VARAG

Visual-enhanced retrieval and generation system

CommonProductProgrammingRetrievalGeneration

VARAG is a system that supports various retrieval technologies, optimized for different use cases of text, image, and multimodal document retrieval. It simplifies traditional retrieval workflows by embedding document pages as images and enhances retrieval accuracy and efficiency through advanced visual language models. VARAG's primary advantage lies in its capability to handle complex visual and textual content, providing robust support for document retrieval.

Supports multiple retrieval technologies
including text
image
and multimodal document retrieval.
Simple RAG: Extracts text from documents using OCR technology for retrieval.
Vision RAG: Incorporates visual information for retrieval
employing the JinaCLIP model for cross-modal encoding.
ColPali RAG: Directly embeds document pages as images and encodes using the PaliGemma model.
Hybrid ColPali RAG: Combines image embedding with ColPali's late interaction mechanism for retrieval.
Offers an interactive playground to compare different RAG solutions.

\VARAG targets data scientists
machine learning engineers
and researchers who need to process and retrieve large volumes of document data. It is particularly suited for scenarios involving complex visual and textual content
such as legal documents
academic papers

Legal teams use VARAG to quickly retrieve relevant clauses from contract documents.
Researchers utilize VARAG to extract key information from a vast number of academic papers.
Business analysts leverage VARAG to analyze charts and data in market reports.

Clone the repository: Use the git command to clone the VARAG GitHub repository.
Set up the environment: Create and activate a virtual environment using Conda.
Install dependencies: Use pip or poetry to install the required Python packages.
Run the demo: Execute the demo.py script and run it locally or on Google Colab with the --share parameter.
Index data sources: Utilize the classes and methods provided by VARAG to index data sources.

Visit

VARAG Visit Over Time

Monthly Visits

Bounce Rate

42.87%

Page per Visit

1.0

Visit Duration

00:00:00

VARAG Visit Trend

VARAG Visit Geography

VARAG Traffic Sources

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VARAG

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