What is multimodal RAG?

A multimodal retrieval augmented generation (RAG) is an advanced AI system that expands the capabilities of traditional RAG by incorporating different types of data such as text, images, tables, audio and video files. In contrast to a traditional RAG, multimodal RAG can process and retrieve information from the multimodal data and generate contextually accurate and relevant responses. This process can be done by using generative AI models like OpenAI’s GPT-4 or Google’s Gemini for output generation.

Multimodal RAG uses modality encoders (a neural network component that transforms raw data from various modalities) and is designed for specific data types. It maps their representations to a shared embedding space, enabling cross-modal retrieval. The ingestion of multimodal data can include OCR-based extraction from images, preprocessing of structured and unstructured inputs and reranking of retrieved results to ensure maximum relevance. Meaning, that when a query is made in one modality (such as text), the data and the representation can be retrieved from another modality (such as images or structured data). This approach grounds the response of the generative model in multimodal evidence.  

In the multimodal RAG pipeline, various encoders like CLIP (Contrastive Language-Image Pretraining), wav2vec or vision transformers are used to support diverse data modalities. These encoders function as translators that convert each modality into a form understandable in the system. It combines insights from different modalities and enhances its accuracy and context awareness, allowing it to reason between inputs to generate precise and grounded responses. This method is valuable for applications like visual question answering, where understanding both an image and associated textual descriptions is crucial. It is also valuable in multimedia search engines that help users to query by using natural language while leveraging image or video content.

Multimodal RAG systems generally follow the same end-to-end workflow. They can be implemented through application programming interfaces (APIs) for easy integration into larger applications or orchestrated by AI agents for autonomous retrieval and response generation.

The first stage is multimodal knowledge preparation, where data from various sources is parsed (organizing raw data into a usable format for models) and encoded into embeddings by using appropriate embedding models. For example, CNNs (convolutional neural networks) for images, transformers for text, wav2vec for audio, are stored in a shared or aligned feature space. This alignment is often achieved by using contrastive learning, where paired data (such as image-caption, audio-transcript) is optimized to lie close in the embedding space, enabling cross-modal semantic matching. Image captioning and image summaries generated by vision language models can enrich retrieval performance by providing textual descriptions of visual content. All original modalities are preserved so that retrieval does not discard nontextual detail. 

The second step processes and retrieves queries. The user's query is converted to an embedding and evaluated against stored data by using a similarity search. To preserve all the relevant information, the original modalities are saved. This process enables any-to-any retrieval where a text query can fetch an image, an image can fetch related text, and so on. Vector databases (such as Pinecone) that can store billions of multimodal embeddings in a vector store offer effective, low-latency nearest-neighbor search at scale. This stage forms the foundation of multimodal information retrieval. 

The obtained results are ready for generation in the third stage, which entails multimodal context building. Some systems use early fusion by converting everything into text before generation, while others use late fusion, where different modalities remain separate and the generator attends to them directly. A framework such as LangChain can be used to orchestrate retrieval, fusion and generation of pipelines efficiently. Also, graphs can be used to represent relationships between multimodal entities, helping the system better understand connections across data.

There are three different approaches for converting modalities:

Text-translation approach: The most direct way to incorporate multimodality into RAG is to convert the non-text data types to text at the time of storage and retrieval.¹ Images can be converted to captions, audio to transcriptions and tabular data can be represented in a serialized structure with CSV or JSON. It is an easy integration for existing text-based RAG systems because every modality is handled as text. This approach has an information bottleneck because many of the nuances and details of the nontext data that makes it unique can be lost in translation.

Text retrieval with multimodal generation: This more advanced approach still performs retrieval over text embeddings (captions, transcripts, metadata) but during response generation, it allows a multimodal large language model (LLM) to directly access the original nontext data. For example, the retriever might fetch an image by using its caption, but the image itself is passed into the multimodal LLM alongside the query. This hybrid approach improves expressiveness during generation, especially by fine-tuning on domain-specific datasets, though retrieval quality still depends on the initial text representation of the nontext data.

Multimodal retrieval: The most advanced approach involves using multimodal embeddings. These embeddings can be mapped for text, images, audio and video into a common vector space for cross-modal retrieval where a textual query will directly retrieve relevant multimodal data. The multimodal evidence retrieved would be submitted to the multimodal LLM for response generation that includes explicit grounding on multiple data sources. The multimodal LLM would avoid the bottlenecks of text translation and offers a maximum degree of contextual grounding. But this remedy can be costly due to the underlying computational demands and advanced modality-specific encoders. It can also benefit from vision language models and can be further optimized by using summarization to condense retrieved content before generation. 
 
Finally, the response generation stage employs a multimodal LLM to synthesize the output. Because this output is grounded in multimodal evidence, the risk of hallucination is reduced. Depending on design, the final output might be text-only or multimodal, such as a written explanation paired with retrieved images, annotated visuals or tables.

Therefore, multimodal RAG progresses from simple text translation pipelines to hybrid text retrieval with multimodal generation and ultimately, to true multimodal retrieval with shared embeddings. The chosen approach defines the balance between simplicity, efficiency and expressive power.

Multimodal RAG systems have become strong instruments in many applications, showing improved performance by using diverse data types. Now let's look at two use cases of multimodal RAG's capabilities in industrial applications and open-domain question answering.

A study was conducted on industrial applications that prototyped a multimodal RAG system by using 100 questions and answers taken from technical manuals requiring understanding from both text and images.² It had three configurations: text-only RAG, image-only RAG and multimodal RAG. An LLM was employed as an evaluator, and it suggested that multimodal data captured by text and image data achieved higher performance than either a text-only or image-only resource. Of the answer synthesis models tested in the experiment, GPT-4V had a higher performance than LLaVA and provided a better ability to accurately synthesize multimodal content. Because the context was more clearly defined, this ability enabled the system to parse diagrams and instructions and give more accurate and relevant answers.

Another study introduced MuRAG (multimodal retrieval augmented transformer) for open-domain question answering over both image and text.³ Unlike many previous models that rely on textual knowledge only, MuRAG evaluates an external (nonparametric) multimodal memory to retrieve relevant images and texts that could augment language generation. MuRAG combines a pretrained T5 model and a pretrained vision transformer (ViT) model to embed multimodal data embeddings in a memory bank. This model leverages a joint contrastive and generative training objective. In large-scale experiments on two open multimodal question answering datasets (WebQA and MultimodalQA), MuRAG outperformed existing baselines by 10–20% on accuracy assessed under different settings.

While multimodal RAG has various benefits, there are a few challenges to consider:

Computational complexity: Training and inference require heavy computation while integrating a multimodality approach leads to slower inference time and increased costs at a large scale.

Data alignment and synchronization issues: Ensuring that the spaces created by sharing embedding spaces align with the most relevant pieces across the different modality types is a challenge. If they are not aligned correctly during training or retrieval, it can lead to misalignment errors that are semantic in nature leading to performance degradation.

Evaluation metrics for multimodal: The benchmarks currently available are primarily text-based and do not include the multimodal grounding and reasoning aspects. This problem is still being researched in order to develop robust evaluation metrics for multimodal RAG. 

Data availability and quality: There are few multimodal datasets that are high quality within someone's domain. Most datasets are more domain-specific and costly to curate. The lack of data is a limitation preventing high-quality generalizable multimodal RAG systems from being trained.

Hallucination and faithfulness: Multimodal LLMs are grounded on retrieved data but there are still hallucination problems to contend with. Even if there is grounded data, multimodal models can hallucinate content but will also tend to overgeneralize when the modalities itself conflict, making reliable grounding a problem.

Despite these challenges, multimodal RAG provides a major step toward building more robust and context-aware RAG applications and systems. The advent of efficient encoders, scalable vector databases and cross-modal alignment processes have led to a decrease in the computational and synchronization barriers. Likewise, new evaluation frameworks and multimodal datasets with richer meaningful data are continuously being developed, which will likely solidify both performance and reliability. If we can close the remaining open problems, multimodal RAG could be a critical technology for knowledge-intensive applications. This achievement can result in AI systems that are able to reason more like humans by combining vision, language, audio and structured data.