Knowledge Graph

The Knowledge Graph is the backbone of RaiSE’s context system. It merges everything — memory patterns, governance documents, skills metadata, work tracking, and discovered components — into a single graph of connected concepts.

What It Is

A directed graph where:

• Nodes are concepts — patterns, principles, requirements, skills, stories, components, modules
• Edges are relationships — “learned from”, “governed by”, “depends on”, “constrained by”

When you run rai memory build, the CLI traverses all project sources and assembles this graph. When you query with rai memory query or rai memory context, you’re searching this graph.

Node Types

Type	ID Pattern	Source	Example
Pattern	PAT-*, BASE-*	Memory JSONL files	”Use fixtures for database tests”
Calibration	CAL-*	Calibration records	Story S3.5: M size, 45 min actual
Session	SES-*	Session history	”Implemented auth module”
Principle	§N	Constitution	”Simple heuristics over complex ML”
Requirement	RF-*	PRD	”Marketing website with craftsman tone”
Guardrail	GR-*	Guardrails	”MUST: No vanity metrics as goals”
Skill	/name	SKILL.md files	/rai-story-plan — decompose into tasks
Story	S*.*	Work tracking	S8.6: Docs Getting Started
Epic	E*	Epic scopes	E8: Website v1 + Docs
Component	comp-*	Discovery scan	SessionManager class
Module	mod-*	Discovery analysis	mod-memory — memory subsystem
Decision	ADR-*	Architecture decisions	ADR-019: Unified context graph

Edge Types

Edges express how concepts relate:

Edge	Meaning	Example
learned_from	Pattern came from this session	PAT-042 → SES-015
governed_by	Requirement implements a principle	RF-01 → §2
implements	Story implements a requirement	S8.6 → RF-05
part_of	Story belongs to an epic	S8.6 → E8
depends_on	Module depends on another	mod-session → mod-memory
belongs_to	Module belongs to a domain	mod-memory → bc-core
constrained_by	Domain is constrained by a guardrail	bc-core → GR-015
applies_to	Pattern applies to a skill	PAT-001 → /rai-story-implement

Building the Graph

rai memory build

This merges all sources:

1. Governance: principles, requirements, guardrails from governance/
2. Memory: patterns, calibration, sessions from .raise/rai/memory/
3. Work: epic and story scopes from work/epics/
4. Skills: metadata from .claude/skills/*/SKILL.md
5. Components: discovered code from work/discovery/

The output is .raise/rai/memory/index.json.

Querying the Graph

Keyword Search

Find concepts by content:

rai memory query "testing patterns"

Concept Lookup

Find a specific concept by ID:

rai memory query "PAT-001" --strategy concept_lookup

Module Context

Get the full architectural context for a module — its domain, layer, constraints, and dependencies:

rai memory context mod-memory

This returns:

• Bounded context: which domain the module belongs to
• Layer: its position in the architecture (leaf, domain, integration, orchestration)
• Constraints: applicable guardrails (MUST and SHOULD)
• Dependencies: what it depends on and what depends on it

Validation

Check the graph for structural issues:

rai memory validate

This detects cycles in dependency relationships, invalid edge types, and dangling references.

Why a Graph

The graph structure enables contextual queries — not just “find this keyword” but “show me everything related to this module, including the rules that constrain it and the patterns learned while building it.”

When your AI partner runs rai session start --context, the CLI assembles a context bundle by traversing this graph. The result is a compressed view of everything relevant to your current work — not a dump of all files, but a curated selection of the most important nodes and their relationships.