💻 programming

VARAG

Visually enhanced retrieval and generation system

#multimodal
#Document processing
#OCR
#generate
#Search
VARAG

Product Details

VARAG is a system that supports multiple retrieval technologies, optimized for different use cases of text, image and multi-modal document retrieval. It simplifies the traditional retrieval process by embedding document pages as images and uses advanced visual language models for encoding, improving retrieval accuracy and efficiency. The main advantage of VARAG is its ability to handle complex visual and textual content, providing powerful support for document retrieval.

Main Features

1
Supports a variety of retrieval technologies, including text, image and multi-modal document retrieval.
2
Simple RAG: Extract text from documents and retrieve them through OCR technology.
3
Vision RAG: Combines visual information for retrieval and uses the JinaCLIP model for cross-modal encoding.
4
ColPali RAG: Directly embed document pages as images, encoded using the PaliGemma model.
5
Hybrid ColPali RAG: Combines image embedding and ColPali’s late interaction mechanism for retrieval.
6
An interactive playground is provided where different RAG solutions can be compared.
7
Supports local running and demo on Google Colab.

How to Use

1
Clone the repository: Use the git command to clone VARAG's GitHub repository.
2
Set up the environment: Use Conda to create and activate a virtual environment.
3
Install dependencies: Use pip or poetry to install the required Python packages.
4
Run the demo: Execute the demo.py script and run it locally or on Google Colab with the --share parameter.
5
Index data source: Use the classes and methods provided by VARAG to index the data source.
6
Perform a search: Enter a query and perform a search to obtain search results.
7
Use results: Use the search results for further analysis or to generate a response.

Target Users

VARAG is targeted at data scientists, machine learning engineers, and researchers who need to process and retrieve large amounts of document data. VARAG is particularly suitable for scenarios where complex visual and textual content needs to be processed, such as legal documents, academic papers, and business reports.

Examples

Legal teams use VARAG to quickly retrieve relevant clauses in contract documents.

Researchers use VARAG to extract key information from a large number of academic papers.

Business analysts use VARAG to analyze charts and data in market reports.

Quick Access

Visit Website →

Categories

💻 programming
› AI search engine
› AI data mining

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