manufacturing-graphrag
Plataforma de Inteligencia de Trazabilidad de Fabricación
Plataforma de IA agéntica impulsada por GraphRAG para la trazabilidad de fabricación utilizando Neo4j, Amazon Bedrock y MCP.
Arquitectura
┌─────────────────────────────────────────────────────────────────────┐
│ 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
Inicio Rápido
1. Requisitos previos
Neo4j 5.x (con el plugin APOC)
Python 3.11+
Credenciales de AWS con acceso a Bedrock (Claude 3.5 Sonnet + Titan Embed v2)
2. Configuración
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. Iniciar Neo4j (Docker)
docker-compose up neo4j -d4. Poblar el grafo de conocimiento
set PYTHONPATH=src
python scripts/seed_data.py5. Iniciar la plataforma
# API server
python main.py api
# MCP server (for Claude Desktop)
python main.py mcp
# Both
python main.py all6. Ingerir documentos
python scripts/ingest.py path/to/spec.pdf path/to/requirements.csvEndpoints de API
Método | Endpoint | Descripción |
GET |
| Comprobación de salud |
GET |
| Conteos de nodos por etiqueta |
POST |
| Subir e ingerir un documento |
POST |
| Ingerir un registro de API estructurado |
POST |
| Consulta híbrida GraphRAG |
POST |
| Agente de razonamiento LangGraph |
POST |
| Agente de llamada a herramientas Strands |
GET |
| Cadena completa de trazabilidad de defectos |
GET |
| Resumen de trazabilidad de producto |
Consultas de ejemplo
# 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-002Integración MCP (Claude Desktop)
Copia el contenido de config/claude_desktop_mcp.json en tu claude_desktop_config.json de Claude Desktop.
Herramientas MCP disponibles:
ask_manufacturing_ai— Q&A híbrido de GraphRAGsemantic_search— búsqueda de similitud vectorialgraph_trace— recuperación por recorrido de grafonatural_language_to_cypher— NL2Cypherget_defect_chain— trazabilidad completa de defectosget_requirement_traceability— cobertura de requisitosproduct_health_dashboard— métricas de producto
Esquema del grafo de conocimiento
(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)Ejecución de pruebas
set PYTHONPATH=src
pytest tests/ -vThis server cannot be installed
Maintenance
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Self-hosted AI-native knowledge workspace with hybrid search, GraphRAG, and MCP.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables storage and retrieval of knowledge in a graph database format, allowing users to create, update, search, and delete entities and relationships in a Neo4j-powered knowledge graph through natural language.5
- AlicenseAqualityDmaintenanceEnables creating, managing, analyzing, and visualizing knowledge graphs with support for multiple graph types (topology, timelines, changelogs, requirements, knowledge bases, ontologies) including node/edge management and resource association.15191MIT
- FlicenseNot gradedqualityDmaintenanceEnables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.2
- FlicenseCqualityDmaintenanceCombines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.13
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