Exploring Multimodal GraphRAG: Document Intelligence, Knowledge Graphs, and Large‑Model Integration

1. Document‑Intelligence Parsing Pipeline

The workflow starts from a PDF, converts it to images, and performs layout analysis to segment blocks such as paragraphs, titles, formulas, and tables. Three main approaches are discussed:

OCR‑PIPELINE : Uses OCR to recognize text, extracts tables via CV, converts formulas to LaTeX, and finally reassembles the document into Markdown.

OCR‑FREE : An end‑to‑end multimodal model (e.g., olmOCR, mistral‑ORC) directly generates Markdown from images, but real‑world tests show hallucination and missing bounding‑box information.

PDF‑PARSE : For editable PDFs, PDFParser extracts text more accurately than OCR.

Advantages of OCR‑PIPELINE include access to bounding‑box data, modular optimization, CPU‑offline deployment, and support for scanned documents. Drawbacks are dependence on scene‑specific data, lower precision in layout and table parsing, and slower CPU inference.

OCR‑FREE suffers from lack of region‑level output, no CPU offline mode, high GPU consumption, large memory footprint for long texts, and noticeable hallucinations.

PDF‑PARSE is fast and accurate for editable PDFs but cannot handle scanned documents or complex layouts.

Layout Analysis

Layout analysis is treated as an object‑detection task. The state‑of‑the‑art model is DocLayout‑YOLO from Shanghai AI Lab, which improves generalisation through extensive multi‑scene annotation. A lightweight YOLOv8 model (6.23 MB) was open‑sourced for Chinese papers, English papers, research reports, and textbooks, offering fast inference in vertical scenarios.

Table parsing remains challenging: multi‑line, missing‑line, and border‑less tables are hard for traditional CV methods due to size, resolution, and cross‑page issues. The best open‑source table parser reported is Baidu’s SLANet‑plus , achieving high TEDS scores on line tables.

Formula parsing models trained on the ICPR 2024 multi‑line math expression task won the competition. The final architecture uses a VisionEncoder‑Decoder backbone, early‑stop training, and is evaluated with ExactMatch and EditDistance. An improved version, HDNet , was accepted by ICASSP and reaches 0.963 on the Fair‑CR metric with only ~300 M parameters.

Chart and Figure Processing

Charts (numeric, bar, pie) are converted to JSON for downstream rendering. Flowcharts are transformed into Mermaid syntax using multimodal models, replacing traditional CV segmentation pipelines.

Figure‑meta extraction (caption, reference, bounding‑box) can be performed by simple bounding‑box heuristics or supervised classifiers that distinguish flowcharts, numeric charts, and ordinary images.

2. Multimodal Graph Index Construction & Retrieval

Multimodal data (text, image, video, audio) are pre‑processed by dedicated modules. Text is tokenised or processed by LLMs; images are encoded with ViT; video frames use 3D‑CNN; audio is transcribed to text. Nodes (entities, images, video clips) and edges (temporal, semantic, cross‑modal) are stored in graph databases such as Neo4j or TigerGraph . Embeddings are indexed in vector stores like FAISS or Milvus .

Retrieval proceeds by chunking documents after layout analysis, then either:

Embedding text, tables, and images separately and storing them in a vector DB (traditional RAG).

Creating multimodal embeddings for each modality and performing joint vector search.

Query processing supports pure‑text or text‑plus‑image inputs, parses the query into multimodal components, performs sub‑graph matching, vector similarity, and cross‑modal alignment, fuses results, re‑ranks, and finally feeds the fused context to a large model for generation.

GraphRAG’s key advantages highlighted are finer‑grained retrieval, higher accuracy, and better interpretability.

3. Knowledge‑Graph Enhancement for Chunk Relations

Traditional RAG suffers from noisy chunk retrieval, poor numeric handling, isolated chunks, limited aggregation, and low explainability. Knowledge graphs (KG) inject expert knowledge, providing hierarchical entity features, parent‑of relations, co‑occurrence, and similarity links between chunks. Microsoft’s GraphRAG enriches chunk relations via KG‑based search and summary, improving relevance.

Building high‑quality, up‑to‑date large‑scale KGs is costly. In the document domain, three KG granularities are proposed:

Metadata‑level KG: nodes are document titles or topics, edges capture similarity or hierarchy.

Chunk‑level KG: nodes are identified blocks (titles, paragraphs, tables, images); edges encode parent‑child, co‑occurrence, or similarity.

Entity‑level KG: nodes are domain entities and relation keywords extracted from the text.

Typical Application Paradigms

KG‑enhanced prompt.

HiQA (hierarchical chunk recall).

LinkedIn KG‑RAG (dual‑embedding index).

UniQA‑Text2Cypher (KG‑RAG).

HippoRAG (entity‑specific KG).

GRAG (topology‑aware KG).

Microsoft GraphRAG (community extraction + KG summarisation).

KAG (full KG integration into RAG).

4. Comparative Analysis

| Approach | Strength | Weakness | |---|---|---| | RAG | Simple chunk‑vector retrieval. | Low precision, no semantic links, poor numeric handling, limited explainability. | | GraphRAG | Entity extraction, community summarisation, richer semantics. | Noisy KG construction, potential hallucinations, high resource cost. | | KG‑QA (pipeline) | Precise, logical reasoning, high confidence in numeric/time queries. | Expensive KG building, possible information loss, lower readability. |

Empirical observations: GPT‑4o scores 56.63 on flow‑chart QA, while open‑source Phi‑3‑Vision achieves higher scores, indicating that multimodal LLMs are still data‑driven.

5. Key Takeaways

Corpus preprocessing is the most critical RAG component; its quality directly impacts QA performance.

Multimodal LLMs open new possibilities (end‑to‑end processing, block‑wise enhancement) but resource constraints often favour pipeline solutions.

Deep document mining still requires human verification; automated checks are essential.

Document intelligence remains a hot research area, yet many long‑tail challenges persist.

Knowledge graphs must evolve—lighter, finer‑grained, and more flexible—while retaining structural benefits.

In resource‑limited, text‑heavy scenarios, traditional NLP/CV/BERT pipelines still hold practical value.

The presentation concludes with a Q&A session addressing deployment choices and title‑recognition optimisation.