Unlocking Multimodal RAG: From Semantic Extraction to Scalable VLM Solutions

Introduction

The session focuses on the implementation routes and development outlook of multimodal Retrieval‑Augmented Generation (RAG), aiming to build a tightly integrated system that seamlessly fuses text, images, and other media for richer user interaction.

Five Core Topics

Semantic‑extraction‑based multimodal RAG

Transformer‑based multimodal RAG

Scaling multimodal RAG built on Visual Language Models (VLM)

Choosing the technical roadmap

Q&A session

Technical Paths

Three main technical routes are presented:

Traditional object recognition and parsing ("carved" route) – Uses OCR to extract text, tables, and figures from images, then parses each object into a textual format for downstream retrieval.

Transformer‑based architecture – Encodes whole documents with a Transformer encoder and decodes them into readable text, improving contextual coherence compared with CNN‑based methods.

Visual Language Model (VLM) approach – Directly processes multimodal inputs, converting them into patch embeddings and multi‑vector tensors that enhance retrieval and generation capabilities.

Traditional "Carved" Route

The "carved" route handles every document detail: layout recognition separates paragraphs, tables, and charts; OCR transcribes text; specialized models parse charts and tables. Although thorough, it is labor‑intensive and less automated, making large‑scale processing challenging.

The RAG architecture splits documents into chunks, embeds each chunk, and stores vectors in a vector database for similarity search. Advanced pipelines add full‑text, dense, sparse, and graph indexes, followed by a Tensor Reranker before feeding prompts to a large model.

Transformer‑Based Table Parsing

A Transformer model parses table structures, handling merged cells, multi‑page tables, and embedded graphics. The pipeline extracts patch embeddings via a Variational Auto‑Encoder (VAE), builds a codebook, and decodes accurate HTML tables after rigorous validation.

VLM‑Based Multimodal RAG

VLMs simultaneously process image and text, enabling fine‑grained understanding such as locating objects in images and answering visual questions. Recent models (e.g., GPT‑4o, open‑source alternatives) have accelerated multimodal capabilities.

Examples include using PaliGemma to answer PDF‑based queries with precise chart values and Qwen2 to interpret graphical content accurately.

ColPali Method and Evaluation

ColPali converts multimodal documents into high‑dimensional tensors (e.g., 1024 patches × 128‑dim vectors) and uses similarity matching followed by large‑model generation. Evaluations on datasets like MLDR show nDCG > 80%, a significant improvement over earlier methods.

Scaling Challenges and Tensor Indexing

Scaling introduces tensor complexity; a single PDF may require 1024 vectors, increasing storage and compute costs. Indexing offers limited relief, so binary quantization and tensor‑based reranking are employed. Infinity database provides unified indexes for full‑text, dense, sparse, and tensor searches, enabling fusion search.

Experimental results show that combining multiple search types and adding tensor reranking markedly improves accuracy compared with BM25 or dense‑only search.

Future Directions

Delayed‑interaction models (e.g., JaColBERT, Jina‑ColBERT v2) are identified as the next evolution for RAG, offering efficient large‑scale retrieval.

Choosing a Technical Roadmap

Both the carved OCR route and VLM approach have merits: VLM excels with abstract images, while OCR is better for structured documents. Long‑term coexistence is expected, with transformer‑based OCR, combined OCR‑VLM pipelines, and tensor‑based delayed interaction becoming standard.

Q&A Highlights

Q1: How to handle the larger state space of multimodal data compared to pure text? A1: Mapping charts to Excel is ideal but impractical due to existing document volume and visual quality concerns.

Q2: Why choose tensors over vectors? A2: Tensors allow flexible length for variable‑size data, which fixed‑dimensional vectors cannot directly accommodate.