps optimize data placement policy based on access frequency, expanding storage capacity for collections without impacting search performance. This page helps you understand how Zilliz Cloud uses mmap to enable fast and efficient data storage and retrieval.

Consistency Level [READ MORE]

As a distributed vector database, Zilliz Cloud offers multiple levels of consistency to ensure that each node or replica can access the same data during read and write operations. Currently, the supported levels of consistency include Strong, Bounded, Eventually, and Session, with Bounded being the default level of consistency used.

Metric Types [READ MORE]

Similarity metrics are used to measure similarities among vectors. Choosing an appropriate distance metric helps improve classification and clustering performance significantly.

Reranking [READ MORE]

Hybrid Search achieves more precise search results through multiple simultaneous ANN searches. Multiple searches return several sets of results, which require a reranking strategy to help merge and reorder the results and return a single set of results. This guide will introduce the reranking strategies supported by Zilliz Cloud and provide tips for selecting the appropriate reranking strategy.