GraphRAG implementation creates excessive node and relationship types
🧭 workproduct problemSingle report ●●●●●
A developer building a personal assistant with GraphRAG found that using LangChain's MongoDBGraphStore resulted in 17 node types and 34 relationship types from only 5 documents, requiring the development of a custom 11-step pipeline to manage data modeling.
What people do today
- Defining strict ontologies to limit LLM output
- Implementing custom three-phase deduplication processes
- Using deterministic string IDs to prevent duplicate nodes
🔧 Fix this
Ideas a builder, maker or community group could start on:
- A library that provides pre-built, validated ontologies for common domains to reduce manual schema design
- A visual data modeling tool that allows developers to map raw document structures to graph schemas before running LLM extraction
- An automated normalization middleware that detects and merges redundant nodes based on semantic similarity during the ingestion phase
Real voices
“I had 17 node types and 34 relationship types from just 5 documents, including three versions of part of . GraphRAG is a data modeling problem, not a retrieval problem.”
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