manufacturing-graphrag
製造トレーサビリティ・インテリジェンス・プラットフォーム
Neo4j、Amazon Bedrock、MCP を使用した、製造トレーサビリティのための GraphRAG 搭載エージェント型 AI プラットフォーム。
アーキテクチャ
┌─────────────────────────────────────────────────────────────────────┐
│ INGESTION & ENRICHMENT LAYER │
│ Connectors → Chunking → Embeddings → Entity Extraction → │
│ Entity Resolution → Graph Construction │
└──────────────────────────┬──────────────────────────────────────────┘
│
┌──────────────────────────▼──────────────────────────────────────────┐
│ KNOWLEDGE GRAPH (Neo4j) │
│ Product → Requirement → Component → TestCase → TestRun → │
│ Defect → ChangeRequest │
└──────────────────────────┬──────────────────────────────────────────┘
│
┌──────────────────────────▼──────────────────────────────────────────┐
│ RETRIEVAL LAYER │
│ VectorRetriever │ GraphRAGRetriever │ NL2CypherRetriever │ Hybrid │
└──────────────────────────┬──────────────────────────────────────────┘
│
┌──────────────────────────▼──────────────────────────────────────────┐
│ AGENT LAYER │
│ LangGraph Agent (plan→retrieve→reason→validate→respond) │
│ Strands Agent (tool-calling with 5 specialized graph tools) │
└──────────────────────────┬──────────────────────────────────────────┘
│
┌──────────────────────────▼──────────────────────────────────────────┐
│ INTERFACE LAYER │
│ FastAPI REST │ MCP Server (Claude Desktop / Cursor compatible) │
└─────────────────────────────────────────────────────────────────────┘Related MCP server: Knowledge Graph MCP Server
クイックスタート
1. 前提条件
Neo4j 5.x(APOC プラグイン付き)
Python 3.11+
Bedrock アクセス権限を持つ AWS 認証情報(Claude 3.5 Sonnet + Titan Embed v2)
2. セットアップ
cd manufacturing-graphrag
python -m venv .venv
.venv\Scripts\activate # Windows
pip install -r requirements.txt
python -m spacy download en_core_web_sm
copy .env.example .env # Edit with your credentials3. Neo4j の起動(Docker)
docker-compose up neo4j -d4. ナレッジグラフのシード
set PYTHONPATH=src
python scripts/seed_data.py5. プラットフォームの起動
# API server
python main.py api
# MCP server (for Claude Desktop)
python main.py mcp
# Both
python main.py all6. ドキュメントの取り込み
python scripts/ingest.py path/to/spec.pdf path/to/requirements.csvAPI エンドポイント
メソッド | エンドポイント | 説明 |
GET |
| ヘルスチェック |
GET |
| ラベル別ノード数 |
POST |
| ドキュメントのアップロードと取り込み |
POST |
| 構造化 API レコードの取り込み |
POST |
| ハイブリッド GraphRAG クエリ |
POST |
| LangGraph 推論エージェント |
POST |
| Strands ツール呼び出しエージェント |
GET |
| 完全な欠陥トレーサビリティチェーン |
GET |
| 製品トレーサビリティの概要 |
クエリ例
# Hybrid GraphRAG query
curl -X POST http://localhost:8000/query \
-H "Content-Type: application/json" \
-d '{"question": "What components are affected by the thermal runaway defect?"}'
# LangGraph agent — multi-step reasoning
curl -X POST http://localhost:8000/agent/langgraph \
-d '{"question": "Trace the full impact chain of DEF-001 and identify all change requests needed"}'
# Strands agent — tool-calling
curl -X POST http://localhost:8000/agent/strands \
-d '{"question": "Which critical defects are blocking the EV BMS release?"}'
# Defect traceability
curl http://localhost:8000/traceability/defect/DEF-002MCP 統合(Claude Desktop)
config/claude_desktop_mcp.json の内容を Claude Desktop の claude_desktop_config.json にコピーします。
利用可能な MCP ツール:
ask_manufacturing_ai— ハイブリッド GraphRAG Q&Asemantic_search— ベクトル類似度検索graph_trace— グラフ走査による検索natural_language_to_cypher— NL2Cypherget_defect_chain— 完全な欠陥トレーサビリティget_requirement_traceability— 要件カバレッジproduct_health_dashboard— 製品メトリクス
ナレッジグラフのスキーマ
(Product)-[:HAS_REQUIREMENT]->(Requirement)
(Component)-[:IMPLEMENTS]->(Requirement)
(TestCase)-[:VALIDATES]->(Requirement)
(TestRun)-[:INSTANCE_OF]->(TestCase)
(TestRun)-[:FOUND_IN]->(Defect)
(Defect)-[:AFFECTS]->(Component)
(Defect)-[:TRIGGERS_CHANGE]->(ChangeRequest)
(ChangeRequest)-[:MODIFIES]->(Component)
(Document)-[:CONTAINS_CHUNK]->(Chunk)
(Chunk)-[:MENTIONS]->(any entity)テストの実行
set PYTHONPATH=src
pytest tests/ -vThis server cannot be installed
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