vision-is-all-you-need
V
Vision Is All You Need
Overview :
vision-is-all-you-need is a demonstration project showcasing the Vision RAG (V-RAG) architecture. The V-RAG architecture directly embeds PDF file pages (or other documents) into vectors using Vision Language Models (VLM), eliminating the need for cumbersome chunk processing. This technology enhances the efficiency and accuracy of document retrieval, especially when dealing with large datasets. Background information indicates that this is an innovative tool leveraging the latest AI technologies to improve document processing capabilities. The project is currently open-source and free to use.
Target Users :
Target audience includes businesses and researchers handling large volumes of document data, especially those who need to quickly retrieve information from documents. This product or technology is suitable for them as it significantly reduces document processing time, improves retrieval accuracy, and can be integrated into existing workflows.
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Use Cases
Businesses quickly retrieve key terms from contract documents using the V-RAG architecture.
Researchers use the system to find specific research results in academic papers.
Legal teams utilize it to retrieve relevant information from case files.
Features
Convert PDF file pages into images.
Use ColPali as VLM to obtain image embeddings.
Store embeddings in QDrant as a vector database.
Users submit queries through the V-RAG system.
Queries are embedded using VLM.
Search for similar embeddings in the vector database using the query embeddings.
Pass the best matching images for user queries and search results to a model that can understand images.
The model generates responses based on the queries and images.
How to Use
1. Ensure you have a Hugging Face account and log in using `transformers-cli login`.
2. Obtain an OpenAI API key and place it in the dotenv file.
3. Install Python version 3.11 or higher.
4. Install Modal using `pip install modal`.
5. Run `modal setup` to configure.
6. Start the demo with `modal serve main.py`.
7. Access the URL provided by Modal through your browser, appending `/docs` to use the API.
8. Click on the `POST /collections` endpoint to upload PDF files for indexing.
9. Use the `POST /search` endpoint to search for similar pages and get responses from the OpenAI API.
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