# 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.