ColBERTFuture Data Systems
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About
ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. These rich interactions allow ColBERT to surpass the quality of single-vector representation models while scaling efficiently to large corpora. The toolkit includes components for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. ColBERT integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts.
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About
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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Platforms Supported
Windows
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Mac
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Linux
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Cloud
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On-Premises
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iPhone
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iPad
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Android
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Chromebook
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
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Android
Not Supported
Chromebook
Not Supported
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Audience
Academic researchers and developers seeking a tool for implementing and evaluating listwise reranking with large language models
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Audience
Enterprise IT teams wanting a solution to implement scalable, secure, and efficient AI-powered search and document processing solutions across diverse data sources
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Support
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Online
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Support
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API
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API
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Pricing
Free
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Pricing
No information available.
Free Version
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Reviews/
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Training
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Training
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Company InformationFuture Data Systems
United States
github.com/stanford-futuredata/ColBERT
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Company InformationMixedbread
Founded: 2023
United States
www.mixedbread.com
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Integrations
Baseten
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Google Drive
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Microsoft SharePoint
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Notion
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Slack
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Integrations
Baseten
Supported
Google Drive
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Microsoft SharePoint
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Notion
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Slack
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