PDF file processing and automated modeling segmentation architecture diagram

1. General Overview

This architecture diagram describes the automated process from PDF input to generating graph models and vector models, with the following highlights:

• • PDF type detection and text extraction

• • Industry classification and content analysis

• • Dynamically create graph and vector models

• • Stored in graph database and vector database

2. Architecture Modules

2.1 Input Module

• •  Input : PDF file (such as your_document.pdf）

• •  Extract the first 1-10 pages :

• • use PyMuPDF Extract text-based PDF content

• • use pytesseract + pdf2image Extract scanned PDF content

2.2 PDF type detection and text extraction

• •  tool :

• • PyMuPDF: Processing text-based PDF

• • pytesseract：Processing scanned PDF

• •  Output : Original text of the first 1-10 pages

2.3 Industry Classification and Content Analysis

• •  tool :

• • Keyword matching (regular expressions)

• • NLP models (such as spaCy) or LLM (Grok 3) for classification

• •  Industry classification rules :

• • Medical: Keywords such as "disease", "treatment", "drug"

• • Legal: Keywords such as "law", "contract", "clause"

• • Technology: Keywords such as “technology”, “algorithm”, “system”

• •  Output : Industry tags (e.g. “medical”) and structured data (JSON/Markdown)

2.4 Dynamic Modeling Module

Select appropriate tools and models according to the industry, and dynamically create graph models and vector models.

2.4.1 Graph Model Creation

• •  Medical industry graph model :

• • Node:Chapter,Section,Disease,Treatment

• • relation:CONTAINS,TREATS

• •  Legal Industry Graph Model :

• • Node:Clause,Party,Contract

• • relation:BELONGS_TO,SIGNATORY

• •  Technology Industry Graph Model :

• • Node:Section,Technology,Process

• • relation:DEPENDS_ON,IMPLEMENTS

• •  Tools : Neo4j Driver

2.4.2 Vector model creation

• •  Embedding model selection :

• • Medical:paraphrase-multilingual-MiniLM-L12-v2 or BioBERT

• • Legal: LegalBERT

• • technology:all-MiniLM-L6-v2 or TechBERT

• •  Tools : Sentence Transformers, Pinecone

2.5 Storage Module

Neo4j stores graph databases Pinecone stores vector databases

• •  Graph database : Neo4j (stores entities and relationships)

• •  Vector database : Pinecone (stores vectors and metadata)

3. Process example (medical industry PDF)

Based on your PDF example ("Chapter 1: Drugs for Respiratory Diseases"):

3.1 Input

• • PDF files:your_document.pdf

3.2 Extraction and detection

• • Extract the first 1-10 pages of text (using PyMuPDF, text-based PDF)

• • Text example:

  Chapter 1 Medication for Respiratory Diseases
  1.1 Acute upper respiratory tract infection
  1. Disease Overview
  Acute bronchitis, treatment: inhaled hormones, which have anti-inflammatory effects.

3.3 Industry Classification

• • Keywords: "disease", "treatment", "drug" → Industry classification: "medical"

3.4 Dynamic Modeling

• •  Graph Model :

• • Node:Chapter(Chapter 1),Section(1.1 Acute upper respiratory tract infection),Disease(Acute Bronchitis),Treatment(Inhaled hormones)

• • relation:CONTAINS(Chapters contain subsections),TREATS(Disease-related treatment)

• •  Vector Model :

• • Embedding Model:paraphrase-multilingual-MiniLM-L12-v2

• • Vectorized text: vectors are generated for each chapter, disease, treatment

3.5 Storage

• • Graph database: Neo4j stores graph models

• • Vector database: Pinecone stores vectors

4. Tools and Dependencies

• •  Python Libraries :

• • PyMuPDF: Text Extraction

• • pytesseract + pdf2image:OCR

• • sentence-transformers: Vectorization

• • neo4j: Graph Database

• • pinecone-client: Vector database

• • spaCy or Hugging Face Transformers: NLP analysis

• •  External Services :

• • Grok 3 (or similar LLM): Industry classification and structuring

• • Neo4j, Pinecone API

5. Notes

• •  Performance optimization : Use parallel processing for large PDFs

• •  Error handling : OCR noise cleaning, structured error detection

• •  Scalability : add classification rules for new industries

• •  Privacy protection : sensitive data is encrypted and stored