# Technical Specification - Graph Rag

**Version**: v1.2.3 | **Status**: Active | **Last Updated**: March 2026

**Module**: `codomyrmex.graph_rag`  
**Last Updated**: 2026-01-29

## 1. Purpose

Knowledge graph integration with RAG for structured knowledge retrieval and reasoning

## 2. Architecture

### 2.1 Components

```
graph_rag/
├── __init__.py          # Module exports
├── README.md            # Documentation
├── AGENTS.md            # Agent guidelines
├── SPEC.md              # This file
├── PAI.md               # Personal AI context
└── core.py              # Core implementation
```

### 2.2 Dependencies

- Python 3.10+
- Parent module: `codomyrmex`

## 3. Interfaces

### 3.1 Public API

```python
from codomyrmex.graph_rag import (
    KnowledgeGraph,    # Core knowledge graph with entities and relationships
    GraphRAGPipeline,  # End-to-end RAG pipeline with graph context
    Entity, EntityType,
    Relationship, RelationType,
    GraphContext,
)
```

### 3.2 Configuration

Environment variables:
- `CODOMYRMEX_*`: Configuration options

## 4. Implementation Notes

### 4.1 Design Decisions

1. **In-memory graph**: `KnowledgeGraph` stores entities and relationships in-memory dicts; suitable for session-scoped knowledge, not persistent storage.
2. **Pipeline pattern**: `GraphRAGPipeline` wraps graph construction + retrieval into a single callable, keeping consumer code simple.

### 4.2 Limitations

- Graph is not persisted across sessions (in-memory only); use `agentic_memory` for durable storage.
- No graph database backend — does not support very large-scale graphs (>100K nodes).

## 5. Testing

```bash
# Run tests for this module
pytest tests/graph_rag/
```

## 6. Future Considerations

- Graph database backend integration (Neo4j, NetworkX) to lift the current 100K-node ceiling on in-memory graphs.
- Persistent graph storage across sessions: serialize the knowledge graph to disk and reload incrementally rather than rebuilding from source on every session start.
- Multi-hop reasoning chains: expose a query interface that traces paths of configurable depth through the graph and returns confidence-weighted inference chains.

## Navigation

- **Self**: `SPEC.md`
- **Parent**: [../README.md](../README.md)
- **Readme**: [README.md](README.md)
- **Agents**: [AGENTS.md](AGENTS.md)
- **Repository Root**: [README.md](../../../README.md)
