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** ✅)

---

## ✅ 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
```bash
# 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
```

---

## 🔴 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

---

### 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

---

### 3. CLI Tool
**Stato**: ✅ **COMPLETATO**
**File**: `src/datacenter_docs/cli.py`

**Funzionalità implementate**:
```bash
# 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

---

## 🟡 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**:
```python
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
```

---

### 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**:
```python
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`)

---

### 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

---

## 🟢 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)

---

### 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

---

## 📋 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

---

## 📊 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)

---

## 🎯 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

---

#### 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

---

#### 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

---

#### Step 5: Testing End-to-End (1 giorno)
**Scenario MVP**:
```bash
# 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

---

### 📋 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**:
```python
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

---

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

#### LLMStudio (Local)
```bash
LLM_BASE_URL=http://localhost:1234/v1
LLM_API_KEY=not-needed
LLM_MODEL=local-model-name
```

#### Open-WebUI (Local)
```bash
LLM_BASE_URL=http://localhost:8080/v1
LLM_API_KEY=your-open-webui-key
LLM_MODEL=llama3
```

#### Ollama (Local)
```bash
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

---

## 📅 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](TESTING_RESULTS.md)

---

### 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

---

### 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

---

## 🚀 Quick Start per Developer

### Setup Ambiente Sviluppo
```bash
# 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
```bash
# 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
```bash
# 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
```

---

## 📊 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

---

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