feat: implement KM-RAG methodology artifacts and core architectural standards with supporting query and service updates

.agent/skills/km-rag-methodology/artifacts/core_concepts.md

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+# Core Concepts of KM-RAG (Knowledge-Map RAG)
+
+Knowledge-Map RAG (KM-RAG) shifts the paradigm from "mechanical chunking" to "structured knowledge engineering".
+
+## 1. From Chunks to Knowledge Units (KU)
+Instead of random character-based splits, knowledge is partitioned into **Knowledge Units** that preserve structural meaning:
+- **Unit Types**: `Section`, `Table`, `Definition`, `ProcedureStep`, `PolicyRule`.
+- **Properties**: Stable ID, Version, Canonical Text, Rendered Context, Provenance (source, page, path).
+
+## 2. The Knowledge Map (Graph)
+Relationships between Knowledge Units are explicitly modeled to enhance retrieval and context assembly:
+- `HAS_UNIT`: Document contains Unit.
+- `NEXT` / `PREVIOUS`: Sequential flow between units.
+- `DEFINES`: Unit defines a specific entity or term.
+- `REFERENCES`: Unit refers to another unit.
+- `EXCEPTION_OF`: Unit describes an exception to a rule in another unit.
+
+## 3. Retrieval Strategy: "Plan over Similarity"
+Retrieval is not just `top-k` similarity but a multi-stage process:
+1. **Candidate Generation**: Hybrid search (Vector + Keyword) to find potential matches.
+2. **Graph Expansion**: Pulling related units (e.g., "Get the section this table belongs to" or "Get the definition of term X used here").
+3. **Reranking**: Using a Cross-Encoder to precisely score the expanded candidates.
+4. **Context Assembly**: Building a grounded context with explicit citations.
+
+## 4. Governance and Provenance
+- **Audit Trail**: Every answer must be traceable back to specific Knowledge Units with valid provenance.
+- **Permission-Aware**: Retrieval filters must enforce ACLs at the unit/graph level before the LLM sees the data.
+- **Continuous Evaluation**: Monitoring "Faithfulness" (groundedness) and "Answer Relevance" using tools like RAGAS or TruLens.