1.6 KiB
1.6 KiB
name, description, tags, version
| name | description | tags | version | ||||||
|---|---|---|---|---|---|---|---|---|---|
| km-rag-methodology | Expertise in implementing Knowledge-Map RAG (KM-RAG), focusing on structured Knowledge Units, Graph relationships, and multi-stage retrieval in .NET. |
|
1.0.0 |
KM-RAG Methodology
This skill provides a comprehensive framework for transitioning from basic chunk-based RAG to a structured Knowledge-Map RAG (KM-RAG) approach.
Core Concepts
- Knowledge Units (KU): Granular pieces of information with stable IDs and types (Section, Table, Definition, Rule).
- Knowledge Map (Graph): Explicit links between units (
Next,Defines,Contains) enabling contextual expansion. - Multi-Stage Retrieval: A pipeline starting with semantic candidate generation followed by graph expansion and optional reranking.
- Provenance & Governance: Full traceability of AI answers back to their source units.
Key Artifacts
- Core Concepts: Deep dive into the methodology.
- Implementation Patterns (.NET): C# code for units, links, and retrieval.
- Quality Checklist: Metrics and safety procedures.
- Deep Research Report: Original research on the KM-RAG approach.
Usage
Use this skill when:
- Designing or refactoring RAG systems for high precision.
- Implementing multi-tenant knowledge bases.
- Enhancing AI answers with structural context from a graph.
- Building evaluation pipelines for hallucination detection.