Adding a Vision RAG Notebook to Llama Recipes

[adithya-s-k]

🚀 The feature, motivation and pitch

I propose the addition of a comprehensive notebook that demonstrates the construction of a Vision-based Retrieval-Augmented Generation (RAG) system using the following components:

• Llama 3.2 11B Vision model: The primary Vision Language Model (VLM) for multimodal data understanding.
• ColPali/Colqwen: For direct vision embedding generation from document images, capturing contextualized image embeddings.
• LanceDB: As the vector store, to manage and retrieve embeddings with optimal performance.

This feature will address the increasing need for seamless integration of both visual and textual data in RAG systems, enhancing their ability to process and retrieve relevant information from multimodal sources.

The motivation behind this proposal stems from our development work on VARAG and from use cases involving document analysis where visual components such as figures, diagrams, and complex images are as essential as textual data. By leveraging ColPali, which provides direct and contextually rich embeddings from visual inputs, this system bridges the gap between text-based and image-based data retrieval. Such integration not only enhances retrieval accuracy but also empowers systems to interpret and interact with multimodal data in a more sophisticated and nuanced way.

The notebook will serve as a hands-on guide, offering step-by-step setup instructions for configuring the environment, integrating ColPali for image embedding generation, and utilizing LanceDB to store and retrieve embeddings efficiently. Users will gain practical insights into implementing a RAG system tailored for vision-based tasks around the Llama 3.2 11B Vision model.

Alternatives

While developing this proposal, I explored alternatives that focus solely on text-based representations, which often rely on Optical Character Recognition (OCR) or layout analysis. However, such approaches can miss valuable visual information. The integration of ColPali with LanceDB allows for simultaneous handling of text and visual data, bypassing the need for complex preprocessing while maintaining high retrieval fidelity.

Additional context

• ColPali/Colqwen: This vision embedding model generates embeddings directly from images, allowing for richer representation of visual content in queries.
• LanceDB: This open-source vector database is optimized for efficient handling of multimodal embeddings and supports rapid retrieval, even in large-scale deployments.
• Llama 3.2 11B Vision Model: A VLM specifically designed for tasks requiring a deep understanding of both text and images, including image recognition, captioning, and visual reasoning.

This notebook will showcase the combined capabilities of these technologies, enabling users to explore and implement their own vision-based RAG systems with code examples, insights, and practical use cases.

Thanks for considering this proposal!

[adithya-s-k]

@HamidShojanazeri @wukaixingxp
I wanted to follow up on this proposal regarding the addition of a Vision RAG Notebook to the Llama Recipes repository. This feature could provide significant value by enabling users to build advanced multimodal Retrieval-Augmented Generation systems, integrating both textual and visual data seamlessly.

If there's any additional information or clarification needed to move forward with this, I'd be happy to provide it. Additionally, I would greatly appreciate it if this issue could be assigned for further development.

Looking forward to your thoughts!

[wukaixingxp]

Great idea! We had a similar vision RAG example in llama-stack-app. It will be great if you can create a PR for this recipe and I am more than happy to take a look and review it!

[adithya-s-k]

Thank you for the positive feedback @wukaixingxp! I'm excited to create this PR for the Vision RAG recipe.

Here's the planned structure for the notebook:

Notebook Structure: "Building a Vision-based RAG System with Llama 3.2 11B Vision"

1. Introduction and Setup

   • Overview of Vision RAG systems and their applications
   • Environment configuration and dependency installation
   • Brief explanation of the components (Llama 3.2 11B Vision, ColPali/Colqwen, LanceDB)

2. Data Preparation

   • Loading and preprocessing document images
   • Creating a sample multimodal dataset (mix of text documents and images)
   • Data validation and exploratory visualization

3. Vision Embedding Generation

   • Implementing ColPali/Colqwen for direct vision embedding extraction
   • Comparing with traditional OCR-based approaches
   • Visualization of embedding spaces for different image types

4. Vector Database Configuration

   • Setting up LanceDB for multimodal vector storage
   • Indexing strategies for efficient retrieval
   • Embedding persistence and management

5. RAG System Integration

   • Connecting Llama 3.2 11B Vision model with the vector store
   • Implementing the retrieval pipeline
   • Query processing for mixed text-image inputs

6. Evaluation and Testing

   • Sample queries and retrieval examples
   • Performance metrics and benchmarking
   • Comparison with text-only RAG systems

7. Advanced Use Cases

   • Document analysis with complex visual elements
   • Cross-modal retrieval scenarios
   • Handling diverse document formats

8. Optimization and Best Practices

   • Performance tuning for large-scale deployments
   • Memory management considerations
   • Trade-offs between accuracy and speed

Each section will include complete code examples, explanations suitable for beginners, and practical insights for implementation. Let me know if there are any changes to the stack

I'll create the PR by tomorrow EOD. Looking forward to your review!