Multimodal Retrieval Augmented Generation (Multimodal RAG) (original) (raw)

Last Updated : 8 Apr, 2026

Multimodal Retrieval-Augmented Generation combines text, images, audio and video with retrieval to enhance generative models, enabling more accurate, context aware and informative responses beyond single modality systems.

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Flow of MM-RAG model

Multimodal RAG improves performance by using diverse data sources, enabling better understanding and more accurate responses.

  1. Enhances contextual understanding by combining textual and non textual data.
  2. Improves content generation with richer, more relevant and engaging outputs.
  3. Increases accuracy by retrieving and using information from multiple sources.

Architecture

Multimodal RAG follows a structured pipeline that processes multiple data types and converts them into embeddings for efficient retrieval and generation.

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Components of Multimodal-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

1. Install Required Libraries

pip install transformers faiss-cpu torch sentence-transformers pillow opencv-python

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2. Import Required Libraries

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

Python `

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

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3. Load Image and Text Models

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")

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**Output:

output2

Output

4. Prepare Multimodal Dataset

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 = [ "/content/cat2.jpg", "/content/dog2.jpg", "/content/car2.jpg", "/content/fruits2.jpg" ]

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

captions = [] for img_path in dataset_images: image = Image.open(img_path).convert("RGB") inputs = processor(image, return_tensors="pt")

output = image_model.generate(**inputs)
caption = processor.decode(output[0], skip_special_tokens=True)

captions.append(caption)

image_embeddings = text_model.encode(captions, convert_to_tensor=True)

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5. Build FAISS Index for Efficient Retrieval

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

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

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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)

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**Output:

Top 3 nearest MultiModal results: [[0 4 2]]

Indices [0, 4, 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.

Download full code from here.

Applications

Limitations