Alankrit Mishra

A Review of Multimodal RAG Approaches

Introduction

Hey there, AI enthusiasts and curious minds! 👋 Today, we’re diving deep into the fascinating world of Multimodal RAG systems. In the rapidly evolving landscape of artificial intelligence, Retrieval Augmented Generation (RAG) has emerged as a powerful technique to enhance the capabilities of Large Language Models (LLMs). While traditional RAG systems primarily focus on text, the advent of Multimodal RAG opens up new frontiers by incorporating various data types such as images, tables, and potentially audio and video. This comprehensive review delves into the world of Multimodal RAG, exploring its components, architectures, implementation strategies, and future prospects.

What is Multimodal RAG?

Alright, let’s start with the basics. Multimodal RAG is like giving AI a superhuman ability to understand and connect information from different sources - text, images, tables, and potentially even audio and video. Instead of relying solely on text-based knowledge, Multimodal RAG systems can leverage information from images, tables, and other data formats to generate more accurate and informative outputs. Imagine having a conversation with an AI that can not only understand your words but also grasp the context from related images or data tables. That’s the power of Multimodal RAG!

Why Should You Care?

Consider a Wikipedia article about SpaceX. While the text provides valuable information, the accompanying images of rockets and infographics offer crucial visual context. Similarly, tables containing launch data or spacecraft specifications complement the narrative. Multimodal RAG aims to harness all these data types to provide a more holistic understanding and generate more informed responses.

  1. Enhanced Understanding: Multimodal RAG systems can provide more comprehensive and accurate responses by considering multiple types of data.
  2. Versatility: These systems can be applied to a wide range of industries, from healthcare to e-commerce and banking.
  3. Improved User Experience: By leveraging diverse data types, Multimodal RAG can offer more intuitive and context-aware interactions.

Core Components of Multimodal RAG

Let’s break down the key components that make Multimodal RAG tick:

1. Vector Embeddings

At the heart of Multimodal RAG are vector embeddings - numerical representations that capture the semantic meaning of various data types. These embeddings allow the system to understand and compare different pieces of information across modalities.:

Once we have our embeddings, we need a way to efficiently search through them. This is where vector search comes into play. Vector search enables the system to find relevant information based on the meaning rather than exact matches. This is crucial for multimodal data where the relevance might span across different data types. It’s like having a super-fast librarian who can find related information across different types of media in the blink of an eye.

Check out this comparison of vector search approaches:

Aspect Brute Force Approximate Nearest Neighbor
Time Complexity O(n) - Linear Often sub-linear, e.g., O(log n)
Accuracy 100% (Exact) Configurable (often >95%)
Scalability Poor for large datasets Good for large datasets
Memory Usage Low Higher (due to index structures)
Preprocessing None Required (index building)

Note: Many vector database SDKs already have efficient ANN search algorithms built-in, so you don’t have to implement them from scratch! [1]

3. Large Language Models (LLMs)

The final piece of the puzzle is the Large Language Model. This is the brains of the operation, taking the retrieved information and generating human-like responses. For Multimodal RAG, we need LLMs that can handle various data types. Let’s compare some of the top contenders:

  1. GPT-4 (OpenAI):
    • Highly capable multimodal model
    • Handles text and images seamlessly
    • Closed-source and accessible only via API
  2. Gemini Pro (Google) [7]:
    • Strong performance across text and visual tasks
    • Available through Google Cloud
    • Offers different sizes for various use cases
  3. Claude (Anthropic):
    • Capable of processing text and images
    • Known for its strong reasoning capabilities
    • Available via API
  4. LLAMA 3.2 Vision (Meta) [8]:
    • Open-source multimodal model
    • Available in 11B and 90B sizes
    • Supports text and image inputs
    • Can be fine-tuned and deployed on-premises

Comparing LLAMA 3.2 Vision to Other Multimodal LLMs

LLAMA 3.2 Vision brings some unique advantages to the table:

However, it’s important to note some considerations:

Here’s a quick comparison of LLAMA 3.2 Vision (11B, instruction-tuned) with other models on some benchmarks:

Benchmark LLAMA 3.2 Vision (11B) GPT-4 Gemini Pro
MMMU (val) 50.7 (CoT) 59.4 59.4
VQAv2 75.2 (test) 77.2 78.6
DocVQA 88.4 (test) 90.6 -

Note: These benchmarks are for reference and may not reflect the latest model versions or real-world performance across all tasks.

When choosing a multimodal LLM for your RAG system, consider factors like:

Multimodal RAG Architectures

Now that we understand the building blocks, let’s explore different ways to put them together. Here are the main approaches to implementing Multimodal RAG:

1. Unified Vector Space Approach

This approach is all about creating a single, unified space for all types of data. It’s like creating a universal language that all data types can speak.

Pros:

Cons:

2. Grounding Modalities to Text Approach

This method converts everything to text before processing. It’s like translating everything into a common language (text) that our AI already understands well.

Pros:

Cons:

3. Separate Vector Stores Approach

This approach keeps different data types in their own specialized stores. It’s like having expert librarians for each type of media.

Pros:

Cons:

4. Contextual RAG Approach

Contextual RAG takes things a step further by adding relevant context to each piece of information before embedding [2]. It’s like giving each data point its own backstory. Let’s dive deeper into this approach:

Key Principles of Contextual RAG

  1. Context-Aware Embedding: Instead of encoding each piece of information in isolation, Contextual RAG considers the surrounding context when creating embeddings.
  2. Dynamic Retrieval: The retrieval process adapts based on the context of the query and the conversation history.
  3. Relevance Refinement: Retrieved information is further refined based on its relevance to the current context.

How Contextual RAG Works

  1. Context Generation: Before embedding, each chunk of information is augmented with relevant context (metadata, surrounding paragraphs, relevant facts).
  2. Contextual Embedding: The augmented text is embedded, capturing both content and contextual relevance.
  3. Query Expansion: Queries are expanded based on conversation history, user preferences, and domain knowledge.
  4. Multi-Stage Retrieval: Retrieval occurs in multiple stages, including initial broad retrieval, re-ranking, and optional iterative retrieval.
  5. Response Generation: The LLM generates a response using the original query, retrieved contextually relevant information, and conversation history.

Implementation Example

Here’s a simplified example based on Anthropic’s Contextual RAG Cookbook [14]:

import anthropic

client = anthropic.Client(api_key="your_api_key")

def get_contextual_embedding(text, context):
    prompt = f"Context: {context}\n\nText to embed: {text}"
    response = client.completions.create(
        model="claude-2",
        prompt=prompt,
        max_tokens_to_sample=1,
        temperature=0
    )
    return response.embedding

def retrieve_with_context(query, conversation_history):
    expanded_query = expand_query(query, conversation_history)
    initial_results = vector_store.similarity_search(expanded_query)
    reranked_results = rerank_results(initial_results, query, conversation_history)
    return reranked_results

def generate_response(query, retrieved_info, conversation_history):
    prompt = f"""
    Conversation history: {conversation_history}
    
    Retrieved information: {retrieved_info}
    
    User query: {query}
    
    Please provide a response based on the above information:
    """
    response = client.completions.create(
        model="claude-3.5",
        prompt=prompt,
        max_tokens_to_sample=300
    )
    return response.completion

Benefits of Contextual RAG

  1. Improved relevance and coherence of responses
  2. Reduced hallucination in LLM outputs
  3. Better handling of ambiguous queries
  4. Enhanced personalization capabilities

Challenges and Considerations

  1. Increased computational overhead
  2. Difficulty in optimal context selection
  3. Privacy concerns with storing contextual information
  4. Balancing specificity and generalization in retrievals

Pros:

Cons:

5. ColPali: Vision-Driven Document Retrieval

ColPali is a cutting-edge approach that leverages vision language models for document retrieval [3]. It’s particularly exciting for handling complex documents with both text and visual elements.

Key Features:

Pros:

Cons:

Comparative Analysis

Let’s break down how these approaches stack up against each other:

Approach Complexity Information Preservation Retrieval Efficiency Scalability Contextual Understanding Multimodal Integration
Unified Vector Space Medium Medium High Medium Low High
Grounding to Text Low Low-Medium High High Medium Medium
Separate Vector Stores High High Medium Medium Medium High
Contextual RAG High High Medium-High Medium High Medium
ColPali High High High High High Very High

Implementation Considerations

When implementing a Multimodal RAG system, keep these factors in mind:

  1. Data Preparation: Each approach requires different data preprocessing steps. For example, the Unified Vector Space approach needs joint embedding models, while the Grounding approach requires effective modality-to-text conversion.

  2. Embedding Generation: The choice of embedding models can make or break your system. Consider factors like computational costs and embedding dimensions.

  3. Vector Store Selection: Choose a vector store that supports your chosen approach. Options include Quadrant, Chroma DB, or custom solutions for more complex setups.

  4. Retrieval Pipeline: Design your retrieval process based on your chosen architecture. For instance, Separate Vector Stores require parallel retrieval and re-ranking strategies.

  5. Response Generation: Leverage capable multimodal LLMs like GPT-4, Gemini Pro, Claude, or LLAMA 3.2 Vision for generating responses that coherently incorporate information from various modalities. When using LLAMA 3.2, consider the trade-offs between model size, performance, and resource requirements.

Advanced Techniques

To take your Multimodal RAG system to the next level, consider these advanced techniques:

Knowledge Graphs and GraphRAG

GraphRAG combines the power of knowledge graphs with RAG systems [4]. It’s particularly useful for handling complex relationships between different data types.

Key benefits:

When implementing GraphRAG, be aware of potential security risks like SQL injections or Cypher injections when generating queries from LLMs. Here are some tips to mitigate these risks:

Few-Shot Learning with LLM Agents

Few-shot learning can significantly improve your system’s ability to handle new types of queries or data. Here’s a quick example of how you might implement this:

few_shot_prompt = """
Given a knowledge graph, answer the following questions:

Q: Who is the CEO of TechCorp?
A: To answer this, I'll search the knowledge graph for an entity "TechCorp" and look for a "CEO" relationship.
   Result: John Smith is the CEO of TechCorp.

Q: What products are associated with Project Alpha?
A: I'll find the "Project Alpha" node and traverse "product" relationships.
   Result: Project Alpha is associated with products X, Y, and Z.

Now answer this question:
Q: {user_query}
"""

result = llm_agent.query(few_shot_prompt.format(user_query=user_input))

LoRA Fine-Tuning for Domain Adaptation

Low-Rank Adaptation (LoRA) is an efficient fine-tuning technique that can help adapt your LLM to specific domains or tasks. Here’s a quick implementation example using the PEFT library:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import get_peft_model, LoraConfig, TaskType

model = AutoModelForCausalLM.from_pretrained("base_model_name")
tokenizer = AutoTokenizer.from_pretrained("base_model_name")

peft_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    inference_mode=False,
    r=8,
    lora_alpha=32,
    lora_dropout=0.1
)

model = get_peft_model(model, peft_config)

# Fine-tune the model on your domain-specific data
# ...

# Use the fine-tuned model for improved knowledge graph querying

Challenges and Future Directions

As exciting as Multimodal RAG is, it’s not without its challenges. Here are some key areas to watch:

  1. Balancing Information: Ensuring the right mix of information from different modalities in the final response.
  2. Computational Resources: Processing multiple data types can be resource-intensive.
  3. Information Loss: Potential loss of nuance when converting between modalities.
  4. Relevance and Coherence: Maintaining relevance and coherence when combining information from diverse sources and modalities.
  5. Scalability: Ensuring systems can handle large-scale data and complex queries efficiently.
  6. Ethical Considerations: Addressing biases and ensuring fair use of multimodal systems.

Looking ahead, I’m particularly excited about these future directions:

  1. Integration of More Data Types: Incorporating audio, video, and other sensory data into RAG systems.
  2. Advanced Multimodal Embedding Models: Development of more sophisticated models for unified embeddings across modalities.
  3. Sophisticated Agent-based Systems: Evolution of RAG systems into more autonomous agents capable of complex reasoning.
  4. Enhanced Multimodal LLMs: Advancement in language models specifically designed for seamless integration of diverse data types.

Conclusion

Multimodal RAG is not just a buzzword; it’s a game-changer in how AI systems understand and process information. By bridging the gap between different data modalities, these systems are paving the way for more intuitive, comprehensive, and context-aware AI interactions.

As we continue to push the boundaries of what’s possible with Multimodal RAG, I’m thrilled about the potential applications across industries - from revolutionizing search engines to creating more immersive and intelligent virtual assistants.

What are your thoughts on Multimodal RAG? Have you implemented any of these approaches in your projects? I’d love to hear about your experiences and insights in the comments below!

Happy coding, and here’s to the exciting future of Multimodal AI! 🚀🤖

References and Additional Reads

[1] Efficient ANN search algorithms in popular vector database SDKs. (n.d.). Retrieved from various vector database documentation.

[2] Anthropic. (n.d.). Contextual RAG: Introducing Contextual Retrieval. Retrieved from https://www.anthropic.com/news/contextual-retrieval

[3] ColPali: Efficient Document Retrieval with Vision Language Models. (2024). Retrieved from https://arxiv.org/abs/2407.01449

[4] Microsoft Research. (n.d.). GraphRAG: Unlocking LLM discovery on narrative private data. Retrieved from https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

[5] Hugging Face. (n.d.). CLIP. Retrieved from https://huggingface.co/docs/transformers/en/model_doc/clip

[6] OpenAI. (n.d.). GPT-4 Mini: Advancing cost-efficient intelligence. Retrieved from https://openai.com/index

[7] Google Developers Blog. (n.d.). Gemini flash 1.5 updates. Retrieved from https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/

[8] Hugging Face. (2024). Llama can now see and run on your device - welcome Llama 3.2. Retrieved from https://huggingface.co/blog/llama32