GraphRAG over Hardware Datasheets, MCU Documentation Q&A
Overview
An internal build applying GraphRAG (graph-based retrieval-augmented generation) to dense hardware documentation. It ingests microcontroller datasheets, builds a knowledge graph of entities and relationships, and answers technical questions grounded in that graph.
Why It Exists
Hardware engineers wade through hundreds of pages of MCU datasheets and design checklists to answer precise technical questions. Vanilla vector RAG struggles with the cross-referenced, structured nature of these documents, GraphRAG’s entity graph and community summaries are a better fit for this kind of corpus.
What We Built
A GraphRAG pipeline driven by the standard prompt set, entity_extraction.txt, summarize_descriptions.txt, and community_report.txt, over an input corpus of Renesas MCU documentation converted to Markdown (RA8M1 datasheet and user manual, RZ/A3UL manual, and a board-circuit design checklist). The pipeline extracts entities/relationships, builds graph communities, and produces summarized reports used to answer queries about the hardware.
Technologies & Approach
Python with Microsoft’s GraphRAG framework. The approach favors knowledge-graph RAG over plain embeddings so that answers can traverse relationships between registers, peripherals, and design rules spread across multiple documents, exactly where graph structure adds value.
Outcome / Impact
A focused build that validated GraphRAG as a technique for technical-documentation Q&A, and demonstrated the studio’s ability to stand up a graph-RAG pipeline on real, domain-specific source material (semiconductor datasheets).
Capabilities Demonstrated
- Applying GraphRAG to specialized technical corpora
- Knowledge-graph construction via LLM entity/relationship extraction
- Domain-grounded document question answering
- Datasheet ingestion and Markdown normalization for RAG