llm-automation-docs-and-remediation-engine/TODO.md

TODO - Componenti da Sviluppare

Last Updated: 2025-10-20 Project Completion: ~72% (Infrastructure + CLI + Workers + 3 Collectors + 2 Generators complete, MVP VALIDATED ✅)

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✅ Completamenti Recenti

Infrastruttura (100% Complete)

• ✅ Python 3.12 Migration - Tutti i file aggiornati da 3.13/3.14 a 3.12
• ✅ Docker Development Environment - Tutti i Dockerfile creati e testati
  • deploy/docker/Dockerfile.api - Multi-stage build con Poetry
  • deploy/docker/Dockerfile.chat - WebSocket server (codice da implementare)
  • deploy/docker/Dockerfile.worker - Celery worker (codice da implementare)
  • deploy/docker/Dockerfile.frontend - React + Nginx
  • deploy/docker/docker-compose.dev.yml - Ambiente completo con 6 servizi
• ✅ CI/CD Pipelines - GitHub Actions, GitLab CI, Gitea Actions configurati per Python 3.12
• ✅ API Service - FastAPI server funzionante e testato
• ✅ Database Layer - MongoDB + Beanie ODM configurato e funzionante
• ✅ Redis - Cache e message broker operativi
• ✅ Auto-Remediation Engine - Implementato e testato
• ✅ MCP Client - Integrazione base con Model Context Protocol
• ✅ CLI Tool - Strumento CLI completo con 11 comandi (2025-10-19)
• ✅ Celery Workers - Sistema completo task asincroni con 8 task (2025-10-19)
• ✅ VMware Collector - Collector completo per vSphere con BaseCollector (2025-10-19)

Servizi Operativi

# Servizi attualmente funzionanti in Docker
✅ MongoDB (porta 27017) - Database principale
✅ Redis (porta 6379) - Cache e message broker
✅ API (porta 8000) - FastAPI con health check funzionante
✅ Worker - Celery worker con 4 code e 8 task
❌ Chat (porta 8001) - Dockerfile pronto, codice mancante (main.py)
❌ Frontend (porta 80) - Build funzionante, app minima

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🔴 Componenti Critici Mancanti

1. Chat Service (WebSocket Server)

Stato: ⚠️ Parziale - Solo agent.py presente File da creare: src/datacenter_docs/chat/main.py

Descrizione:

• Server WebSocket per chat in tempo reale
• Integrazione con DocumentationAgent esistente
• Gestione sessioni utente
• Conversational memory

Dipendenze:

• ✅ python-socketio (già in pyproject.toml)
• ✅ websockets (già in pyproject.toml)
• ✅ chat/agent.py (già presente)

Riferimenti:

• Script Poetry definito: docs-chat = "datacenter_docs.chat.main:start" (line 95 pyproject.toml)
• Dockerfile pronto: deploy/docker/Dockerfile.chat
• Porta configurata: 8001

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2. Celery Worker Service

Stato: ✅ COMPLETATO Directory: src/datacenter_docs/workers/

File implementati:

• ✅ src/datacenter_docs/workers/__init__.py - Module initialization
• ✅ src/datacenter_docs/workers/celery_app.py - Configurazione Celery completa
• ✅ src/datacenter_docs/workers/tasks.py - 8 task asincroni implementati

Tasks implementati:

1. ✅ generate_documentation_task - Generazione documentazione periodica (ogni 6 ore)
2. ✅ generate_section_task - Generazione sezione specifica
3. ✅ execute_auto_remediation_task - Esecuzione azioni correttive
4. ✅ process_ticket_task - Processamento ticket con AI
5. ✅ collect_infrastructure_data_task - Raccolta dati infrastruttura (ogni ora)
6. ✅ cleanup_old_data_task - Pulizia dati vecchi (giornaliero, 2 AM)
7. ✅ update_system_metrics_task - Aggiornamento metriche (ogni 15 min)
8. ✅ Task base class - DatabaseTask con inizializzazione DB automatica

Caratteristiche:

• ✅ 4 code separate: documentation, auto_remediation, data_collection, maintenance
• ✅ Rate limiting configurato (10 auto-remediation/ora, 5 generazioni/ora)
• ✅ Scheduling periodico con Celery Beat
• ✅ Task lifecycle signals (prerun, postrun, success, failure)
• ✅ Timeout configurati (1h hard, 50min soft)
• ✅ Integrazione completa con MongoDB/Beanie
• ✅ Logging completo e audit trail
• ⚠️ Task skeletons pronti (richiedono Collectors/Generators per funzionalità complete)

Periodic Schedule:

• Every 6 hours: Full documentation generation
• Every 1 hour: Infrastructure data collection
• Every 15 minutes: System metrics update
• Daily at 2 AM: Old data cleanup

Dipendenze:

• ✅ celery[redis] (già in pyproject.toml)
• ✅ flower per monitoring (già in pyproject.toml)
• ✅ Redis configurato in docker-compose

Riferimenti:

• Script Poetry definito: docs-worker = "datacenter_docs.workers.celery_app:start" (line 95 pyproject.toml)
• Dockerfile pronto: deploy/docker/Dockerfile.worker
• Completato il: 2025-10-19

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3. CLI Tool

Stato: ✅ COMPLETATO File: src/datacenter_docs/cli.py

Funzionalità implementate:

# Comandi implementati
datacenter-docs serve                     # ✅ Avvia API server (uvicorn)
datacenter-docs worker                    # ✅ Avvia Celery worker (skeleton)
datacenter-docs init-db                   # ✅ Inizializza database con collezioni e dati
datacenter-docs generate <section>        # ✅ Genera sezione specifica (skeleton)
datacenter-docs generate-all              # ✅ Genera tutta la documentazione (skeleton)
datacenter-docs list-sections             # ✅ Lista sezioni disponibili
datacenter-docs stats                     # ✅ Mostra statistiche sistema
datacenter-docs remediation enable        # ✅ Abilita auto-remediation
datacenter-docs remediation disable       # ✅ Disabilita auto-remediation
datacenter-docs remediation status        # ✅ Mostra stato policies
datacenter-docs version                   # ✅ Info versione

Caratteristiche:

• ✅ Interfaccia Typer con Rich formatting
• ✅ Comandi asincroni con MongoDB/Beanie
• ✅ Gestione completa auto-remediation policies
• ✅ Statistiche in tempo reale
• ✅ Gestione errori e help completo
• ⚠️ Generate commands sono skeleton (richiedono Collectors/Generators)

Dipendenze:

• ✅ typer (già in pyproject.toml)
• ✅ rich per output colorato (già in pyproject.toml)

Riferimenti:

• Script Poetry definito: datacenter-docs = "datacenter_docs.cli:app" (line 93 pyproject.toml)
• Completato il: 2025-10-19

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🟡 Componenti da Completare

4. Collectors (Data Collection)

Stato: ⚠️ Parziale - Base + 3 collectors implementati (60%) Directory: src/datacenter_docs/collectors/

File implementati:

• ✅ base.py - BaseCollector abstract class (COMPLETATO 2025-10-19)
• ✅ vmware_collector.py - VMware vSphere collector (COMPLETATO 2025-10-19)
• ✅ proxmox_collector.py - Proxmox VE collector (COMPLETATO 2025-10-20)
• ✅ kubernetes_collector.py - Kubernetes collector (COMPLETATO 2025-10-20)
• ✅ __init__.py - Module exports

VMware Collector Features:

• ✅ Connection via MCP client with fallback to mock data
• ✅ Collects VMs (power state, resources, tools status, IPs)
• ✅ Collects ESXi hosts (hardware, version, uptime, maintenance mode)
• ✅ Collects clusters (DRS, HA, vSAN, resources)
• ✅ Collects datastores (capacity, usage, accessibility)
• ✅ Collects networks (VLANs, port groups, distributed switches)
• ✅ Calculates comprehensive statistics (totals, usage percentages)
• ✅ Data validation with VMware-specific checks
• ✅ MongoDB storage via BaseCollector.store()
• ✅ Integrated with Celery task collect_infrastructure_data_task
• ✅ Full async/await workflow with connect/collect/validate/store/disconnect
• ✅ Comprehensive error handling and logging

Proxmox Collector Features (COMPLETATO 2025-10-20):

• ✅ Connection via MCP client with fallback to mock data
• ✅ Collects VMs/QEMU (power state, resources, uptime)
• ✅ Collects LXC containers (resources, status)
• ✅ Collects Proxmox nodes (hardware, CPU, memory, storage)
• ✅ Collects cluster info (quorum, version, nodes)
• ✅ Collects storage (local, NFS, Ceph)
• ✅ Collects networks (bridges, VLANs, interfaces)
• ✅ Calculates comprehensive statistics (VMs, containers, nodes, storage)
• ✅ Data validation with Proxmox-specific checks
• ✅ MongoDB storage via BaseCollector.store()
• ✅ Integrated with Celery tasks
• ✅ Full async/await workflow
• ✅ Tested and validated with mock data

Kubernetes Collector Features (COMPLETATO 2025-10-20):

• ✅ Connection via MCP client with fallback to mock data
• ✅ Collects Namespaces (5 default namespaces)
• ✅ Collects Nodes (master + workers, CPU, memory, version)
• ✅ Collects Pods (status, containers, restart count, namespace)
• ✅ Collects Deployments (replicas, ready state, strategy)
• ✅ Collects Services (LoadBalancer, NodePort, ClusterIP)
• ✅ Collects Ingresses (hosts, TLS, backend services)
• ✅ Collects ConfigMaps and Secrets (metadata only)
• ✅ Collects Persistent Volumes and PVCs
• ✅ Calculates comprehensive K8s statistics (56 CPU cores, 224 GB RAM, 6 pods, 9 containers)
• ✅ Data validation with Kubernetes-specific checks
• ✅ MongoDB storage via BaseCollector.store()
• ✅ Integrated with Celery tasks (section_id: 'kubernetes' or 'k8s')
• ✅ Full async/await workflow
• ✅ Tested and validated with mock data

Collectors da implementare (3 rimanenti):

• ❌ network_collector.py - Raccolta configurazioni network (via NAPALM/Netmiko)
• ❌ storage_collector.py - Raccolta info storage (SAN, NAS)
• ❌ database_collector.py - Raccolta metriche database

BaseCollector Interface:

class BaseCollector(ABC):
    @abstractmethod
    async def connect(self) -> bool
    @abstractmethod
    async def disconnect(self) -> None
    @abstractmethod
    async def collect(self) -> dict

    async def validate(self, data: dict) -> bool
    async def store(self, data: dict) -> bool
    async def run(self) -> dict  # Full collection workflow
    def get_summary(self) -> dict

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5. Generators (Documentation Generation)

Stato: ⚠️ Parziale - Base + 2 generators implementati (30%) Directory: src/datacenter_docs/generators/

File implementati:

• ✅ base.py - BaseGenerator abstract class (COMPLETATO 2025-10-19)
• ✅ infrastructure_generator.py - Panoramica infrastruttura VMware (COMPLETATO 2025-10-19)
• ✅ network_generator.py - Documentazione network (COMPLETATO 2025-10-19)
• ✅ __init__.py - Module exports

BaseGenerator Features:

• ✅ LLM-powered documentation generation via generic LLM client
• ✅ Markdown output with validation
• ✅ MongoDB storage (DocumentationSection model)
• ✅ File system storage (optional)
• ✅ Post-processing and formatting
• ✅ Full async/await workflow (generate/validate/save)
• ✅ Comprehensive error handling and logging

InfrastructureGenerator Features:

• ✅ Generates comprehensive VMware infrastructure documentation
• ✅ Covers VMs, hosts, clusters, datastores, networks
• ✅ Statistics and resource utilization
• ✅ Professional Markdown with tables and structure
• ✅ Integrated with VMware collector
• ✅ Connected to Celery tasks for automation

NetworkGenerator Features:

• ✅ Generates network topology documentation
• ✅ VLANs, subnets, distributed switches, port groups
• ✅ Security-focused documentation
• ✅ Connectivity matrix and diagrams
• ✅ Integrated with VMware collector (virtual networking)

Generators da implementare (8 rimanenti):

• ❌ virtualization_generator.py - Documentazione VMware/Proxmox dettagliata
• ❌ kubernetes_generator.py - Documentazione K8s clusters
• ❌ storage_generator.py - Documentazione storage SAN/NAS
• ❌ database_generator.py - Documentazione database
• ❌ monitoring_generator.py - Documentazione monitoring
• ❌ security_generator.py - Audit e compliance
• ❌ runbook_generator.py - Procedure operative
• ❌ troubleshooting_generator.py - Guide risoluzione problemi

BaseGenerator Interface:

class BaseGenerator(ABC):
    @abstractmethod
    async def generate(self, data: dict) -> str  # Markdown output

    async def generate_with_llm(self, system: str, user: str) -> str
    async def validate_content(self, content: str) -> bool
    async def save_to_file(self, content: str, output_dir: str) -> str
    async def save_to_database(self, content: str, metadata: dict) -> bool
    async def run(self, data: dict, save_to_db: bool, save_to_file: bool) -> dict
    def get_summary(self) -> dict

Integrazione completata:

• ✅ Celery task generate_section_task integrato con generators
• ✅ Celery task generate_documentation_task integrato con generators
• ✅ Full workflow: Collector → Generator → MongoDB → API
• ✅ CLI commands pronti per utilizzo (datacenter-docs generate vmware)

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6. Validators

Stato: ⚠️ Solo skeleton Directory: src/datacenter_docs/validators/

Validators da implementare:

• config_validator.py - Validazione configurazioni
• security_validator.py - Security checks
• compliance_validator.py - Compliance checks
• performance_validator.py - Performance checks

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🟢 Componenti Opzionali/Futuri

7. Frontend React App

Stato: ⚠️ Parziale - Solo skeleton Directory: frontend/src/

Componenti da sviluppare:

• Dashboard principale
• Viewer documentazione
• Chat interface
• Auto-remediation control panel
• Analytics e statistiche
• Settings e configurazione

File esistenti:

• App.jsx e App_Enhanced.jsx (probabilmente prototipi)
• Build configurato (Vite + Nginx)

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8. MCP Server

Stato: ❓ Esterno al progetto Note: Sembra essere un servizio separato per connettività ai device

Potrebbe richiedere:

• Documentazione integrazione
• Client SDK/library
• Examples

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📋 Priorità Sviluppo Consigliata

Fase 1 - Core Functionality (Alta Priorità)

1. ✅ API Service - COMPLETATO
2. ✅ CLI Tool - COMPLETATO (2025-10-19)
3. ✅ Celery Workers - COMPLETATO (2025-10-19)
4. 🔴 Base Collectors - Almeno 2-3 collector base (NEXT PRIORITY)
5. 🔴 Base Generators - Almeno 2-3 generator base

Fase 2 - Advanced Features (Media Priorità)

6. 🟡 Chat Service - Per supporto real-time
7. 🟡 Tutti i Collectors - Completare raccolta dati
8. 🟡 Tutti i Generators - Completare generazione docs
9. 🟡 Validators - Validazione e compliance

Fase 3 - User Interface (Bassa Priorità)

10. 🟢 Frontend React - UI web completa
11. 🟢 Dashboard Analytics - Statistiche e metriche
12. 🟢 Admin Panel - Gestione configurazione

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📊 Stato Attuale Progetto

✅ Funzionante (100%)

• ✅ API FastAPI - Server completo con tutti gli endpoint (main.py, models.py, main_enhanced.py)
• ✅ Auto-remediation Engine - Sistema completo (auto_remediation.py, reliability.py)
• ✅ MCP Client - Integrazione base funzionante (mcp/client.py)
• ✅ Database Layer - MongoDB con Beanie ODM completamente configurato (utils/database.py)
• ✅ Configuration Management - Sistema completo di gestione config (utils/config.py)
• ✅ Docker Infrastructure - Tutti i Dockerfile e docker-compose.dev.yml pronti e testati
• ✅ CI/CD Pipelines - GitHub Actions, GitLab CI, Gitea Actions funzionanti
• ✅ Python Environment - Python 3.12 standardizzato ovunque

⚠️ Parziale (5-40%)

• ⚠️ Chat Service (40%) - DocumentationAgent implementato (chat/agent.py), manca WebSocket server
• ⚠️ Frontend React (20%) - Skeleton base con Vite build, app minima funzionante
• ⚠️ Collectors (20%) - BaseCollector + VMware collector completati (2025-10-19)
• ⚠️ Generators (5%) - Solo directory e init.py, nessun generator implementato
• ⚠️ Validators (5%) - Solo directory e init.py, nessun validator implementato

❌ Mancante (0%)

• ❌ Collector Implementations - 5 collectors rimanenti (K8s, Network, Storage, Database, Monitoring)
• ❌ Generator Implementations - Nessuno dei 10 generators implementato
• ❌ Validator Implementations - Nessun validator implementato
• ❌ Chat WebSocket Server - File chat/main.py non esiste
• ❌ Logging System - utils/logging.py non esiste
• ❌ Helper Utilities - utils/helpers.py non esiste

🎯 Completamento per Categoria

Categoria	%	Stato	Blockers
Infrastructure	100%	✅ Complete	None
API Service	80%	✅ Complete	None
Database	70%	✅ Complete	None
Auto-Remediation	85%	✅ Complete	None (fully integrated with workers)
CLI Tool	100%	✅ Complete	None
Workers	100%	✅ Complete	None (fully integrated with collectors/generators)
Collectors	60%	🟡 Partial	Base + VMware + Proxmox + Kubernetes done, 3 more needed
Generators	30%	🟡 Partial	Base + Infrastructure + Network done, 8 more needed
MCP Integration	60%	🟡 Partial	External MCP server needed
Chat Service	40%	🟡 Partial	WebSocket server missing
Validators	5%	🟡 Medium	All implementations missing
Frontend	20%	🟢 Low	UI components missing

Overall: ~72% (Infrastructure + CLI + Workers + 3 Collectors + 2 Generators complete, MVP validated)

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🎯 Next Steps Immediati

🔥 CRITICAL PATH - MVP (3-4 giorni effort rimanenti)

Step 1: CLI Tool (1 giorno) - ✅ COMPLETATO

File: src/datacenter_docs/cli.py Status: ✅ COMPLETATO il 2025-10-19 Risultato: CLI completo con 11 comandi funzionanti

Implementato:

• ✅ serve: Avvia API server con uvicorn
• ✅ worker: Avvia Celery worker (con gestione errori)
• ✅ init-db: Inizializza database completo
• ✅ generate/generate-all: Skeleton per generazione
• ✅ list-sections: Lista sezioni da DB
• ✅ stats: Statistiche complete
• ✅ remediation enable/disable/status: Gestione policies
• ✅ version: Info sistema

Dipendenze: ✅ Tutte presenti (typer, rich) Priorità: ✅ COMPLETATO

---

Step 2: Celery Workers (1-2 giorni) - ✅ COMPLETATO

Directory: src/datacenter_docs/workers/ Status: ✅ COMPLETATO il 2025-10-19 Risultato: Sistema completo task asincroni con 8 task e scheduling

Implementato:

• ✅ __init__.py - Module initialization
• ✅ celery_app.py - Configurazione completa con 4 code e beat schedule
• ✅ tasks.py - 8 task asincroni completi:
  • generate_documentation_task (ogni 6h)
  • generate_section_task
  • execute_auto_remediation_task (rate limit 10/h)
  • process_ticket_task
  • collect_infrastructure_data_task (ogni 1h)
  • cleanup_old_data_task (giornaliero 2 AM)
  • update_system_metrics_task (ogni 15min)
  • DatabaseTask base class

Caratteristiche:

• 4 code: documentation, auto_remediation, data_collection, maintenance
• Rate limiting e timeout configurati
• Celery Beat per task periodici
• Integrazione completa MongoDB/Beanie
• Task lifecycle signals
• CLI command funzionante: datacenter-docs worker

Dipendenze: ✅ Tutte presenti (celery[redis], flower) Priorità: ✅ COMPLETATO

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Step 3: Primo Collector (1-2 giorni) - ✅ COMPLETATO

File: src/datacenter_docs/collectors/vmware_collector.py Status: ✅ COMPLETATO il 2025-10-19 Risultato: Collector VMware completo con MCP integration

Implementato:

• ✅ base.py - BaseCollector con full workflow (connect/collect/validate/store/disconnect)
• ✅ vmware_collector.py - Collector completo per vSphere:
  • collect_vms() - VMs con power state, risorse, tools, IPs
  • collect_hosts() - ESXi hosts con hardware, version, uptime
  • collect_clusters() - Clusters con DRS, HA, vSAN
  • collect_datastores() - Storage con capacità e utilizzo
  • collect_networks() - Networks con VLANs e distributed switches
  • Statistiche comprehensive (totali, percentuali utilizzo)
  • Validazione VMware-specific
• ✅ Integrazione con MCP client (con fallback a mock data)
• ✅ Integrazione con Celery task collect_infrastructure_data_task
• ✅ MongoDB storage automatico via BaseCollector.store()
• ✅ Async/await completo con error handling

Dipendenze: ✅ pyvmomi già presente Priorità: ✅ COMPLETATO

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Step 4: Primo Generator (1-2 giorni) - ✅ COMPLETATO

Files:

• src/datacenter_docs/generators/base.py - ✅ COMPLETATO
• src/datacenter_docs/generators/infrastructure_generator.py - ✅ COMPLETATO
• src/datacenter_docs/generators/network_generator.py - ✅ COMPLETATO (bonus)

Status: ✅ COMPLETATO il 2025-10-19 Risultato: 2 generators funzionanti + base class completa

Implementato:

• ✅ BaseGenerator con LLM integration (via generic LLM client)
• ✅ InfrastructureGenerator per documentazione VMware
• ✅ NetworkGenerator per documentazione networking
• ✅ MongoDB storage automatico
• ✅ File system storage opzionale
• ✅ Validation e post-processing
• ✅ Integrazione completa con Celery tasks
• ✅ Professional Markdown output con headers/footers

Caratteristiche:

• Generic LLM client (supporta OpenAI, Anthropic, LLMStudio, etc.)
• Comprehensive prompts con system/user separation
• Data summary formatting per migliore LLM understanding
• Full async/await workflow
• Error handling e logging completo

Dipendenze: ✅ Tutte presenti (openai SDK per generic client) Priorità: ✅ COMPLETATO

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Step 5: Testing End-to-End (1 giorno)

Scenario MVP:

# 1. Inizializza DB
datacenter-docs init-db

# 2. Avvia worker
datacenter-docs worker &

# 3. Genera documentazione VMware
datacenter-docs generate vmware

# 4. Verifica API
curl http://localhost:8000/api/v1/sections/vmware

# 5. Verifica MongoDB
# Controlla che i dati siano stati salvati

Risultato atteso: Documentazione VMware generata e disponibile via API

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📋 SECONDARY TASKS (Post-MVP)

Task 6: Chat WebSocket Server (1-2 giorni)

File: src/datacenter_docs/chat/main.py Status: ❌ Non esiste Priorità: 🟡 MEDIA

Implementazione:

import socketio
from datacenter_docs.chat.agent import DocumentationAgent

sio = socketio.AsyncServer(async_mode='asgi')
app = socketio.ASGIApp(sio)

@sio.event
async def message(sid, data):
    agent = DocumentationAgent()
    response = await agent.process_query(data['query'])
    await sio.emit('response', response, room=sid)

---

Task 7: Rimanenti Collectors (3-5 giorni)

• kubernetes_collector.py
• network_collector.py
• storage_collector.py
• database_collector.py
• monitoring_collector.py

Priorità: 🟡 MEDIA

---

Task 8: Rimanenti Generators (4-6 giorni)

• network_generator.py
• virtualization_generator.py
• kubernetes_generator.py
• storage_generator.py
• database_generator.py
• monitoring_generator.py
• security_generator.py
• runbook_generator.py
• troubleshooting_generator.py

Priorità: 🟡 MEDIA

---

Task 9: Frontend React (5-7 giorni)

• Dashboard principale
• Documentation viewer
• Chat interface
• Auto-remediation panel

Priorità: 🟢 BASSA

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📝 Note Tecniche

Architettura Target

User Request → API/CLI
    ↓
Celery Task (async)
    ↓
Collectors → Raccolta dati da infrastruttura (via MCP)
    ↓
Generators → Generazione documentazione con LLM (Claude)
    ↓
Storage → MongoDB
    ↓
API Response/Notification

Stack Tecnologico Definito

• Backend: Python 3.12, FastAPI, Celery
• Database: MongoDB (Beanie ODM), Redis
• LLM: OpenAI-compatible API (supports OpenAI, Anthropic, LLMStudio, Open-WebUI, Ollama, LocalAI)
  • Generic LLM client: src/datacenter_docs/utils/llm_client.py
  • Configured via: LLM_BASE_URL, LLM_API_KEY, LLM_MODEL
  • Default: OpenAI GPT-4 (can be changed to any compatible provider)
• Frontend: React 18, Vite, Material-UI
• Infrastructure: Docker, Docker Compose
• CI/CD: GitHub Actions, GitLab CI, Gitea Actions
• Monitoring: Prometheus, Flower (Celery)

Dipendenze Già Configurate

Tutte le dipendenze Python sono già in pyproject.toml e funzionanti. Nessun package aggiuntivo necessario per iniziare lo sviluppo.

🔌 LLM Provider Configuration

Il sistema utilizza l'API standard OpenAI per massima flessibilità. Puoi configurare qualsiasi provider LLM compatibile tramite variabili d'ambiente:

OpenAI (Default)

LLM_BASE_URL=https://api.openai.com/v1
LLM_API_KEY=sk-your-openai-key
LLM_MODEL=gpt-4-turbo-preview

Anthropic Claude (via OpenAI-compatible API)

LLM_BASE_URL=https://api.anthropic.com/v1
LLM_API_KEY=sk-ant-your-anthropic-key
LLM_MODEL=claude-sonnet-4-20250514

LLMStudio (Local)

LLM_BASE_URL=http://localhost:1234/v1
LLM_API_KEY=not-needed
LLM_MODEL=local-model-name

Open-WebUI (Local)

LLM_BASE_URL=http://localhost:8080/v1
LLM_API_KEY=your-open-webui-key
LLM_MODEL=llama3

Ollama (Local)

LLM_BASE_URL=http://localhost:11434/v1
LLM_API_KEY=not-needed
LLM_MODEL=llama3

File di configurazione: src/datacenter_docs/utils/config.py Client LLM generico: src/datacenter_docs/utils/llm_client.py Utilizzo: Tutti i componenti usano automaticamente il client configurato

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📅 Timeline Stimato

Milestone 1: MVP (5-6 giorni) - ✅ 100% COMPLETATO 🎉

Obiettivo: Sistema base funzionante end-to-end

• ✅ Infrastruttura Docker (COMPLETATO)
• ✅ API Service (COMPLETATO)
• ✅ CLI Tool (COMPLETATO 2025-10-19)
• ✅ Celery Workers (COMPLETATO 2025-10-19)
• ✅ 1 Collector (VMware) (COMPLETATO 2025-10-19)
• ✅ 2 Generators (Infrastructure + Network) (COMPLETATO 2025-10-19)
• ✅ End-to-End Testing (COMPLETATO 2025-10-20) ✅

Deliverable: ✅ Comando datacenter-docs generate vmware pronto (needs LLM API key) Status: MVP VALIDATED - All core components functional with mock data Test Results: See TESTING_RESULTS.md

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Milestone 2: Core Features (2-3 settimane)

Obiettivo: Tutti i collector e generator implementati

• Tutti i 6 collectors
• Tutti i 10 generators
• Base validators
• Chat WebSocket server
• Scheduling automatico (ogni 6 ore)

Deliverable: Documentazione completa di tutta l'infrastruttura

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Milestone 3: Production (3-4 settimane)

Obiettivo: Sistema production-ready

• Frontend React completo
• Testing completo
• Performance optimization
• Security hardening
• Monitoring e alerting

Deliverable: Deploy in produzione

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🚀 Quick Start per Developer

Setup Ambiente Sviluppo

# 1. Clone e setup
git clone <repo>
cd llm-automation-docs-and-remediation-engine

# 2. Install dependencies
poetry install

# 3. Avvia stack Docker
cd deploy/docker
docker-compose -f docker-compose.dev.yml up -d

# 4. Verifica servizi
docker-compose -f docker-compose.dev.yml ps
curl http://localhost:8000/health

# 5. Accedi al container API per sviluppo
docker exec -it datacenter-api bash

Development Workflow

# Durante sviluppo, modifica codice in src/
# I volumi Docker sono montati, quindi le modifiche sono immediate

# Restart servizi dopo modifiche
cd deploy/docker
docker-compose -f docker-compose.dev.yml restart api

# Visualizza logs
docker-compose -f docker-compose.dev.yml logs -f api

Cosa Implementare per Primo

1. src/datacenter_docs/cli.py - CLI tool base
2. src/datacenter_docs/workers/celery_app.py - Celery setup
3. src/datacenter_docs/collectors/base.py - Base collector class
4. src/datacenter_docs/collectors/vmware_collector.py - Primo collector
5. src/datacenter_docs/generators/base.py - Base generator class
6. src/datacenter_docs/generators/infrastructure_generator.py - Primo generator

Testing

# Unit tests
poetry run pytest

# Test specifico
poetry run pytest tests/test_collectors/test_vmware.py

# Coverage
poetry run pytest --cov=src/datacenter_docs --cov-report=html

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📊 Summary

Status	Count	%
✅ Completato	~9 componenti principali	55%
⚠️ Parziale	4 componenti	15%
❌ Da implementare	~20 componenti	30%

Focus immediato: End-to-End Testing e validazione workflow completo

Estimated Time to MVP: Poche ore rimanenti (solo testing con LLM API configurata) Estimated Time to Production: 2-3 settimane full-time per completare tutti i collectors/generators

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Last Updated: 2025-10-19 Next Review: Dopo completamento MVP (CLI + Workers + 1 Collector + 1 Generator)