To incorporate multiple modalities like images or tables into a Retrieval-Augmented Generation (RAG) system while still using an LLM for generation, you need to address two key challenges: representing non-text data in a retrievable format and enabling the LLM to interpret these modalities. First, multimodal data must be converted into embeddings that capture their semantic meaning. For images, models like CLIP or vision encoders from frameworks such as OpenAI’s GPT-4V can generate vector representations. Tables can be serialized into structured text (e.g., Markdown) or processed using specialized encoders that understand tabular relationships. These embeddings are then indexed in a vector database alongside text embeddings. During retrieval, a query (text, image, or mixed) is embedded using the same models, and the system fetches relevant documents, images, or tables based on similarity. For example, a medical RAG system could retrieve X-ray images alongside textual reports by embedding both into a shared vector space.
The generation phase requires the LLM to handle mixed inputs. Since most LLMs are text-only, non-text data must be converted into text descriptions or references. For images, this could involve generating captions with a vision-language model (e.g., BLIP-2) and passing them as context to the LLM. Tables might be summarized into natural language or retained as structured text. Alternatively, multimodal LLMs like GPT-4V or LLaVA can directly process images and tables if they’re included in the input (e.g., base64-encoded images). However, this approach often demands significant computational resources and careful prompt engineering. For instance, if a user asks, “Explain the trend in this chart,” the system might retrieve the chart image, generate a textual summary of its data, and feed both the summary and raw chart to the LLM for synthesis.
Evaluating multimodal RAG introduces new considerations beyond traditional text-based systems. First, relevance assessment becomes more complex: retrieved images or tables must align with the query’s intent, which may require human evaluation or specialized metrics (e.g., CLIP score for image-text alignment). Second, the LLM’s ability to accurately interpret multimodal data must be tested—for example, verifying that a generated analysis of a table doesn’t misrepresent numerical values. Third, hallucinations specific to non-text data (e.g., inventing details about an image) need monitoring. Additionally, latency and scalability become critical, as processing images or large tables can slow down retrieval and generation. Finally, fairness and bias risks expand: an image retrieval system might disproportionately surface certain demographics, while tabular data could encode historical biases. These factors necessitate a combination of automated metrics (e.g., retrieval accuracy, BLEURT for text quality) and human-in-the-loop validation to ensure robustness.