# Neo4j Operations Implementation Complete ✅

## Summary

Successfully added complete Neo4j entity and relationship management to both shortterm and longterm memory repositories. Phase 2 is now **100% complete** with full PostgreSQL and Neo4j integration.

## What Was Added

### ShorttermMemoryRepository Neo4j Operations

**File:** `agent_mem/database/repositories/shortterm_memory.py` (expanded from 711 to 1,135 lines)

#### Entity Operations (5 methods)
- `create_entity()` - Create entity node with confidence tracking
  - Properties: external_id, shortterm_memory_id, name, type, description, confidence, metadata
  - Temporal: first_seen, last_seen
  
- `get_entity()` - Retrieve entity by Neo4j node ID

- `get_entities_by_memory()` - Get all entities for a shortterm memory
  - Ordered by confidence DESC
  
- `update_entity()` - Update entity properties
  - Updatable: name, description, confidence, metadata
  - Auto-updates: last_seen timestamp
  
- `delete_entity()` - Delete entity and all relationships
  - Uses DETACH DELETE to remove connected relationships

#### Relationship Operations (5 methods)
- `create_relationship()` - Create RELATES_TO relationship between entities
  - Properties: external_id, shortterm_memory_id, type, description, confidence, strength, metadata
  - Temporal: first_observed, last_observed
  - Returns: from_entity_name and to_entity_name for convenience
  
- `get_relationship()` - Retrieve relationship by Neo4j relationship ID

- `get_relationships_by_memory()` - Get all relationships for a shortterm memory
  - Ordered by strength DESC, confidence DESC
  
- `update_relationship()` - Update relationship properties
  - Updatable: description, confidence, strength, metadata
  - Auto-updates: last_observed timestamp
  
- `delete_relationship()` - Delete relationship

### LongtermMemoryRepository Neo4j Operations

**File:** `agent_mem/database/repositories/longterm_memory.py` (expanded from 632 to 1,119 lines)

#### Entity Operations (5 methods)
- `create_entity()` - Create entity node with importance scoring
  - Properties: external_id, name, type, description, confidence, importance, metadata
  - Temporal: start_date, last_updated
  - Key difference from shortterm: importance score instead of shortterm_memory_id
  
- `get_entity()` - Retrieve entity by Neo4j node ID

- `get_entities_by_external_id()` - Get all entities for an external_id
  - Filters: min_confidence, min_importance
  - Ordered by importance DESC, confidence DESC
  
- `update_entity()` - Update entity properties
  - Updatable: name, description, confidence, importance, metadata
  - Auto-updates: last_updated timestamp
  
- `delete_entity()` - Delete entity and all relationships
  - Uses DETACH DELETE

#### Relationship Operations (5 methods)
- `create_relationship()` - Create RELATES_TO relationship with importance
  - Properties: external_id, type, description, confidence, strength, importance, metadata
  - Temporal: start_date, last_updated
  - Key difference from shortterm: importance score instead of shortterm_memory_id
  
- `get_relationship()` - Retrieve relationship by Neo4j relationship ID

- `get_relationships_by_external_id()` - Get all relationships for an external_id
  - Filters: min_confidence, min_importance
  - Ordered by importance DESC, strength DESC, confidence DESC
  
- `update_relationship()` - Update relationship properties
  - Updatable: description, confidence, strength, importance, metadata
  - Auto-updates: last_updated timestamp
  
- `delete_relationship()` - Delete relationship

## Key Design Decisions

### 1. Node Labels
- **Shortterm**: `ShorttermEntity` label
- **Longterm**: `LongtermEntity` label
- Allows separate indexing and querying per tier

### 2. Relationship Type
- All relationships use `RELATES_TO` type
- Specific relationship type stored in `type` property
- Allows flexible relationship semantics

### 3. Temporal Tracking
- **Shortterm**: `first_seen`, `last_seen`, `first_observed`, `last_observed`
- **Longterm**: `start_date`, `last_updated`
- Different naming reflects different lifecycle management

### 4. Scoring System
- **Shortterm**: `confidence` (0-1)
- **Longterm**: `confidence` + `importance` (both 0-1)
- Longterm adds importance for prioritization

### 5. Query Returns
- Relationships return entity names (`from_entity_name`, `to_entity_name`)
- Convenient for display without additional queries

### 6. Delete Strategy
- `delete_entity()` uses `DETACH DELETE` - removes all connected relationships
- `delete_relationship()` uses simple `DELETE` - only removes the relationship

## Usage Examples

### Shortterm Entity/Relationship Example

```python
from agent_mem.database.repositories import ShorttermMemoryRepository

repo = ShorttermMemoryRepository(postgres, neo4j)

# Create entities
api_entity = await repo.create_entity(
    external_id="agent-123",
    shortterm_memory_id=memory_id,
    name="REST API",
    entity_type="Technology",
    description="RESTful web service architecture",
    confidence=0.9,
    metadata={"domain": "web"}
)

http_entity = await repo.create_entity(
    external_id="agent-123",
    shortterm_memory_id=memory_id,
    name="HTTP",
    entity_type="Protocol",
    description="Hypertext Transfer Protocol",
    confidence=0.95,
    metadata={"layer": "application"}
)

# Create relationship
relationship = await repo.create_relationship(
    external_id="agent-123",
    shortterm_memory_id=memory_id,
    from_entity_id=api_entity.id,
    to_entity_id=http_entity.id,
    relationship_type="USES",
    description="REST APIs use HTTP for communication",
    confidence=0.92,
    strength=0.88
)

# Update entity
await repo.update_entity(
    entity_id=api_entity.id,
    description="RESTful web service architecture using HTTP verbs",
    confidence=0.95
)

# Get all entities for memory
entities = await repo.get_entities_by_memory(memory_id)
print(f"Found {len(entities)} entities")

# Get all relationships
relationships = await repo.get_relationships_by_memory(memory_id)
for rel in relationships:
    print(f"{rel.from_entity_name} --[{rel.type}]--> {rel.to_entity_name}")
```

### Longterm Entity/Relationship Example

```python
from agent_mem.database.repositories import LongtermMemoryRepository

repo = LongtermMemoryRepository(postgres, neo4j)

# Create important entities
design_pattern = await repo.create_entity(
    external_id="agent-123",
    name="Singleton Pattern",
    entity_type="Design Pattern",
    description="Ensures a class has only one instance",
    confidence=0.98,
    importance=0.85,
    metadata={"category": "creational"}
)

python = await repo.create_entity(
    external_id="agent-123",
    name="Python",
    entity_type="Programming Language",
    description="High-level interpreted language",
    confidence=0.99,
    importance=0.9,
    metadata={"paradigm": "multi-paradigm"}
)

# Create relationship with importance
relationship = await repo.create_relationship(
    external_id="agent-123",
    from_entity_id=python.id,
    to_entity_id=design_pattern.id,
    relationship_type="IMPLEMENTS",
    description="Python can implement the Singleton pattern",
    confidence=0.95,
    strength=0.9,
    importance=0.8
)

# Get entities with filtering
important_entities = await repo.get_entities_by_external_id(
    external_id="agent-123",
    min_confidence=0.9,
    min_importance=0.8
)

# Get relationships with filtering
key_relationships = await repo.get_relationships_by_external_id(
    external_id="agent-123",
    min_confidence=0.9,
    min_importance=0.75
)
```

## Neo4j Cypher Query Patterns

### Entity Creation
```cypher
CREATE (e:ShorttermEntity {
    external_id: $external_id,
    shortterm_memory_id: $shortterm_memory_id,
    name: $name,
    type: $entity_type,
    description: $description,
    confidence: $confidence,
    first_seen: $now,
    last_seen: $now,
    metadata: $metadata
})
RETURN id(e) AS id, e.external_id AS external_id, ...
```

### Relationship Creation
```cypher
MATCH (from:ShorttermEntity), (to:ShorttermEntity)
WHERE id(from) = $from_entity_id AND id(to) = $to_entity_id
CREATE (from)-[r:RELATES_TO {
    external_id: $external_id,
    type: $relationship_type,
    confidence: $confidence,
    strength: $strength,
    ...
}]->(to)
RETURN id(r) AS id, from.name AS from_entity_name, to.name AS to_entity_name, ...
```

### Entity Update
```cypher
MATCH (e:ShorttermEntity)
WHERE id(e) = $entity_id
SET e.name = $name, e.confidence = $confidence, e.last_seen = $now
RETURN id(e) AS id, e.name AS name, ...
```

### Entity Delete with Relationships
```cypher
MATCH (e:ShorttermEntity)
WHERE id(e) = $entity_id
DETACH DELETE e
```

## Technical Notes

### Neo4j Session Management
All operations use async context managers:
```python
async with self.neo4j.session() as session:
    result = await session.run(query, **params)
    record = await result.single()
```

### ID Handling
- Neo4j uses internal node IDs (`id(e)`)
- These are integers returned as `id` property
- Stored in models as `id: int`

### Metadata Storage
- Metadata stored as JSON/dict in Neo4j
- Empty dict `{}` used as default instead of None
- Allows flexible property storage

### Error Handling
- No explicit error handling in these methods
- Relies on Neo4j driver exceptions
- TODO: Add try/except blocks in future refinement

## Updated Documentation

### Files Modified
1. **IMPLEMENTATION_CHECKLIST.md**
   - Phase 2 marked as ✅ COMPLETED (19/19 tasks)
   - Overall progress: 40% (37/94 tasks)
   
2. **PHASE2_COMPLETE.md**
   - Added Neo4j operation documentation
   - Added usage examples for entities and relationships
   - Updated file line counts
   - Updated status to 100% complete

## Statistics

### Code Added
- **ShorttermMemoryRepository**: +424 lines (711 → 1,135)
- **LongtermMemoryRepository**: +487 lines (632 → 1,119)
- **Total**: +911 lines of Neo4j operations

### Methods Added
- **Entity CRUD**: 5 methods × 2 repos = 10 methods
- **Relationship CRUD**: 5 methods × 2 repos = 10 methods
- **Total**: 20 new methods

### Test Coverage
- **Unit tests**: None yet (Phase 5)
- **Manual testing**: Not performed yet
- **TODO**: Add comprehensive tests

## Next Steps

### Phase 3: Memory Manager (Priority)
1. Implement `_consolidate_to_shortterm()` - Uses entities/relationships from active memory
2. Implement `_promote_to_longterm()` - Copies entities/relationships to longterm
3. Add automatic consolidation triggers
4. Complete `retrieve_memories()` with entity/relationship search

### Phase 4: Pydantic AI Agents
1. **Entity Extraction**: Agents can now use entity operations
2. **Relationship Extraction**: Agents can use relationship operations
3. **Conflict Resolution**: Merge duplicate entities, update relationships

### Future Enhancements
1. **Graph Queries**: Path finding between entities, pattern matching
2. **Batch Operations**: Bulk entity/relationship creation
3. **Entity Merging**: Detect and merge duplicate entities
4. **Relationship Inference**: Derive new relationships from existing ones
5. **Indexing**: Add Neo4j indexes for performance

## Completion Status

- ✅ **Phase 1**: Core Infrastructure (100%)
- ✅ **Phase 2**: Memory Tiers (100%)
  - ✅ PostgreSQL operations (chunks, search)
  - ✅ Neo4j operations (entities, relationships)
- ⏸️ **Phase 3**: Memory Manager (0%)
- ⏸️ **Phase 4**: Pydantic AI Agents (0%)
- ⏸️ **Phase 5**: Testing (0%)

**Overall Progress: 40% (37/94 tasks)**

---

**Date:** 2025-01-XX  
**Status:** ✅ Phase 2 COMPLETE  
**Milestone:** Full repository layer with PostgreSQL + Neo4j integration
