When you ask a question from GenAI, you expect a fast response that considers all the relevant information. But the GenAI system may not have access to all the sources of data that are required to adequately answer your question, especially if it was trained primarily on public text-based sources. To enable a complete response that offers full context, AI systems are required to process, analyze, and generate content based on all relevant data.

Whereas traditional models excel at processing text, many advanced AI systems have adopted Multimodal techniques that are capable of understanding and connecting information across text, images, audio, structured data and video to provide comprehensive, context-aware responses. Whereas business users not that long ago who wanted to learn about how to create a pivot table would go straight to the Help file – the only game in town – now a GenAI system is more likely to be consulted. If the model was trained primarily on text sources, it’s missing all the ideas within instructional videos on YouTube, and the response may be incomplete. 

The same holds true for organizational data that is not public and not textual, hence hidden from the GenAI model. That data may be sales data, customer details, IIoT events streams, and many other different types of data. 

By incorporating multimodal information, Multimodal Large Language Models (MLLMs) can interpret a variety of data types, accessing a broader range of information, making their outputs more nuanced and context-aware. The multimodal AI market is expected to grow at an astonishing CAGR of 35%, and reach $4.5 billion by 2028, according to a report by MarketsandMarkets. But Multimodal is only part of the story, since when AI systems incorporate many sources of data, they need a way to verify which data is relevant, to be able to add the correct context. Many AI systems use Retrieval Augmented Generation (RAG) which incorporates a retrieval component that allows the model to access and utilize relevant information from additional sources. To incorporate structured data in addition to other sources, solutions such as eRAG use semantic reasoning, which enables knowledge acquisition across multiple structured data sources and and unstructured organizational data, ensuring that foundation models get the right context and meaning of structured data.

Multimodal RAG takes the traditional RAG concept a step further by leveraging multiple modalities. It is capable of understanding and connecting information across text, images, audio, and video to provide comprehensive, context-aware responses. Multimodal RAG actually mimics human sensory perception, where information from different senses is combined to form a comprehensive picture of the environment.

Supported Modalities 

Multimodal RAG incorporates inputs from a number of sources, including: 

Text Documents: written content like documentation, articles and emails which the model processes to gain the context and semantics of the text source, supplemented by Natural Language Processing (NLP) techniques that extract key information. 

Structured Data: databases, spreadsheets, and other information stored with clear relationships and hierarchies. While most LLMs excel at extracting data from documents and other unstructured data sources, extracting data from structured sources is a different kettle of fish. Special expertise is required to preserve the inherent structure and relationships while converting this data into vector representations that can be integrated with other data types, and still maintain the original context. 

Video Content: requires sophisticated processing to extract meaningful information from both visual and audio elements. The system analyzes frame sequences, motion, scene changes, and synchronized audio to understand the complete context. Key frame extraction and temporal understanding help manage the complexity of video data. 

Images and Diagrams: vision-language models like CLIP process photographs, illustrations, technical diagrams, charts and more, to identify objects, read text, and understand complex visual relationships within diagrams, and understand visual elements, text within images, and spatial relationships. 

Audio Files: speech-to-text models like Whisper convert voice recordings, meetings, calls, podcasts, etc. into text, while preserving important aspects like tone and emphasis. The system can process multiple speakers, different languages, and acoustic characteristics. 

Other Organizational Files: Organization’s data products, such as definitions of analytical reports, data access code and others can be analyzed using AI models to provide additional information. 

Key Components of Multimodal RAG Patterns

Data Preparation and Representation

Before training, data must be cleaned and preprocessed to remove noise, inconsistencies, and irrelevant information. Then, each modality needs to be encoded in a way that preserves its relevant features, while allowing for meaningful cross-modal relationships to be established. The data representation process is crucial for multimodal retrieval because it determines how information from different modalities can be effectively integrated and compared. Additionally, efficient data representation supports faster processing and more precise retrieval, improving the overall performance of multimodal systems.

Different modalities require different representation techniques; textual data can be represented using embeddings, while images and audio can be represented using feature vectors or spectrograms. If you are working with a document, you must ensure that the semantic representation of a chart aligns with the semantic representation of the text that discusses the same chart.

Data Retrieval 

Data retrieval requires a certain level of sophistication for  identifying and retrieving information that is relevant to a given query, regardless of its format. These include text-based keyword matching and semantic search, columns matching for structured data repositories, image-based retrieval that incorporates image recognition, object detection, and visual embeddings and audio content retrieval from media libraries, podcasts, and music streaming platforms. Identification of relevant content is not only matching of keywords, but also identifying all related data items that are necessary for providing an adequate answer to the query.

Generation

In this step, the fetched data is combined into a unified representation to ensure that the multimodal information is aligned and complementary. The model processes the fused data to generate the final response, using Direct Input that feeds retrieved information directly fed into the model, Fine-tuning: uses the retrieved data to fine-tune the model to improve its performance on specific tasks, or combination of direct input and fine-tuning. 

Challenges and Limitations of Multimodal RAG 

To ensure an effective multimodal RAG system, a number of challenges must be addressed, including ethical concerns, privacy and bias issues. Many kinds of organizational data require access restrictions so certain data items are accessible to authorized users only.

Data Integration requires specialized processing and expertise to create a cohesive model from a wide variety of inputs. For example, including structured data sources is beyond the scope of most LLMs, therefore a solution that enables GenAI to interact with structured data is required. 

Another issue is aligning the semantic representation of information across different modalities, so that the meaning derived from an image matches the corresponding text description. Reducing error propagation is critical to ensure that the effect of an error in one modality does not propagate and lead to incorrect text generation. Language models themselves also introduce challenges, as a standard LLM trained by public data will not automatically adapt to internal organization’s terminology.

Controlling computational complexity and costs is challenging since computations that enable simultaneous processing of video and audio input often require extreme computing power, which can also lead to significant costs. Organizations may need to beef up their compute capabilities to be able to execute these multimodal computations.

Applications of Multimodal RAG Patterns

Customer Support and Virtual Assistants: Virtual assistants equipped with multimodal capabilities can understand user queries more comprehensively by considering text, organization’s databases, voice, and visual inputs. This leads to more personalized and efficient interactions, improving overall customer satisfaction. Being able to add context from the organization’s data is critical for excellent service; a solution such as eRAG interprets the context of structured data for LLMs to ensure that they get the right context and meaning of the organization’s data. 

Healthcare and Medical Diagnostics: multimodal RAG models can be used to analyze and generate insights from diverse data sources such as medical records, radiology images, and patient histories, some of which requires access to the healthcare organization’s databases, using a solution such as eRAG. This enables more accurate diagnosis, treatment recommendations, and personalized patient care. 

Business Analysis: multimodal RAG significantly enhances business analysis by integrating diverse data sources like databases, spreadsheets, and text documents. This allows for a more comprehensive understanding of business operations and trends. By incorporating structured data and unstructured information, analysts can uncover deeper insights, improve forecasting accuracy, and make data-driven decisions. For instance, analyzing sales data alongside customer feedback forms and market trend reports provides a holistic view of business performance and opportunities. This integrated approach enables businesses to identify correlations, predict future outcomes, and develop effective strategies.

Content Creation and Marketing: by combining text, images, and audio, multimodal RAG patterns enable enriched and engaging content for multimedia marketing campaigns, educational materials, and interactive experiences that captivate audiences. Businesses should incorporate information from the organization’s data sources to ensure that campaigns are true to brand. 

Autonomous Systems and Robotics: multimodal RAG enables machines to interpret and respond to complex environments by integrating data from microphones, infrared and ultrasonic sensors, cameras and LiDAR, allowing more robust decision-making in dynamic environments.

Education: multimodal RAG can make complex topics more accessible and engaging by providing educational materials that combine text explanations with relevant images, diagrams and video clips. Educational institutions can benefit from solutions such as eRAG that add meaning and context from the institution’s structured data that can customize and personalize the educational experience. 

Last words 

Multimodal RAG offers a range of benefits, including enhanced accuracy, richer user experiences, better context understanding, improved training efficiency, and enhanced creativity and flexibility. By integrating multiple modalities, these systems are poised to transform various industries and redefine the capabilities of AI, making them more powerful and versatile than ever before. Multimodality in AI represents a significant advancement in creating systems that more closely resemble human cognitive abilities. By integrating and processing diverse data types, multimodal AI enhances the depth and accuracy of machine understanding and interaction, paving the way for more sophisticated and versatile AI applications.