Multimodal Retrieval Augmented Generation (Multimodal RAG)

Multimodal Retrieval-Augmented Generation (MM-RAG) is a technique that enhances generative models by using multiple data such as text, images, audio and video into the learning and generation process. This approach is beneficial when relying on single data like only using text data is insufficient for understanding and generation.

Integrating retrieval-based learning (RAG) with generative capabilities enables AI models to provide more informed, context-aware and accurate responses.

Importance of Multimodal RAG

Multimodal RAG is useful where the data is available from diverse data sources. This significantly improves model performance because:

1. Contextual Understanding: By retrieving information from multiple datatypes it ensures AI systems can make more accurate inferences about the input query using both textual and non-textual data.
2. Content Generation: By using various modalities like text, images, audio, etc enriches the content generation process, enabling AI to produce highly relevant, accurate and engaging responses.
3. Acuraccy: Instead of generating responses based on incomplete or unsupported data, model can retrieve and reference from multiple sources for better response accuracy.

Architecture of MM-RAG

1. RAG Pipeline: controls the workflow. It pulls source documents (or user uploads) and hands off any embedded images to the next component.
2. Image Extractor: receives raw inputs, isolates each image and forwards them to the Metadata Generator.
3. Metadata Generator: creates a natural‑language caption and any other metadata for each image. It pushes the raw image files into an Object Storage or CDN then retrieves their public URLs.
4. Object Storage / CDN : stores the original images and returns stable URLs which the pipeline uses for downstream embedding.
5. Text Embedding Model: takes the captions or image URLs plus prompts and converts them into fixed‑size vectors.
6. Vector Database: inserts the embeddings with associated metadata and URLs into FAISS, ChromaDB, etc making them instantly searchable for later retrieval.

Implementation of Multimodal RAG

1. Install Required Libraries

First we will install the necessary libraries like transformers, faiss-cpu, torch m sentence-transformers, PIL and OpenCv .

!pip install transformers faiss-cpu torch sentence-transformers PIL opencv-python

2. Import Necessary Libraries

We import the required libraries for working with images, text and embeddings.

import torch
import faiss
import cv2
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration, AutoModel, AutoTokenizer
from sentence_transformers import SentenceTransformer

3. Load Models for Image and Text Processing

We’re using the BlipProcessor and BlipForConditionalGeneration to load a pretrained BLIP model that generates captions from images. Then we load a SentenceTransformer (all-MiniLM-L6-v2) to convert those captions or any text into embeddings for retrieval or similarity tasks.

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
image_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")

text_model = SentenceTransformer("all-MiniLM-L6-v2")

4. Prepare the Multimodal Dataset

Here we define a sample dataset with both textual descriptions and associated image files. Provide the image paths in the dataset_images list for the choosen images. These are then converted into embeddings.

dataset_texts = ["A cat sitting on a table", "A dog playing in the park", "A red sports car", "A bowl of fresh fruit"]
dataset_images = ["cat.jpg", "dog.jpg", "car.jpg", "fruit.jpg"] 

text_embeddings = text_model.encode(dataset_texts, convert_to_tensor=True)

image_embeddings = []
for img_path in dataset_images:
    image = Image.open(img_path).convert("RGB")
    inputs = processor(image, return_tensors="pt")
    img_emb = image_model.generate(**inputs)
    image_embeddings.append(img_emb)

image_embeddings = torch.cat(image_embeddings)

5. Build FAISS Index for Efficient Retrieval

To enable fast retrieval of both text and image embeddings we use FAISS (Facebook AI Similarity Search) which is a library optimized for efficient similarity search.

data_embeddings = torch.cat((text_embeddings, image_embeddings)).detach().numpy()

index = faiss.IndexFlatL2(data_embeddings.shape[1])
index.add(data_embeddings)

6. Perform a Query Search

Finally we perform a search query based on a text input. This search will retrieve the most relevant Multimodal results (text and image).

query_text = "A cute kitten"
query_embedding = text_model.encode([query_text], convert_to_tensor=True).detach().numpy()

distances, indices = index.search(query_embedding, k=3)

print("Top 3 nearest MultiModal results:", indices)

Output:

Top 3 nearest Multimodal results: [[0, 1, 2]]

Indices [0, 1, 2] correspond to the most relevant results from Multimodal dataset based on the input query. Each index represents a combination of text and image data retrieved from the dataset.

Applications of Multimodal RAG

• Healthcare: In medical diagnostics it can analyze both textual patient reports and medical images like X-rays, MRIs to provide more accurate diagnoses and treatment recommendations.
• E-Commerce: It improves product search by matching both textual descriptions and images. For example, customers can search for products using either text-based queries or visual inputs like uploading a product image.
• Education: Supports interactive and immersive learning experiences by combining text, diagrams and videos to explain complex concepts in a engaging manner.
• Legal and Finance: Enhances decision-making by retrieving relevant legal case studies, financial reports and charts enabling professionals to make informed decisions based on both textual and visual data.

Multimodal RAG combines retrieval and generation across text, images and more to produce context-aware and accurate responses and can be used across various industries.