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Multimodal RAG Explained.

Introduction:

“Multimodal RAG Intuitively and Exhaustively” discusses the application of Retrieval-Augmented Generation (RAG) in multimodal AI systems. It explores how RAG models can be used to integrate various data modalities (such as text, images, and audio) to improve AI’s reasoning capabilities. The podcast also covers different architectures and techniques used in multimodal RAG, emphasizing its potential to enhance both accuracy and interpretability in AI-driven tasks.

Listen to the podcast at https://podcasts.apple.com/us/podcast/multimodal-rag-explained/id1684415169?i=1000665669799

Multimodal RAG Explained in details

Welcome listeners to “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” I’m your host, Anna. In today’s episode, we dive into an exciting topic inspired by Daniel Warfield’s blog post titled “Multimodal RAG — Intuitively and Exhaustively Explained.” This episode is produced by Etienne Noumen, and we encourage you to follow Daniel Warfield on Substack for more insights. We’ll break down the complex subject of Multimodal Retrieval Augmented Generation. So sit back, relax, and let’s unravel the fascinating world of AI together.

https://youtu.be/tf9pJ74sHog

First, let’s cover the basics of traditional Retrieval Augmented Generation, or RAG. Essentially, RAG is a technique that enhances the capabilities of language models by integrating external information. Here’s how it works: Imagine you have a query, like asking for detailed information about a specific topic. Instead of the language model relying solely on pre-existing knowledge, a RAG system first searches for relevant documents or data pieces that match your query. This process of finding pertinent information is known as retrieval. RAG leverages sophisticated AI models to transform text and other forms of data into numerical representations called embeddings. These embeddings are essentially vectors, which are mathematical constructs that help the system understand and measure the relevance of the information to your query. Once the system retrieves the most relevant information, this data is combined, or augmented, with the original query. This enriched query is then passed to the language model, which uses this augmented data to generate a more precise and informative response. So, in summary, RAG enhances language models by providing them with additional relevant context, making their output much more accurate and contextually rich.

Before we dive into Multimodal RAG, it’s essential to understand the concept of multimodality. In data science, ‘modality’ refers to a type of data, like text, images, or videos. For years, these different types of data were treated as separate entities, requiring different models to process each type. However, this notion has evolved significantly. Today, multimodal models are at the forefront, designed to understand and integrate multiple types of data seamlessly. One of the core ideas behind these models is the use of joint embeddings. Joint embeddings allow the model to learn and represent various types of data in a unified way, enabling the creation of more comprehensive and efficient data processing systems. The development of these multimodal models has truly revolutionized the field. They offer greater versatility and performance, opening new horizons for data science and AI applications. By understanding and leveraging multiple modalities, these models can tackle complex tasks that single-modality models would struggle with, making data interactions more intuitive and powerful.

Now, let’s explore Multimodal Retrieval Augmented Generation, or Multimodal RAG. This innovative approach builds on the foundational concept of traditional RAG but takes it a step further by incorporating multiple forms of data. Instead of just retrieving and augmenting text, a Multimodal RAG system can include images, videos, and other types of information. Picture this: Imagine querying an AI, not just with text but also asking it to consider relevant images, videos, or even audio clips. The AI then processes all these modalities, aggregates the most pertinent data, and uses it to generate more accurate, contextually rich responses. This fusion of various data types makes the Multimodal RAG system incredibly versatile and enhances the output’s richness. It can provide a more holistic understanding and response to queries, effectively leveraging a broader spectrum of information than text alone. This advancement opens up an array of applications, from more sophisticated customer service bots to advanced research tools that can generate insights by drawing on a diverse range of data sources.

By broadening the scope of data that can be integrated into AI models, Multimodal RAG systems offer powerful, comprehensive results that were previously unattainable with text-only approaches.

The first approach to Multimodal RAG involves using a shared vector space. This method leverages encoders specifically designed to harmonize different modalities of data—such as text, images, and videos—into a unified representation. By processing these diverse data types through a cohesive encoding system, the information is translated into a shared vector space. This allows the retrieval mechanism to draw the most relevant and contextually appropriate pieces of data across all modalities, optimizing the system’s ability to generate more nuanced and comprehensive outputs. This approach not only enhances the retrieval process but also ensures that the language model receives a diverse set of enriched information for better generation results.

The second approach to achieving Multimodal Retrieval Augmented Generation is known as Single Grounded Modality. In this approach, all data modalities—whether they are videos, images, or audio—are converted into a single modality, typically text. By unifying different types of data into one common format, the complexity of the system is significantly reduced. However, this method does carry the theoretical risk of losing subtle information during the conversion process. Despite this potential drawback, in practice, it frequently yields high-quality results. This approach simplifies the architecture while maintaining a robust performance, making it a popular choice in various applications.

Approach 3: Separate Retrieval. The third approach is to utilize multiple models, each uniquely designed for different modalities such as text, images, or videos. These models perform retrieval separately and independently, which means they each fetch relevant information within their specialized domain. Once these individual retrievals are complete, their results are combined into a unified set. This method offers the advantage of specialized optimization for each modality, providing greater precision and flexibility. Additionally, it can handle unique modalities that aren’t supported by existing solutions, making it a versatile and robust option in the realm of Multimodal Retrieval Augmented Generation.

Let’s talk about building your own Multimodal RAG system, a cutting-edge tool that enhances the relevance and richness of the data retrieved for a language model. To get started, you’ll need some key tools, namely Google Gemini and a CLIP-style model for encoding. Google Gemini helps streamline the process of working with multiple data modalities. Essentially, you use it to set up a robust framework for retrieving various types of data, like text, images, and videos. The setup involves feeding your dataset into Google Gemini, which will then process and store this information in a way that makes it easier to retrieve later. Next, you’ll need a CLIP-style model for encoding. CLIP is a powerful model designed to understand both images and text simultaneously, allowing you to create what’s known as a joint embedding. This joint embedding ensures that different data types are interpreted in a compatible manner, making the retrieval process more efficient and accurate.

Once you have these tools in place, the next step is to configure your retrieval system. This typically involves setting up encoders that can take in queries from different modalities, translate them into a shared vector space, and then fetch the most relevant data across all formats. The retrieved data is then combined and passed into a language model, which generates a more comprehensive and contextually accurate response. Building a Multimodal RAG system might sound complex, but with the right tools and a methodical approach, you can create a powerful retrieval system that significantly enhances the capabilities of standard language models. So, roll up your sleeves and dive into the exciting world of Multimodal RAG!

Conclusion:

That wraps up our deep dive into Multimodal RAG. We hope you now have a clearer understanding of this emerging design paradigm and how it can be applied. Thank you for tuning in to ‘AI Unraveled.’ Don’t forget to follow Daniel Warfield on Substack for more fascinating articles. This is Anna, signing off!

Resources:

Source: https://open.substack.com/pub/iaee/p/multimodal-rag-intuitively-and-exhaustively

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