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