# Production-Grade GraphRAG Data Pipeline: End-to-End Construction from PDF Parsing to Knowledge Graph

1. Introduction: The Hybrid Data Challenge in Intelligent Customer Service In enterprise-level intelligent customer service scenarios, the system must simultaneously handle two categories of core data: structured data (e.g., e-commerce orders, customer profiles, product inventory stored in relational databases) and unstructured data (e.g., PDF product manuals, service agreements, and after-sales guides). Traditional RAG solutions are typically designed for plain text only, and face three critical limitations when dealing with hybrid data: Difficulty integrating structured data: Order and customer data lives in relational databases. Traditional vector retrieval cannot efficiently leverage entity relationships, resulting in very low accuracy for complex queries such as "Find the logistics information for Customer A's Order B." Difficulty parsing unstructured data: PDF documents contain multimodal content — text, tables, images, and formulas. Conventional parsing tools (e.g., PyMuPDF) fre