What is Multimodal RAG?
In the latest episode of the RAG Masters show, we explore Multimodal RAG, how it works, three distinct approaches to implementing it, and the challenges and opportunities it presents.
Multimodal RAG is an advanced extension of traditional Retrieval-Augmented Generation systems. Classic RAG involves a retrieval engine that searches a database of text documents to find relevant information and injects this data into a prompt for a language model to generate a response. Multimodal RAG expands this by including non-text data types, which enhances the model's ability to understand and generate responses based on a more comprehensive set of inputs.
Taking multimodal inputs allows for RAG engineers to build a more complex retrieval engine that can ask a store of information about information across different mediums. This means that the retrieval engine can grab data from various sources—whether text, images, audio, or video—and use that information to answer a query. For instance, an expert's audio commentary on the Eiffel Tower can be retrieved alongside text and image data to provide a more holistic response that anchors the answer in the data provided.
How Multimodal RAG Works
The mechanics of Multimodal RAG involve transforming different data types into a structured data format like vectors that a model can process. This allows the model to retrieve and generate information across multiple modalities seamlessly.
Once these data types are encoded into vectors, they can be stored in a vector space or similar storage vehicle, enabling the model to find relevant information regardless of the original data type. This process could involve clustering similar data and separating dissimilar data, making it easier to retrieve the most pertinent information for a given query.
Three Approaches to Multimodal RAG
Implementing Multimodal RAG can be approached in a few distinct ways, each with its advantages and challenges. The three main methods include using a single multimodal model, employing a grounded modality approach, and utilizing multiple encoders.
Single Multimodal Model
This approach uses a unified model trained to encode different types of data (text, images, audio) into a common vector space. The model can then perform retrieval and generation across these different data types seamlessly. A single multimodal approach tends to be one of the most common approaches people talk about when they talk about multimodal RAG.
This method simplifies the process but relies heavily on the model’s ability to accurately encode and retrieve multimodal data. However, if the model is well-trained, it can store and retrieve similar information across different modalities effectively.
Google is a great example of using a single multimodal model, as described in this clip:
Grounded Modality (Text-Based)
In this approach, all data types are converted into text descriptions before being encoded and stored. This method leverages the strength of text-based models but may involve some loss of information during the conversion process.
Turning all data types into one modality creates a unified set of information for the model to retrieve, and today’s models are strongest on text. That’s not to say in the future there won't be models that are better suited for other modalities. And that future might be months not years. But for today’s powerhouse models, they started out as text machines and that is still where they are strongest.
This approach allows the use of robust text-based models for encoding and retrieval, making it a practical solution for environments where text is the primary data type.
Multiple Encoders
This method employs separate models to encode different data types. Each type of data (audio, images, text) is processed by its respective model, and the results are integrated later in the retrieval process. Passing them through a set of encoders that can play nicely together creates an environment where each model and encoder can be fine-tuned to play to its particular strengths.
This approach allows for specialized encoding but increases complexity in managing multiple models. It offers the flexibility to use the best model for each data type, enhancing the accuracy and relevance of the retrieved information. But often it can be the most difficult to implement and maintain due to the increased complexity of inputs and outputs.
With the emergence of powerful models that are starting to outperform other models in specific modalities, this approach to multimodal RAG may grow in popularity. As discussed in this clip, the model wars are heating up…
Challenges and Considerations
Implementing Multimodal RAG comes with its own set of challenges, such as handling temporal changes in data and ensuring the accuracy of the retrieval and generation process.
Temporal changes, like the varying appearances of the Eiffel Tower over time, pose a significant challenge. Ensuring that the retrieved information is temporally accurate and relevant requires sophisticated handling of metadata and context which can be even more challenging when trying to pull data from multiple modalities like images and audio.
Another consideration is the balance between using a single unified model and multiple specialized models. While a single model offers simplicity, multiple models provide more tailored encoding for different data types. This decision depends on the specific application and the need for flexibility.
Practical Applications and Future Prospects
Multimodal RAG holds immense potential for various practical applications, from enhancing search engines to improving AI-driven personal assistants. By integrating multiple data types, these systems can provide richer, more nuanced responses, improving user experience and satisfaction.
Looking forward, the field of Multimodal RAG is poised for significant advancements. As models continue to improve and new techniques are developed, the ability to effectively integrate and leverage multiple data types will become increasingly crucial. This progress will open up new opportunities for powerful applications and improved AI performance.
Conclusion
Multimodal RAG represents a significant advancement in AI, as it can enable richer and more contextually accurate information retrieval and generation that grounds the model in the truth of the data across modalities. While the field continues to evolve, the various approaches to implementing Multimodal RAG offer different trade-offs between simplicity, flexibility, and complexity. As technology progresses, the ability to effectively integrate and leverage multiple data types will be crucial for developing advanced AI applications.
You can check out the full episode of the Multimodal RAG discussion on the latest episode of RAG Masters.
