VisRAG
V
Visrag
Overview :
VisRAG is an innovative retrieval-augmented generation (RAG) process based on visual language models (VLMs). Unlike traditional text-based RAG, VisRAG embeds documents directly as images through a VLM, which enhances the generative capabilities of the VLM. This method maximizes the retention of data information from the original documents, eliminating the information loss introduced during parsing. The application of the VisRAG model on multimodal documents demonstrates its strong potential in information retrieval and enhanced text generation.
Target Users :
The primary audience for VisRAG includes researchers and developers, particularly those working in the fields of multimodal document processing, information retrieval, and enhanced text generation. As VisRAG can handle various types of data, including images and text, it is well-suited for scenarios that require extracting and generating information from complex documents, such as automated document summarization, content recommendation systems, and intelligent question-answering systems.
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Use Cases
In academic research, VisRAG can be used to retrieve and generate relevant abstracts from a vast amount of literature.
In content recommendation systems, VisRAG can retrieve and generate personalized content based on users' historical behavior and preferences.
In intelligent question-answering systems, VisRAG can improve accuracy and efficiency by retrieving relevant documents and generating precise answers.
Features
Directly embed documents as images to enhance document generation capabilities.
Utilize visual language models for document embedding, improving information retention.
Enhance document generation quality and relevance through retrieval augmentation.
Support the use of different VLMs for generation, such as MiniCPM-V 2.0 and GPT-4o.
Provide detailed training and evaluation scripts for easy reproduction and application.
Use gradient checkpointing during training to reduce memory usage.
Support multimodal documents, including PDFs and pseudo-queries generated by VLM.
How to Use
1. Install the necessary environment, such as Python 3.10.8 and CUDA Toolkit.
2. Clone the VisRAG repository and navigate to the project directory.
3. Install dependencies and the timm_modified library if needed.
4. Prepare the training dataset, which can be a public academic dataset or a synthetic dataset.
5. Run the training and evaluation process using the provided scripts and parameters.
6. Utilize the VisRAG model for document embedding and retrieval-augmented generation tasks.
7. Adjust model parameters and training configurations as needed to optimize performance.
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