Multimodal Retrieval-Augmented Generation (RAG)

Your go-to guide to understand MultiModal RAG in detail.

Ever tried searching for something but couldn’t put it into words? Maybe you saw an image of a product but had no clue what it was called. Or you heard a tune but couldn’t remember the lyrics. Traditional AI retrieval systems struggle because they rely on a single modality just text or just images. But in the real world, information comes in multiple forms.

In today’s edition of Where’s The Future in Tech newsletter we will dive deep into MultiModal RAGs architecture, functionality, and applications. I’ll walk you through everything, from the basics to advanced architectural details so you can understand how this works and why it’s a game-changer for AI. Let’s get started! 🚀

The Problem With Single-Modality AI Retrieval

Single-modality retrieval falls short in real-world scenarios where context spans multiple forms. It’s like trying to understand a book by only reading the chapter titles or watching a movie without sound. The information is incomplete, leading to inaccurate results, poor user experiences, and limited AI capabilities.

Visual information-seeking queries: These queries are unanswerable with text-only retrieval and require retrieving and reasoning over images.

This limitation creates a significant gap between how humans interact with information and how AI processes it. Imagine trying to describe a complex painting using only words or summarizing an audio clip without ever listening to it. The disconnect between modalities often results in incomplete or inaccurate AI-generated responses. This is where Multi-Modal RAG changes the game. Instead of limiting retrieval to a single data type, it allows AI to search and generate responses across multiple formats text, images, audio, and even video. Essentially, it enables AI to interpret and connect different forms of information, making it significantly more powerful and intuitive.

What Is Multi-Modal RAG?

At its core, Multi-Modal RAG enhances the standard Retrieval-Augmented Generation (RAG) framework by integrating multiple data types into the retrieval and generation pipeline. In traditional RAG systems, an AI model retrieves relevant text documents before generating responses. Multi-Modal RAG takes this a step further by including non-textual sources like images, audio, and videos.

In simpler terms:

• RAG: Combines external knowledge retrieval with text-based generation.

  

  RAG Architecture

• Multimodal RAG: Extends this to handle diverse data types like images, videos, and audio alongside text.

  

Illustration of text-only RAG versus multi-modal RAG

For example, a user could upload an image of a plant, and instead of relying solely on textual search, the AI would process the visual input, identify key characteristics, and then fetch relevant botanical texts or similar images. Similarly, if someone uploads an audio recording, the AI can transcribe the speech, extract key insights, and cross-reference them with related textual documents or video content. This ability to merge different data streams dramatically enhances the model’s contextual understanding, allowing for more precise and insightful responses.

Why Is This Important?

• Human-like understanding: Humans process information across multiple modalities (e.g., reading text while observing visuals). MM-RAG mimics this ability.

• Improved accuracy: By grounding responses in multimodal data, MM-RAG reduces hallucinations (false outputs) and enhances context-awareness.

• Broader applications: From healthcare to retail to education, MM-RAG enables AI to solve real-world problems that require multimodal reasoning.

Architecture Of MultiModal RAG

Multimodal Retrieval-Augmented Generation is an advanced AI framework that extends the capabilities of traditional Retrieval-Augmented Generation (RAG) by incorporating multiple data modalities text, images, audio, and video. Its architecture is designed to handle diverse data formats, retrieve relevant information from multimodal sources, and generate coherent responses grounded in retrieved context. Let’s break this down into its core components and processes in detail.

1. Core Components of MM-RAG Architecture

The MM-RAG pipeline consists of three primary components: retrieval, fusion, and generation. Each component plays a critical role in enabling the system to process multimodal inputs and outputs effectively.

A. Multimodal Encoders: Translating inputs into Vector representations

The first step in the MM-RAG pipeline is encoding input data into vector embeddings that reside in a shared high-dimensional space. This allows the system to compare and reason across different modalities.

• Text encoders: Models like BERT, T5, or GPT encode text into semantic embeddings that capture linguistic meaning. These embeddings are dense vectors representing the relationships between words or phrases.

• Image encoders: CLIP (Contrastive Language-Image Pretraining) is widely used for image encoding. It aligns textual and visual embeddings by training on paired data (e.g., captions and images), enabling cross-modal understanding.

• Audio encoders: Whisper or Wav2Vec2 encodes audio signals into embeddings by extracting features like pitch, tone, and phonemes, making it possible to integrate speech-based inputs.

• Video encoders: Videos are processed frame-by-frame using image encoders like CLIP for visual data and models like Whisper for audio tracks. Temporal relationships between frames are often captured using transformers or recurrent networks.

MM RAG Architecture

Why shared Embedding Space matters?

All these encoders map their respective modalities into a shared embedding space, where semantically similar inputs regardless of modality are placed closer together. For example:

• A query like "sunset" will have a vector embedding close to both an image of a sunset and a description of it.

B. Vector Database

Once inputs are encoded into embeddings, they are stored in a vector database a specialized storage system optimized for similarity searches. Examples include FAISS, Pinecone, or Milvus.

Key features of Vector databases:

1. Similarity search: Finds vectors that are most similar to the query embedding using distance metrics like cosine similarity or Euclidean distance.

2. Multimodal storage: Stores embeddings from text, images, audio, and video in a unified manner.

3. Scalability: Handles large-scale datasets efficiently, enabling real-time retrieval across millions of entries.

Workflow:

1. A user query (e.g., “How do I fix this?” + image of a broken gadget) is encoded into a vector.

2. The vector database retrieves relevant multimodal content (e.g., repair manuals, how-to videos).

3. The retrieved content serves as context for the next stage.

C. Cross-Modal Retrieval: Finding relevant Data across Modalities

This step involves retrieving relevant information from the vector database across multiple modalities simultaneously.

How it works:

1. The system encodes the query (text + additional modalities like images/audio) into a vector.

2. The retriever matches this query vector against stored embeddings in the database.

3. Top-ranked results regardless of their format are selected based on similarity scores.

Example:

For a query like “What’s wrong with my car?” + audio clip of engine noise:

• The retriever might return:

  • Textual descriptions from car repair manuals.

  • Videos demonstrating similar engine issues.

  • Audio recordings of comparable engine sounds.

This cross-modal retrieval ensures that all relevant information is considered during response generation.

D. Fusion Mechanism: Combining Multimodal context

Before generating a response, MM-RAG fuses the retrieved multimodal content with the original query to create a unified context representation.

Techniques used:

1. Cross-Attention mechanisms: Allows the model to focus on specific parts of each modality when combining them.

2. Contrastive learning: Ensures alignment between modalities by minimizing differences between semantically related embeddings.

3. Tokenization & Concatenation: Converts multimodal inputs into tokenized sequences that can be processed by generative models.

E. Generative Models: Synthesizing responses

The final step involves generating an output based on the fused context using large multimodal language models (MLLMs). These models are extensions of traditional LLMs (e.g., GPT-4) but are trained to handle multimodal inputs.

Key features:

1. Multimodal input handling: Accepts text, images, audio, and video as input.

2. Grounded generation: Generates responses that are factually consistent with retrieved content.

3. Flexible outputs: Can produce text-based answers, captions for images/videos, or even new images/videos based on input prompts.

Example:

For a query about assembling furniture with an accompanying image of parts:

• The model might generate step-by-step instructions tailored to the specific parts shown in the image.

How MultiModal RAG works: A Step-by-Step Guide

Here’s a detailed walkthrough of the MM-RAG workflow:

1. You Ask a Question

   • “Why does my car make this noise?” + [audio clip of engine squealing].

2. The System Encodes everything

   • Your audio becomes a vector. So does every manual, video tutorial, and forum post in its database.

3. Retrieval phase

   • It finds vectors similar to your query: maybe a video titled “Fixing Belt Squeaks” and a forum thread about alternator issues.

4. Fusion & generation

   • The AI blends these sources, then generates:
     “Your squeal likely comes from a worn serpentine belt (see timestamp 2:15 in the video). Check for cracks replacement costs ~$150.”

Beginners Hands on with MultiModal RAG

Want to experiment with MM-RAG? Here’s a simple starter recipe:

1. Use CLIP for image embeddings. Load an OpenCLIP model and process images into vector representations.

2. Pair with a vector database for retrieval. Store image embeddings in Pinecone or FAISS for efficient search.

3. Plug into a generative model for response generation. Use GPT-4V or a fine-tuned LLM to generate multimodal responses.

# Sample code to encode an image with CLIP

import torch
import open_clip
from PIL import Image
import requests
from io import BytesIO

# Load CLIP model
device = "cuda" if torch.cuda.is_available() else "cpu"
model = open_clip.create_model("ViT-B-32", pretrained="openai").to(device)
tokenizer = open_clip.get_tokenizer("ViT-B-32")
transform = open_clip.image_transform(model.visual.image_size, is_train=False)

# Load image from URL
image_url = "IMAGE_URL"  # Replace with actual image URL
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))

# Preprocess image
image_tensor = transform(image).unsqueeze(0).to(device)

# Encode image
with torch.no_grad():
    image_vector = model.encode_image(image_tensor)

print("Image encoding successful! Shape:", image_vector.shape)

Real-World applications of MM-RAG

MM-RAG isn’t just theoretical it’s already transforming industries! Here are some real-world applications:

1. Visual question answering

AI systems equipped with MM-RAG can answer questions based on visual inputs like photos or diagrams. For example:

• In healthcare: Diagnosing conditions using patient symptoms and medical imaging.

• In education: Explaining scientific concepts with diagrams alongside textual explanations.

2. Dynamic customer support: Retailers can use MM-RAG-powered bots to provide personalised support by combining product FAQs with visual guides or instructional videos. This reduces escalations and improves customer satisfaction.

3. Legal research assistance: Lawyers can leverage MM-RAG systems to retrieve case law, statutes, and legal documents while integrating visual evidence like charts or scanned contracts for better case preparation.

Challenges

While promising, MM RAG still faces challenges:

1. Data representation: Creating unified embeddings for diverse modalities (text vs images vs audio) is complex but critical for effective retrieval.

2. Computational costs: Handling large-scale multimodal data requires significant resources for storage and processing.

3. Ethical concerns: Ensuring privacy when dealing with sensitive multimodal data (e.g., medical records) is paramount.

The Takeaway

Multi-Modal RAG represents a major leap forward in AI’s ability to understand and generate meaningful responses across different types of data. By moving beyond text and incorporating images, audio, and video into the retrieval and generation process, it enables richer, more accurate, and more human-like interactions.

While challenges remain, the potential benefits from more intelligent search engines to advanced AI assistants make this a groundbreaking shift in how we interact with AI. As we push forward, one thing is certain: the future of information retrieval isn’t just about words. It’s about embracing the full spectrum of human knowledge.