

VARAG
Overview :
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.
Target Users :
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, and business reports.
Use Cases
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.
Features
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.
Supports local execution and demonstrations on Google Colab.
How to Use
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.
Perform searches: Input queries and execute searches to obtain retrieval results.
Utilize results: Use the retrieval results for further analysis or response generation.
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