ColBERT

ColBERT

Future 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.

About

Local and secure AI on your desktop, ensuring comprehensive insights with complete data security and privacy. Experience unparalleled efficiency, privacy, and intelligence with our cutting-edge macOS-native app and advanced AI features. RAG can utilize data from a local knowledge base to supplement the large language model (LLM). This means you can keep sensitive data on-premises while leveraging it to enhance the model‘s response capabilities. To implement RAG locally, you first need to segment documents into smaller chunks and then encode these chunks into vectors, storing them in a vector database. These vectorized data will be used for subsequent retrieval processes. When a user query is received, the system retrieves the most relevant chunks from the local knowledge base and inputs these chunks along with the original query into the LLM to generate the final response. We promise lifetime free access for individual users.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Academic researchers and developers seeking a tool for implementing and evaluating listwise reranking with large language models

Audience

Enterprises and individuals requiring a tool to search, integrate, and display their local files and knowledge base

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Future Data Systems
United States
github.com/stanford-futuredata/ColBERT

Company Information

Klee
kleedesktop.com

Alternatives

TILDE

TILDE

ielab

Alternatives

Azure AI Search

Azure AI Search

Microsoft
RankLLM

RankLLM

Castorini
RankGPT

RankGPT

Weiwei Sun
BERT

BERT

Google
RoBERTa

RoBERTa

Meta

Categories

Categories

Integrations

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Codestral Mamba
Hugging Face
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Llama 3.2
Llama 3.3
Mathstral
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Mixtral 8x22B
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Integrations

Codestral
Codestral Mamba
Hugging Face
LangChain
Le Chat
Llama 2
Llama 3
Llama 3.2
Llama 3.3
Mathstral
Meta Pixel
Ministral 3B
Mistral 7B
Mistral AI
Mistral Large
Mistral NeMo
Mistral Small
Mixtral 8x22B
OpenAI
Pixtral Large
Claim ColBERT and update features and information
Claim ColBERT and update features and information
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