Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@manufacturing-graphrag Which critical defects are blocking the EV BMS release?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
GraphRAG-powered agentic AI platform for manufacturing traceability using Neo4j, Amazon Bedrock, and MCP.
┌─────────────────────────────────────────────────────────────────────┐
│ 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: GraphRAG MCP
Neo4j 5.x (with APOC plugin)
Python 3.11+
AWS credentials with Bedrock access (Claude 3.5 Sonnet + Titan Embed v2)
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 credentialsdocker-compose up neo4j -dset PYTHONPATH=src
python scripts/seed_data.py# API server
python main.py api
# MCP server (for Claude Desktop)
python main.py mcp
# Both
python main.py allpython scripts/ingest.py path/to/spec.pdf path/to/requirements.csvMethod | Endpoint | Description |
GET |
| Health check |
GET |
| Node counts by label |
POST |
| Upload and ingest a document |
POST |
| Ingest a structured API record |
POST |
| Hybrid GraphRAG query |
POST |
| LangGraph reasoning agent |
POST |
| Strands tool-calling agent |
GET |
| Full defect traceability chain |
GET |
| Product traceability summary |
# 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-002Copy config/claude_desktop_mcp.json content into your Claude Desktop claude_desktop_config.json.
Available MCP tools:
ask_manufacturing_ai — hybrid GraphRAG Q&A
semantic_search — vector similarity search
graph_trace — graph traversal retrieval
natural_language_to_cypher — NL2Cypher
get_defect_chain — full defect traceability
get_requirement_traceability — requirement coverage
product_health_dashboard — product metrics
(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)set PYTHONPATH=src
pytest tests/ -vThis server cannot be deployed
Natural-language queries over a verified emissions knowledge graph, plus standards validation
Knowledge graph ingestion, entity search, ontology analysis, and CoSync scoring.
Shared semantic graph for AI reviews, classification and structured memory across AI assistants.
AI knowledge graph for architecture, portfolio, and digital strategy management.
