Compare the Top Reranking Models that integrate with Notion as of June 2025

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

What are Reranking Models for Notion?

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 Notion currently available using the table below. This list is updated regularly.

  • 1
    Ragie

    Ragie

    Ragie

    Ragie streamlines data ingestion, chunking, and multimodal indexing of structured and unstructured data. Connect directly to your own data sources, ensuring your data pipeline is always up-to-date. Built-in advanced features like LLM re-ranking, summary index, entity extraction, flexible filtering, and hybrid semantic and keyword search help you deliver state-of-the-art generative AI. Connect directly to popular data sources like Google Drive, Notion, Confluence, and more. Automatic syncing keeps your data up-to-date, ensuring your application delivers accurate and reliable information. With Ragie connectors, getting your data into your AI application has never been simpler. With just a few clicks, you can access your data where it already lives. Automatic syncing keeps your data up-to-date ensuring your application delivers accurate and reliable information. The first step in a RAG pipeline is to ingest the relevant data. Use Ragie’s simple APIs to upload files directly.
    Starting Price: $500 per month
  • 2
    Mixedbread

    Mixedbread

    Mixedbread

    Mixedbread is a fully-managed AI search engine that allows users to build production-ready AI search and Retrieval-Augmented Generation (RAG) applications. It offers a complete AI search stack, including vector stores, embedding and reranking models, and document parsing. Users can transform raw data into intelligent search experiences that power AI agents, chatbots, and knowledge systems without the complexity. It integrates with tools like Google Drive, SharePoint, Notion, and Slack. Its vector stores enable users to build production search engines in minutes, supporting over 100 languages. Mixedbread's embedding and reranking models have achieved over 50 million downloads and outperform OpenAI in semantic search and RAG tasks while remaining open-source and cost-effective. The document parser extracts text, tables, and layouts from PDFs, images, and complex documents, providing clean, AI-ready content without manual preprocessing.
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