ColBERTFuture Data Systems
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RankLLMCastorini
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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
RankLLM is a Python toolkit for reproducible information retrieval research using rerankers, with a focus on listwise reranking. It offers a suite of rerankers, pointwise models like MonoT5, pairwise models like DuoT5, and listwise models compatible with vLLM, SGLang, or TensorRT-LLM. Additionally, it supports RankGPT and RankGemini variants, which are proprietary listwise rerankers. It includes modules for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. RankLLM 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 (MoE) models. The toolkit supports various backends, including SGLang and TensorRT-LLM, and is compatible with a wide range of LLMs.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
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Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
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
Academic researchers and developers seeking a solution offering tools for implementing and evaluating listwise reranking with large language models
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Support
Phone Support
Not Supported
24/7 Live Support
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Online
Supported
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Support
Phone Support
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24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
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In Person
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationFuture Data Systems
United States
github.com/stanford-futuredata/ColBERT
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Company InformationCastorini
Canada
github.com/castorini/rank_llm/
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Categories |
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Integrations
Gemini
Not Supported
Gemini Enterprise
Not Supported
Llama
Not Supported
Mistral AI
Not Supported
NVIDIA TensorRT
Not Supported
OpenAI
Not Supported
Python
Not Supported
Qwen
Not Supported
RankGPT
Not Supported
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Integrations
Gemini
Supported
Gemini Enterprise
Supported
Llama
Supported
Mistral AI
Supported
NVIDIA TensorRT
Supported
OpenAI
Supported
Python
Supported
Qwen
Supported
RankGPT
Supported
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