Compare the Top Reranking Models that integrate with Google Sheets as of July 2025

This a list of Reranking Models that integrate with Google Sheets. Use the filters on the left to add additional filters for products that have integrations with Google Sheets. View the products that work with Google Sheets in the table below.

What are Reranking Models for Google Sheets?

Reranking models are AI models in information retrieval systems that refine the order of retrieved documents to better match user queries. Typically employed in two-stage retrieval pipelines, these models first generate a broad set of candidate documents and then reorder them based on relevance. They utilize sophisticated techniques, such as deep learning models like BERT, T5, and their multilingual variants, to capture complex semantic relationships between queries and documents. The primary advantage of reranking models lies in their ability to improve the precision of search results, ensuring that the most pertinent documents are presented to the user. However, this enhanced accuracy often comes at the cost of increased computational resources and potential latency. Despite these challenges, rerankers are integral to applications requiring high-quality information retrieval, such as question answering, semantic search, and recommendation systems. Compare and read user reviews of the best Reranking Models for Google Sheets currently available using the table below. This list is updated regularly.

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    Cohere Rerank
    Cohere Rerank is a powerful semantic search tool that refines enterprise search and retrieval by precisely ranking results. It processes a query and a list of documents, ordering them from most to least semantically relevant, and assigns a relevance score between 0 and 1 to each document. This ensures that only the most pertinent documents are passed into your RAG pipeline and agentic workflows, reducing token use, minimizing latency, and boosting accuracy. The latest model, Rerank v3.5, supports English and multilingual documents, as well as semi-structured data like JSON, with a context length of 4096 tokens. Long documents are automatically chunked, and the highest relevance score among chunks is used for ranking. Rerank can be integrated into existing keyword or semantic search systems with minimal code changes, enhancing the relevance of search results. It is accessible via Cohere's API and is compatible with various platforms, including Amazon Bedrock and SageMaker.
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