Multimodal Retrieval‑Augmented Generation (RAG): Implementation Paths and Future Prospects

The presentation focuses on multimodal Retrieval‑Augmented Generation (RAG), outlining five core agenda items: (1) semantic‑based multimodal RAG, (2) VLM‑based multimodal RAG, (3) scaling VLM‑based multimodal RAG, (4) choosing technical routes, and (5) a Q&A session.

Three primary technical paths for building multimodal RAG systems are described. The first, a traditional “carving” pipeline, relies on OCR and object‑recognition to extract text, tables, and charts from documents, then parses each component into chunks for embedding and vector‑based retrieval.

The second path adopts a Transformer architecture, encoding entire documents with an encoder‑decoder model to capture contextual dependencies, improving coherence over the first approach.

The third path leverages Visual Language Models (VLMs) that directly ingest raw multimodal inputs, generate patch embeddings, and construct multi‑vector (tensor) representations to preserve richer document semantics, especially for complex layouts.

Detailed examples illustrate the carving route: document structure recognition, OCR for text, specialized models for chart parsing, and the creation of chunk embeddings stored in a vector database. The workflow also incorporates dense, sparse, and graph indexes, followed by a Tensor Reranker to improve retrieval quality.

For VLM‑based RAG, the talk highlights recent advances such as GPT‑4o, PaliGemma, and Qwen2, demonstrating accurate multimodal question answering on PDFs and charts. The ColPali method is introduced, converting documents into multi‑dimensional tensors and using similarity matching (MaxSim) for retrieval, achieving over 80% nDCG on benchmark datasets.

Scaling challenges are addressed by binarizing tensors, combining full‑text, dense, sparse, and tensor searches via the Infinity database, and employing fusion search to boost accuracy. Experiments show that adding tensor‑based re‑ranking significantly improves results compared to pure BM25 or dense vector search.

The discussion concludes that both traditional OCR pipelines and VLM approaches will coexist, with future systems likely to support tensor‑based delayed interaction as a standard feature for multimodal RAG.

Finally, a brief Q&A covers handling the large state space of multimodal data, the rationale for using tensors over fixed‑size vectors, and acknowledges the ongoing research and development in this area.