manufacturing-graphrag
制造可追溯性智能平台
基于 GraphRAG 的智能体 AI 平台,用于制造可追溯性,使用 Neo4j、Amazon Bedrock 和 MCP。
架构
┌─────────────────────────────────────────────────────────────────────┐
│ 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
快速开始
1. 先决条件
Neo4j 5.x(带 APOC 插件)
Python 3.11+
具有 Bedrock 访问权限的 AWS 凭证(Claude 3.5 Sonnet + Titan Embed v2)
2. 设置
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. 启动 Neo4j(Docker)
docker-compose up neo4j -d4. 填充知识图谱
set PYTHONPATH=src
python scripts/seed_data.py5. 启动平台
# API server
python main.py api
# MCP server (for Claude Desktop)
python main.py mcp
# Both
python main.py all6. 摄取文档
python scripts/ingest.py path/to/spec.pdf path/to/requirements.csvAPI 端点
方法 | 端点 | 描述 |
GET |
| 健康检查 |
GET |
| 按标签统计节点数量 |
POST |
| 上传并摄取文档 |
POST |
| 摄取结构化 API 记录 |
POST |
| 混合 GraphRAG 查询 |
POST |
| LangGraph 推理智能体 |
POST |
| Strands 工具调用智能体 |
GET |
| 完整缺陷可追溯链 |
GET |
| 产品可追溯性摘要 |
示例查询
# 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 集成(Claude Desktop)
将 config/claude_desktop_mcp.json 的内容复制到你的 Claude Desktop claude_desktop_config.json 中。
可用的 MCP 工具:
ask_manufacturing_ai— 混合 GraphRAG 问答semantic_search— 向量相似性搜索graph_trace— 图遍历检索natural_language_to_cypher— 自然语言转 Cypherget_defect_chain— 完整缺陷可追溯性get_requirement_traceability— 需求覆盖product_health_dashboard— 产品指标
知识图谱模式
(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)运行测试
set PYTHONPATH=src
pytest tests/ -vThis server cannot be installed
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