The Visual Rag Model Is Coming! From Text to Images, How AI "Understands" The World

1. What is RAG?

RAG (Retrieval-Augmented Generation, Retrieval-Augmented Generation) is an important breakthrough in the field of artificial intelligence in recent years. Traditional language models rely on pre-trained data to generate text, while RAG enhances generation capabilities by retrieving external sources of information. Simply put, it can find documents or data related to the question from an external database, and then combine this information to generate more accurate, timely, and more context-sensitive answers.

For example, if you ask a traditional language model "What is the weather today?" it can only give a general answer based on pre-trained data. But if you use the RAG model, it can retrieve the latest data from a real-time weather website and then give an accurate, weather forecast for your area. This capability makes RAGs more intelligent and reliable when dealing with complex problems.

2. Vision RAG: Let AI "understand" the world

Vision RAG is an extension of the RAG model, which incorporates visual data (such as images, charts, videos, etc.) into the processing scope. Unlike traditional RAG models that mainly deal with text, Vision RAG uses visual language models (VLMs) to index, retrieve and process visual information. This means it can handle complex documents containing text and visual content, such as PDF files.

Vision The core advantage of RAG is that it is able to generate answers that are not only textually correct, but also visually rich and accurate. For example, you can upload a scientific report containing charts and text and ask "What does this chart mean?" Vision RAG will not only understand the content of the chart, but also give a complete explanation based on text information.

3. Characteristics of Vision RAG

Vision RAG has made AI more intelligent and efficient when processing multimodal data. Here are some of its main features:

1. Multimodal retrieval and generation

Vision RAG can process text and visual information in documents at the same time. This means it can answer questions about images, tables, and more, not just text. For example, you can ask "What is the style of architecture in this picture?" It will give the answer in combination with the text information in the picture and the document.

2. Direct visual embedding

Unlike traditional OCR (optical character recognition) or manual parsing, Vision RAG uses visual language models to embed visual information directly. This method preserves semantic relationships and context, making retrieval and understanding more accurate.

3. Unified cross-modal search

Vision RAG is capable of semantically meaningful searches and searches in a single vector space, covering mixed modal content. Whether you ask about text or images in a document, it can find the answer within a unified framework.

These features enable Vision RAG to support more natural and flexible interaction methods. Users can ask questions in natural language, and the model extracts answers from text and visual sources to provide more comprehensive information.

4. How to use Vision RAG?

To integrate the functionality of Vision RAG into our work, we can use a model called localGPT-vision. localGPT-vision is a powerful, end-to-end visual RAG system that directly processes visual document data (such as scanned PDFs or images) without relying on OCR.

Currently, localGPT-vision supports the following visual language models:

• Qwen2-VL-7B-Instruct
• LLAMA-3.2-11B-Vision
• Pixtral-12B-2409
• Molmo-&B-O-0924
• Google Gemini
• OpenAI GPT-4o
• LLAMA-32 with Ollama

localGPT-Vision architecture

localGPT-Vision's system architecture mainly consists of two parts:

1. Visual document search

Colqwen and ColPali are visual encoders designed specifically to understand the image representation of documents. During the indexing process, the document page will be converted into image embedding, and the user's problems will be embedded and matched with the indexed page embedding. This method allows retrieval to be based not only on text, but also on visual layout, charts and other content.

2. Response generation

The page with the highest matching degree to the document will be submitted as an image to the Visual Language Model (VLM), which generates context-related answers by decoding visual and text signals.

Note : The quality of the answer depends to a large extent on the VLM used and the resolution of the document image.

This design eliminates the complex text extraction process and directly understands the document from a visual perspective, without the need to choose an embedding model or retrieval strategy like traditional RAG systems.

LocalGPT-Vision Features

• Interactive chat interface : Users can upload documents and ask questions through the chat interface.
• End-to-end visual RAG : Completely visual-based retrieval and generation, no OCR is required.
• Document upload and index : Support uploading PDFs and images and indexing through ColPali.
• Permanent index : All indexes are stored locally and are automatically loaded after restarting.
• Model selection : You can select a variety of VLMs, such as GPT-4, Gemini, etc.
• Session Management : You can create, rename, switch and delete chat sessions.

V. Practical operation of localGPT-Vision

Let's see how localGPT-Vision works with a simple example.

In the video below, you can see the running process of the model. On the left side of the screen is a settings panel where you can select the VLM model used to handle PDFs. After selecting the model, upload the PDF file and the system will start indexing. After the index is complete, you just need to enter a question about the PDF and the model will generate correct and relevant answers based on the content.

Because this setting requires GPU to achieve optimal performance, I shared a Google Colab notebook, which contains the implementation of the entire model. You only need a model API key (such as Gemini, OpenAI, or others) and an Ngrok key to deploy the application publicly.

6. Application scenarios of Vision RAG

The emergence of Vision RAG has brought new possibilities to many fields. The following are some typical application scenarios:

1. Medical imaging

Vision RAG can combine medical imaging and medical records to help doctors make smarter and more accurate diagnosis. For example, it can analyze text information in X-rays and medical records, providing more comprehensive diagnostic suggestions.

2. Document search

Vision RAG is able to extract information from documents containing text and visual content and generate summary. This is very useful for researchers and professionals who can quickly find the key information they need.

3. Customer Support

Vision RAG can solve problems through photos uploaded by users. For example, customers can upload photos of equipment failures, and the model provides solutions in combination with text descriptions.

4. Education

Vision RAG can help teachers and students better understand complex concepts. It can provide students with a personalized learning experience through a combination of charts and text.

5. e-commerce

Vision RAG can generate more accurate product recommendations based on product pictures and descriptions. For example, if a user uploads a picture of his favorite clothing, the model can recommend products of similar styles.

7. Summary

Vision RAG is an important advance in the field of artificial intelligence. It allows AI to not only "understand" text, but also "understand" images and charts. With the widespread use of Vision RAG models, we can expect smarter, faster and more accurate solutions. It not only has great potential in areas such as education, healthcare, etc., but also unlocks new possibilities for innovation and insight in many other areas.

Now, AI has begun to understand and perceive the world in a human way. The emergence of Vision RAG makes us look forward to the future AI. If you are interested in Vision RAG, you might as well try localGPT-vision and experience the charm of multimodal AI for yourself