it-ops/llm-automation-docs-and-remediation-engine

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LLM Automation System 1ba5ce851d Initial commit: LLM Automation Docs & Remediation Engine v2.0

Features:
- Automated datacenter documentation generation
- MCP integration for device connectivity
- Auto-remediation engine with safety checks
- Multi-factor reliability scoring (0-100%)
- Human feedback learning loop
- Pattern recognition and continuous improvement
- Agentic chat support with AI
- API for ticket resolution
- Frontend React with Material-UI
- CI/CD pipelines (GitLab + Gitea)
- Docker & Kubernetes deployment
- Complete documentation and guides

v2.0 Highlights:
- Auto-remediation with write operations (disabled by default)
- Reliability calculator with 4-factor scoring
- Human feedback system for continuous learning
- Pattern-based progressive automation
- Approval workflow for critical actions
- Full audit trail and rollback capability

2025-10-17 23:47:28 +00:00

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API Documentation

La documentazione datacenter è accessibile tramite API REST ottimizzata per umani e LLM.

Base URL

https://docs.datacenter.local/api/v1

Authentication

Attualmente l'API è accessibile senza autenticazione nella rete interna. Per accesso esterno è richiesta autenticazione API key.

Endpoints

GET /sections

Lista tutte le sezioni disponibili con metadata.

Response:

[
  {
    "id": "01_infrastruttura_fisica",
    "title": "Infrastruttura Fisica",
    "section": "01",
    "last_updated": "2025-01-20T10:30:00Z",
    "size_bytes": 45000,
    "token_estimate": 11250,
    "url": "/docs/sections/01_infrastruttura_fisica/",
    "api_url": "/api/v1/sections/01_infrastruttura_fisica"
  }
]

GET /sections/{section_id}

Ottieni contenuto completo di una sezione.

Parameters:

format (query): markdown | html | json (default: markdown)

Example:

curl https://docs.datacenter.local/api/v1/sections/02_networking?format=markdown

GET /summary

Summary ottimizzato per LLM con key points di ogni sezione.

Response:

[
  {
    "section_id": "01_infrastruttura_fisica",
    "title": "Infrastruttura Fisica",
    "key_points": [
      "Informazioni Generali Datacenter",
      "Layout e Organizzazione",
      "Sistema Elettrico"
    ],
    "subsections": ["1.1", "1.2", "2.1", ...],
    "last_updated": "2025-01-20T10:30:00Z"
  }
]

GET /search

Ricerca full-text nella documentazione.

Parameters:

q (query, required): Search query
limit (query): Max results (default: 10, max: 50)

Example:

curl "https://docs.datacenter.local/api/v1/search?q=ups&limit=5"

GET /stats

Statistiche generali della documentazione.

GET /llm-optimized/{section_id}

Contenuto ottimizzato specificamente per consumo LLM.

Features:

Markdown pulito
Metadata espliciti
Istruzioni per LLM
Token count

Rate Limiting

API pubblica: 100 req/min
Con API key: 1000 req/min

Esempi d'uso

Python

import requests

# Get all sections
response = requests.get('https://docs.datacenter.local/api/v1/sections')
sections = response.json()

# Get specific section
response = requests.get(
    'https://docs.datacenter.local/api/v1/sections/02_networking',
    params={'format': 'markdown'}
)
content = response.json()

print(f"Section: {content['metadata']['title']}")
print(f"Tokens: {content['metadata']['token_estimate']}")
print(content['content'])

cURL

# Get summary
curl https://docs.datacenter.local/api/v1/summary | jq

# Search
curl "https://docs.datacenter.local/api/v1/search?q=vmware&limit=10" | jq

# Get stats
curl https://docs.datacenter.local/api/v1/stats | jq

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