# KNOWLEDGE GRAPH MASTERY PRD

## Vision
AIVA develops world-class knowledge graph capabilities for comprehensive entity relationships, intelligent retrieval, and cross-domain knowledge synthesis.

## Stories

### Story 1: Current Knowledge Graph Architecture Audit
**As** AIVA
**I need to** audit existing Genesis knowledge graph implementation
**So that** I understand what's built and identify gaps

**Acceptance Criteria:**
- Review all files in E:\genesis-system\knowledge\
- Document current Neo4j integration status
- Map existing entity types and relationships
- Identify missing capabilities
- Output: KNOWLEDGE_GRAPH_AUDIT_REPORT.md

### Story 2: Neo4j Deep Dive Research
**As** AIVA
**I need to** research Neo4j best practices via YouTube transcripts and documentation
**So that** I understand advanced graph database patterns

**Acceptance Criteria:**
- Extract 10+ YouTube transcripts on Neo4j patterns
- Study Cypher query optimization
- Research graph modeling patterns
- Document in knowledge/neo4j_mastery.md

### Story 3: Graph RAG Implementation
**As** AIVA
**I need to** implement Graph RAG (Retrieval Augmented Generation)
**So that** queries leverage knowledge graph context

**Acceptance Criteria:**
- Create knowledge/graph_rag/retriever.py
- Implement entity-aware retrieval
- Build relationship traversal queries
- Test with Genesis context queries
- 90%+ relevance on test queries

### Story 4: Entity Extraction Pipeline
**As** AIVA
**I need to** build automated entity extraction from conversations
**So that** knowledge graph grows from interactions

**Acceptance Criteria:**
- Create knowledge/entity_extractor/pipeline.py
- Extract entities: People, Organizations, Tools, Concepts, Projects
- Extract relationships: uses, creates, depends_on, related_to
- Process conversation history automatically
- Output entities to Neo4j

### Story 5: Relationship Inference Engine
**As** AIVA
**I need to** infer relationships between entities
**So that** knowledge graph captures implicit connections

**Acceptance Criteria:**
- Create knowledge/inference/relationship_inferrer.py
- Implement co-occurrence analysis
- Build semantic similarity detection
- Infer missing relationships with confidence scores
- Validate inferred relationships

### Story 6: Knowledge Synthesis Workflows
**As** AIVA
**I need to** create workflows that synthesize knowledge across domains
**So that** I can answer complex cross-domain queries

**Acceptance Criteria:**
- Create knowledge/synthesis/cross_domain.py
- Build multi-hop query patterns
- Implement domain bridging logic
- Test: "How does AgileAdapt relate to Genesis?"
- Generate synthesis reports

### Story 7: Query Optimization Framework
**As** AIVA
**I need to** optimize Cypher queries for performance
**So that** knowledge retrieval is fast

**Acceptance Criteria:**
- Create knowledge/optimization/query_optimizer.py
- Implement query caching
- Build index recommendations
- Profile slow queries
- Achieve <100ms average query time

### Story 8: Graph Visualization Interface
**As** AIVA
**I need to** create knowledge graph visualization
**So that** Kinan can explore entity relationships

**Acceptance Criteria:**
- Create knowledge/visualization/graph_viewer.py
- Export to D3.js compatible format
- Build filtering by entity type
- Implement relationship highlighting
- Output interactive HTML

### Story 9: Incremental Update System
**As** AIVA
**I need to** implement incremental knowledge graph updates
**So that** graph stays current without full rebuilds

**Acceptance Criteria:**
- Create knowledge/updates/incremental_updater.py
- Process new conversations only
- Handle entity merging and deduplication
- Track update history
- Support rollback

### Story 10: Cross-Domain Linking Engine
**As** AIVA
**I need to** link entities across different domains
**So that** knowledge is unified

**Acceptance Criteria:**
- Create knowledge/linking/cross_domain_linker.py
- Link: Genesis entities ↔ AgileAdapt entities
- Link: Tool entities ↔ Capability entities
- Link: Research entities ↔ Implementation entities
- Build link confidence scoring

### Story 11: Knowledge Decay Detection
**As** AIVA
**I need to** detect stale knowledge
**So that** outdated information is flagged

**Acceptance Criteria:**
- Create knowledge/quality/decay_detector.py
- Track entity last-updated timestamps
- Flag entities not referenced in 30+ days
- Suggest refresh queries
- Generate staleness reports

### Story 12: Semantic Search Integration
**As** AIVA
**I need to** integrate Qdrant vector search with knowledge graph
**So that** queries combine semantic and structural search

**Acceptance Criteria:**
- Create knowledge/hybrid/semantic_graph_search.py
- Embed entities in Qdrant
- Build hybrid query pipeline
- Combine vector similarity with graph traversal
- Test: "Find tools similar to what I used for X"

### Story 13: Knowledge Graph Testing Framework
**As** AIVA
**I need to** create comprehensive tests for knowledge graph
**So that** graph quality is validated

**Acceptance Criteria:**
- Create tests/knowledge/test_graph.py
- Test entity extraction accuracy
- Test relationship correctness
- Test query performance
- Test incremental updates
- 90%+ test coverage

### Story 14: Knowledge Provenance Tracking
**As** AIVA
**I need to** track where knowledge came from
**So that** facts can be verified

**Acceptance Criteria:**
- Create knowledge/provenance/tracker.py
- Store source for each entity/relationship
- Link to conversation IDs
- Link to research sources
- Build audit trail

### Story 15: Knowledge Graph Documentation
**As** AIVA
**I need to** document the complete knowledge graph system
**So that** the architecture is understood

**Acceptance Criteria:**
- Create docs/KNOWLEDGE_GRAPH_ARCHITECTURE.md
- Document entity schema
- Document relationship types
- Document query patterns
- Include operational guides

## Success Metrics
- 1000+ entities in knowledge graph
- 5000+ relationships mapped
- <100ms average query time
- 90%+ entity extraction accuracy
- Hybrid search operational
