GraphRAG pipeline: turning enterprise documents into knowledge graphs in five stages
adnan_hashmi · x · 2026-10-18
A shared overview of building enterprise GraphRAG systems with knowledge graphs, with a full architecture diagram:
- Parsing & chunking: PDFs parsed to Markdown via Docling, then split into chunks with LangChain splitters;
- Entity extraction: an LLM extracts entities from each chunk;
- Type extraction: LLM-extracted entity types are merged using a cache of existing types plus semantic clustering;
- Relationship extraction: same iterative pattern — cached existing relations + relation semantic clustering;
- Aggregation & storage: extracted entities/relations are deduplicated, aggregated, post-processed, then written to a schema and graph database.
The key design is incremental deduplication via "cache existing types/relations + semantic clustering," preventing duplicate entity types across chunks — a practical reference flow for enterprise knowledge-graph RAG.
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