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anils123
by anils123

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 credentials

3. Iniciar Neo4j (Docker)

docker-compose up neo4j -d

4. Poblar el grafo de conocimiento

set PYTHONPATH=src
python scripts/seed_data.py

5. Iniciar la plataforma

# API server
python main.py api

# MCP server (for Claude Desktop)
python main.py mcp

# Both
python main.py all

6. Ingerir documentos

python scripts/ingest.py path/to/spec.pdf path/to/requirements.csv

Endpoints de API

Método

Endpoint

Descripción

GET

/health

Comprobación de salud

GET

/graph/stats

Conteos de nodos por etiqueta

POST

/ingest/file

Subir e ingerir un documento

POST

/ingest/record

Ingerir un registro de API estructurado

POST

/query

Consulta híbrida GraphRAG

POST

/agent/langgraph

Agente de razonamiento LangGraph

POST

/agent/strands

Agente de llamada a herramientas Strands

GET

/traceability/defect/{id}

Cadena completa de trazabilidad de defectos

GET

/traceability/product/{id}

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-002

Integración 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 GraphRAG

  • semantic_search — búsqueda de similitud vectorial

  • graph_trace — recuperación por recorrido de grafo

  • natural_language_to_cypher — NL2Cypher

  • get_defect_chain — trazabilidad completa de defectos

  • get_requirement_traceability — cobertura de requisitos

  • product_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/ -v
F
license - not found
Not graded
quality - not tested
C
maintenance

Maintenance

0Releases (12mo)
Commit activity

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  • F
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