# Knowledge Graph Connectivity Enhancement - Implementation Summary
## Status: Phase 1 Complete
### Completed Tasks
#### 1. Schema Migration ✅
- **File:** `/Users/les/Projects/session-buddy/scripts/add_kg_embedding_column.py`
- **Action:** Added `embedding FLOAT[384]` column to `kg_entities` table
- **Status:** Column successfully added and verified
- **Database:** `~/.claude/data/knowledge_graph.duckdb`
#### 2. Migration Script Created ✅
- **File:** `/Users/les/Projects/session-buddy/scripts/migrate_knowledge_graph_embeddings.py`
- **Features:**
- Batch processing of entities
- Embedding generation using existing ONNX model
- Progress tracking and error handling
- Dry-run mode for testing
- **Note:** Script works but embedding generation is slow for 597 entities
- **Decision:** Generate embeddings on-demand instead of batch migration
### Current Knowledge Graph State
```
Database: ~/.claude/data/knowledge_graph.duckdb (58.0 MB)
Schema:
kg_entities (597 rows)
- id, name, entity_type
- observations, properties
- created_at, updated_at, metadata
- embedding FLOAT[384] ✅ NEW
kg_relationships (19 rows)
- id, from_entity, to_entity
- relation_type, properties
- created_at, updated_at, metadata
Connectivity Issues:
- 597 entities, 19 relationships = 0.032 ratio (very low)
- 312 test entities (52%) polluting production graph
- No automatic relationship discovery
- No semantic similarity linking
```
### Next Steps (Prioritized)
#### Priority 1: Auto-Discovery System
**Files to Modify:**
1. `session_buddy/adapters/knowledge_graph_adapter_oneiric.py`
2. `session_buddy/mcp/tools/collaboration/knowledge_graph_tools.py`
**Implementation:**
```python
# In knowledge_graph_adapter_oneiric.py
async def create_entity(
self,
name: str,
entity_type: str,
observations: list[str] | None = None,
properties: dict[str, t.Any] | None = None,
metadata: dict[str, t.Any] | None = None,
auto_discover: bool = True, # NEW: Enable auto-discovery
) -> dict[str, t.Any]:
"""Create entity with optional automatic relationship discovery."""
# Create entity
entity = await self._create_entity_impl(...)
# Generate embedding
if auto_discover:
embedding = await self._generate_entity_embedding(entity)
# Update entity with embedding
# Discover similar entities
similar = await self._find_similar_entities(
embedding=embedding,
threshold=0.75,
limit=10
)
# Create relationships
for similar_entity in similar:
await self.create_relation(
from_entity=name,
to_entity=similar_entity["name"],
relation_type="related_to",
properties={"similarity": similar_entity["score"]}
)
return entity
```
#### Priority 2: Semantic Similarity Search
```python
async def _find_similar_entities(
self,
embedding: list[float] | None = None,
entity_name: str | None = None,
threshold: float = 0.75,
limit: int = 10,
) -> list[dict[str, t.Any]]:
"""Find semantically similar entities using cosine similarity."""
conn = self._get_conn()
# Use provided embedding or generate from entity name
if embedding is None:
entity = await self.find_entity_by_name(entity_name)
embedding = entity.get("embedding")
if not embedding:
return []
# DuckDB cosine similarity search
result = conn.execute("""
SELECT
name,
entity_type,
array_cosine_similarity(embedding, ?) as similarity
FROM kg_entities
WHERE embedding IS NOT NULL
AND array_cosine_similarity(embedding, ?) > ?
AND name != ?
ORDER BY similarity DESC
LIMIT ?
""", (embedding, embedding, threshold, entity_name, limit)).fetchall()
return [
{
"name": row[0],
"entity_type": row[1],
"score": float(row[2])
}
for row in result
]
```
#### Priority 3: New MCP Tools
```python
# Tool 1: discover_relationships
@mcp_server.tool()
async def discover_relationships(
similarity_threshold: float = 0.75,
limit: int = 50
) -> str:
"""Automatically discover and create relationships between similar entities."""
# Implementation creates relationships for entities without connections
# Tool 2: generate_embeddings
@mcp_server.tool()
async def generate_embeddings(
entity_type: str | None = None,
batch_size: int = 20
) -> str:
"""Generate embeddings for entities missing them."""
# Implementation processes entities without embeddings
# Tool 3: analyze_graph_connectivity
@mcp_server.tool()
async def analyze_graph_connectivity() -> str:
"""Analyze graph health and connectivity metrics."""
# Returns connectivity ratio, isolated entities, recommendations
# Tool 4: create_semantic_relation
@mcp_server.tool()
async def create_semantic_relation(
from_entity: str,
to_entity: str,
context: str | None = None
) -> str:
"""Create relationship with automatic type detection."""
# Analyzes context to determine best relationship type
```
#### Priority 4: Enhanced Statistics
```python
async def get_stats(self) -> dict[str, t.Any]:
"""Get enhanced knowledge graph statistics."""
conn = self._get_conn()
# Basic counts
entity_count = conn.execute("SELECT COUNT(*) FROM kg_entities").fetchone()[0]
relationship_count = conn.execute("SELECT COUNT(*) FROM kg_relationships").fetchone()[0]
# Connectivity metrics
isolated_entities = conn.execute("""
SELECT COUNT(DISTINCT e.id)
FROM kg_entities e
LEFT JOIN kg_relationships r ON (e.id = r.from_entity OR e.id = r.to_entity)
WHERE r.id IS NULL
""").fetchone()[0]
# Connectivity ratio
connectivity_ratio = relationship_count / entity_count if entity_count > 0 else 0
# Embedding coverage
entities_with_embeddings = conn.execute(
"SELECT COUNT(*) FROM kg_entities WHERE embedding IS NOT NULL"
).fetchone()[0]
return {
"total_entities": entity_count,
"total_relationships": relationship_count,
"connectivity_ratio": connectivity_ratio,
"isolated_entities": isolated_entities,
"embedding_coverage": entities_with_embeddings / entity_count if entity_count > 0 else 0,
"entity_types": {...},
"relationship_types": {...},
}
```
### Implementation Order
1. **Week 1: Core Auto-Discovery**
- [ ] Add `_generate_entity_embedding()` method
- [ ] Add `_find_similar_entities()` method
- [ ] Modify `create_entity()` to support `auto_discover` parameter
- [ ] Add embedding generation on entity creation
2. **Week 2: MCP Tools**
- [ ] Implement `discover_relationships` tool
- [ ] Implement `generate_embeddings` tool
- [ ] Implement `analyze_graph_connectivity` tool
- [ ] Implement `create_semantic_relation` tool
3. **Week 3: Enhanced Statistics & Testing**
- [ ] Enhance `get_stats()` with connectivity metrics
- [ ] Add unit tests for auto-discovery
- [ ] Add integration tests for semantic search
- [ ] Performance testing with 1000+ entities
4. **Week 4: Data Quality**
- [ ] Clean up test entities (or filter them out)
- [ ] Validate existing relationships
- [ ] Add relationship confidence scoring
- [ ] Documentation and examples
### Expected Results
**Before (Current):**
- Entities: 597
- Relationships: 19
- Connectivity: 0.032
- Embedding coverage: 0%
**After (Target):**
- Entities: 597
- Relationships: 200-500 (3-8x increase)
- Connectivity: 0.33-0.84 (10-25x improvement)
- Embedding coverage: 100%
- Auto-discovery: Enabled for new entities
### Testing Strategy
```bash
# 1. Test schema changes
python scripts/test_database_status.py
# 2. Test embedding generation
python scripts/generate_test_embeddings.py
# 3. Test auto-discovery
pytest tests/unit/test_knowledge_graph_discovery.py -v
# 4. Test MCP tools
pytest tests/integration/test_knowledge_graph_tools.py -v
# 5. Performance test
pytest tests/performance/test_kg_scaling.py -v
```
### Risks & Mitigation
**Risk 1: Performance**
- Embedding generation: ~50ms per entity
- Similarity search: Fast with DuckDB cosine similarity
- Mitigation: Batch processing, async operations
**Risk 2: Low-Quality Relationships**
- Auto-discovery might create noisy relationships
- Mitigation: High similarity threshold (0.75), manual review tools
**Risk 3: Database Size**
- 597 entities × 1.5 KB = ~900 KB for embeddings
- Mitigation: Monitor size, implement retention if needed
### Success Metrics
- [ ] 100% embedding coverage for all entities
- [ ] Connectivity ratio > 0.2 (10x improvement)
- [ ] < 100 isolated entities (down from ~580)
- [ ] Average relationship confidence > 0.75
- [ ] Graph query performance < 100ms
- [ ] Auto-discovery enabled by default
### Files Modified
1. **Created:**
- `/Users/les/Projects/session-buddy/scripts/add_kg_embedding_column.py`
- `/Users/les/Projects/session-buddy/scripts/migrate_knowledge_graph_embeddings.py`
- `/Users/les/Projects/session-buddy/KNOWLEDGE_GRAPH_CONNECTIVITY_PLAN.md`
- `/Users/les/Projects/session-buddy/IMPLEMENTATION_SUMMARY.md`
2. **To Modify:**
- `session_buddy/adapters/knowledge_graph_adapter_oneiric.py`
- `session_buddy/mcp/tools/collaboration/knowledge_graph_tools.py`
- `tests/unit/test_knowledge_graph_adapter.py`
### Next Actions
1. ✅ Add embedding column to schema
2. ⏳ Implement auto-discovery in adapter
3. ⏳ Add semantic similarity search
4. ⏳ Create new MCP tools
5. ⏳ Enhance statistics reporting
6. ⏳ Add comprehensive tests
7. ⏳ Clean up test data
8. ⏳ Document usage patterns
---
**Phase 1 Status:** ✅ COMPLETE
**Phase 2 Status:** ⏳ PENDING (Auto-Discovery Implementation)
**Overall Progress:** 20% COMPLETE
