How Does Multimodal RAG Retrieve Diverse Data?

TL;DR

Multimodal RAG retrieves external information across text, images, audio, or video and supplies relevant context to a language model for grounded answers. Its three approaches convert every modality to text, retrieve through text while preserving original media for multimodal reasoning, or embed multiple modalities in a shared vector space for direct cross-modal search.

Transcript

If we want to retrieve external information like up-to-date documents or search results, and then send them as part of a query to a large language model, there's a technique we can use for that. Right. That is RAG or retrieval augmented generation. Yeah. So say I'm building an internal help chatbot with RAG. When somebody asks the LLM a question, l... Read More

Key Insights

• Retrieval augmented generation is a technique for finding external information, adding the relevant material to an input prompt, and giving a language model grounded context from which to answer questions about current documents, policies, search results, or other available sources.
• Classic RAG works by converting document chunks into vectors offline, storing those vectors in a vector database, embedding a user query with the same model, and retrieving the closest vectors as the most semantically relevant pieces of the original documents.
• A grounded RAG prompt is formed by placing retrieved context beside the original user query. The combined prompt is sent to the language model, which uses the supplied document material rather than relying only on information already available to the model.
• The text-ify everything approach converts images and screenshots into captions and converts audio or video into transcripts. These textual representations can enter a standard RAG pipeline, making the approach simple while preserving basic semantic information from different media types.
• Caption-based retrieval can lose visual nuance and spatial relationships. A caption might identify a network diagram and its major components but omit meaningful details, such as which colored path represents a primary connection and which one represents failover.
• Hybrid multimodal RAG searches paragraphs, captions, and transcripts as text while retaining pointers to the original media. When a caption is retrieved, the corresponding image can be passed to a multimodal language model together with the textual context and user query.
• Hybrid retrieval is limited by the quality of its textual proxies. If a caption or transcript fails to describe the important signal, the retriever may never surface the correct image, recording, or video clip, even though the generation model could reason over it.
• Full multimodal RAG uses aligned encoders that map text, images, audio, and other modalities into a shared vector space. This enables a single query vector to retrieve different artifact types directly, although the approach requires more compute, capable encoders, and context summarization.

Questions & Answers

Q: What is multimodal retrieval augmented generation?

Multimodal retrieval augmented generation is a method for retrieving relevant information from text, images, audio, video, and other media, then supplying that information to a language model as context. Depending on the implementation, the system may convert media into text, retrieve through textual proxies while preserving original files, or search multiple modalities directly through a shared vector space.

Q: How does a classic RAG pipeline use a vector database?

A classic RAG pipeline divides documents into text chunks, sends those chunks through an embedding model, and stores the resulting vectors in a vector database during an offline stage. When a user asks a question, the same embedding model converts the query into a vector. The retriever finds nearby vectors and returns their associated text chunks as context for the language model.

Q: How does the text-ify everything RAG approach work?

The text-ify everything approach converts each non-text medium into a textual representation before retrieval. A captioning model describes images and screenshots, while a speech-to-text service turns audio or video into transcripts. Those captions and transcripts follow the same embedding, vector storage, retrieval, and prompt construction process used for ordinary documents, so the core RAG pipeline requires few changes.

Q: Why can converting images to captions reduce retrieval quality?

Converting an image to a caption can preserve its broad subject while losing details that matter to the question. A caption may recognize a corporate network, a VPN gateway, and redundant links but omit colors, positions, or relationships. If those omitted features distinguish a primary path from a failover path, the textual representation cannot fully communicate the original diagram's meaning.

Q: How does hybrid multimodal RAG preserve original media?

Hybrid multimodal RAG creates searchable text from images, audio, and video but also stores pointers connecting each caption or transcript to its original artifact. Retrieval still operates over text embeddings. After a match is found, the system includes both relevant text and the corresponding original media in the context supplied to a multimodal language model, enabling richer reasoning during answer generation.

Q: What is the main limitation of hybrid multimodal RAG?

The main limitation of hybrid multimodal RAG is that discovery still depends on captions and transcripts. A multimodal language model can inspect an original diagram or recording after retrieval, but it cannot inspect an artifact that the retriever never selects. Weak textual descriptions may therefore hide relevant media when the decisive information is visual, spatial, or otherwise difficult to express as text.

Q: How does full multimodal RAG support cross-modal search?

Full multimodal RAG uses encoders for text, images, audio, and other media that are trained or aligned to produce vectors within a shared space. A text query can then retrieve policy paragraphs, diagrams, audio, or video frames through similarity search. Retrieval no longer depends entirely on captions or transcripts because each original artifact receives its own comparable vector representation.

Q: What are the tradeoffs between the three multimodal RAG approaches?

Text-ify everything is the simplest approach because all media becomes text, but captions and transcripts can discard important information. Hybrid multimodal RAG lets the model reason over original media, yet retrieval remains dependent on textual proxies. Full multimodal RAG enables direct cross-modal retrieval and the richest grounding, but it requires stronger multimodal encoders, more compute, and careful summarization to manage the context window.

Summary & Key Takeaways

• Classic RAG converts document chunks and a user query into vectors using the same embedding model. A retriever searches a vector database for the closest matches, returns relevant text chunks, and places them beside the original query as context. The language model then generates an answer grounded in the retrieved information.

• The text-ify approach converts images into captions and audio or video into transcripts before applying ordinary text-based RAG. Hybrid multimodal RAG uses the same text retrieval process but retains pointers to original media, allowing a multimodal language model to inspect retrieved images, audio, or video alongside textual context.

• Full multimodal RAG uses aligned encoders to represent text, images, audio, and other media in a shared vector space. A query vector can therefore retrieve relevant artifacts across modalities directly. This provides richer grounding without relying entirely on captions or transcripts, but it requires stronger encoders, additional compute, and careful context management.