Production-Grade GraphRAG Data Pipeline: End-to-End Construction from PDF Parsing to Knowledge Graph
This article presents a production-grade hybrid data pipeline that integrates structured and unstructured data for intelligent customer service. It leverages Neo4j for structured knowledge graphs, MinerU+LitServe for multimodal PDF parsing, and Microsoft GraphRAG for semantic retrieval.
Why it matters
This hybrid data pipeline addresses a critical challenge in enterprise-level intelligent customer service, enabling seamless integration and retrieval of structured and unstructured data.
Key Points
- 1Addresses limitations of traditional RAG solutions in handling hybrid data (structured and unstructured)
- 2Utilizes Neo4j for storing structured knowledge graphs, MinerU+LitServe for multimodal PDF parsing, and GraphRAG for semantic retrieval
- 3Follows a layered decoupling and service-oriented architecture to ensure module independence and coordinated use of hybrid data
Details
The article discusses the challenges of handling hybrid data (structured and unstructured) in enterprise-level intelligent customer service scenarios. Traditional RAG solutions face difficulties in integrating structured data, parsing unstructured data, and coordinating hybrid retrieval. To address these limitations, the article presents a production-grade hybrid knowledge base data pipeline. It uses Neo4j for storing structured knowledge graphs, MinerU+LitServe for high-accuracy parsing of multimodal PDF content (text, tables, images, formulas), and Microsoft GraphRAG for semantic retrieval that combines knowledge graphs and semantic indexing. The overall architecture follows a layered decoupling and service-oriented design, separating data processing, index construction, and retrieval service to ensure module independence and coordinated use of hybrid data.
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