Build Multimodal RAG using Llama 3.2 11B

RAGMultimodal

Build Multimodal RAG using Llama 3.2 11BThis blog explores how to build a multimodal RAG system using Llama-3.2-11B-Vision, which combines the strengths of both text and image modalities.Read more

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Introduction

Retrieval-Augmented Generation (RAG) has revolutionized the field of natural language processing by enhancing the capabilities of large language models (LLMs) through the integration of external knowledge sources. In this blog, we explore the process of building a multimodal RAG system using the Llama-3.2-11B-Vision model, which combines the strengths of both text and image modalities.

Overview

The architecture of our multimodal RAG system is depicted in the following Mermaid diagram:

The following process describes how the user interacts with the system:

Image input: The user provides an image as input to the system.
Llama-3.2-11B-Vision-Instruct: The image is processed using the Llama-3.2-11B-Vision-Instruct model, which generates a caption that describes the image.
Text input: The user provides a text query or prompt to the system.
Llama-3.2-11B: The text input is processed using the Llama-3.2-11B model, which generates a text embedding.
Text embedding: The image caption and text input are combined to form a text embedding, which is used to retrieve relevant information from the vector database.
Vector database: The vector database stores a collection of text embeddings that correspond to the knowledge base.
RAG model: The RAG model uses the retrieved information to generate a response to the user's query.

Implementation Details

Step 1: Data Preparation

The first step in building the multimodal RAG system is to prepare the dataset. We start by downloading 5000 images of clothing items and labeling them using the Llama-3.2-11B-Vision-Instruct model.

Step 2: Cleaning Labels

The labels generated by the Llama-3.2-11B-Vision-Instruct model are cleaned and preprocessed to remove any noise or inconsistencies.

Step 3: Building Vector DB and RAG Pipeline

The cleaned labels are then used to create a vector database using a text embedding model. The RAG pipeline is built using the Llama-3.2-11B model, which generates responses based on the retrieved information from the vector database.

Step 4: Gradio App

The final step is to create a Gradio app that integrates the RAG pipeline with a user-friendly interface. The app allows users to upload images and query the system using text prompts.

Experimental Results

Our experiments demonstrate the effectiveness of the multimodal RAG system in generating accurate and relevant responses to user queries. The results show that the system outperforms traditional RAG systems that rely solely on text input.

Multimodal input: The use of both image and text input enhances the capabilities of the RAG system.
Llama-3.2-11B model: The Llama-3.2-11B model is effective in generating accurate and relevant responses to user queries.
Vector database: The use of a vector database enables efficient retrieval of relevant information.

Related Resources

Multi-Modal-RAG: Implementation of multimodal RAG using Llama-3.2-11B model
Llama-3.2-11B-Vision-Instruct: Documentation on Llama-3.2-11B-Vision-Instruct model
Lance-db: Vector database used in the implementation

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