Update docs/gitops-cicd/11-ollama-comprehensive-enterprise-guide.md

docs/gitops-cicd/11-ollama-comprehensive-enterprise-guide.md

@@ -182,7 +182,7 @@ Self-hosted AI-инфраструктура на базе Ollama с интегр
 
 ### Уровень 1: User Access Layer
 
-**Веб-интерфейс** на базе Gradio предоставляет удобный браузерный доступ без установки дополнительного ПО. Это основной способ взаимодействия для большинства пользователей.
+**Веб-интерфейс** на базе Open WebUI предоставляет удобный браузерный доступ без установки дополнительного ПО. Это основной способ взаимодействия для большинства пользователей.
 
 **VS Code Extension** интегрирует AI-ассистента непосредственно в процесс разработки. Разработчик может задавать вопросы о коде, генерировать тесты, получать объяснения, не покидая IDE.
 
@@ -237,7 +237,7 @@ Embedding Service использует модель bge-large-en-v1.5 для с
 | **Network** | 2x 10 Gbps (bonded) | High throughput для MCP data retrieval |
 | **PSU** | 1600W 80+ Titanium | GPU power requirements |
 
-**Ориентировочная стоимость:** $12,000-15,000
+
 
 ### Выбор GPU по сценарию использования
 
@@ -261,29 +261,7 @@ Embedding Service использует модель bge-large-en-v1.5 для с
 
 *с частичным offloading в RAM
 
-### Распределение системной памяти (128 GB)
 
-```
-16 GB  → Операционная система Ubuntu Server
-8 GB   → Ollama service
-32 GB  → Vector Database Qdrant
-16 GB  → MCP Services
-8 GB   → Embedding service
-8 GB   → API Gateway + мониторинг
-40 GB  → Model offloading buffer
-```
-
-### Распределение хранилища (2 TB NVMe)
-
-```
-300 GB → AI Models
-500 GB → Vector Database
-200 GB → MCP Services cache
-100 GB → OS и приложения
-900 GB → Резерв для роста
-```
-
----
 
 ## Выбор и оптимизация AI-моделей
 
@@ -604,33 +582,6 @@ Effective AI-ассистент строит каждое взаимодейст
 
 **Relevance-based selection** - вместо отбора по времени, анализируется relevance каждого сообщения к текущему запросу через embedding similarity.
 
-### Persistent storage
-
-PostgreSQL хранит conversation data:
-- **sessions** table: ID, user_id, created_at, updated_at, title, status
-- **messages** table: session_id, role, content, created_at, model_used, token_count
-- JSONB columns для semi-structured metadata
-
-**Indexes:**
-- (user_id, updated_at) для listing недавних сессий
-- (session_id, created_at) для получения истории
-
-**Partitioning:** Monthly partitions поддерживают performance при росте данных.
-
-### Конфиденциальность и retention
-
-**Encryption:**
-- At rest: Database или filesystem-level encryption
-- In transit: TLS для всех коммуникаций
-
-**Access controls:**
-- Пользователи видят только свои диалоги
-- RBAC для managers с audit trail
-
-**Retention policies:**
-- Automated cleanup согласно policy
-- User right to deletion
-- Anonymization для analytics
 
 ### Search и navigation
 
@@ -652,678 +603,7 @@ PostgreSQL хранит conversation data:
 
 **Sharing links** - read-only URL с expiration time и access controls.
 
-### Analytics
 
-**Usage metrics:**
-- Активные пользователи per day
-- Количество сессий
-- Среднее messages per session
-- Peak usage times
-
-**Query patterns:**
-- Common question types
-- Frequently discussed topics
-- Typical workflows
-
-**User satisfaction:**
-- Explicit ratings
-- Implicit signals (conversation length, corrections)
-
-### Таблица session management
-
-| Параметр | Значение | Обоснование |
-|----------|----------|-------------|
-| Max messages в window | 40 | Баланс context/performance |
-| Trigger для summarization | 30 messages | До исчерпания window |
-| Compression ratio | 5:1 | 5 messages → 1 summary |
-| Max session idle time | 24 часа | Auto-close неактивных |
-| Max concurrent sessions | 10/user | Предотвращение abuse |
-
-### Таблица retention policy
-
-| Тип данных | Retention | Действие | Access |
-|------------|-----------|----------|--------|
-| Active sessions | Indefinite | N/A | User only |
-| Inactive (<30d) | Indefinite | N/A | User only |
-| Old (30-90d) | Summarized | Messages→summary | User only |
-| Very old (>90d) | Archived | Cold storage | Read-only |
-| Marked deletion | 30d grace | Permanent delete | User during grace |
-
----
-
-## Стратегия хранения данных
-
-### Многоуровневая архитектура
-
-Эффективная AI-инфраструктура требует sophisticated подхода к хранению различных типов данных с различными характеристиками и требованиями.
-
-### Hot Storage: NVMe SSD RAID
-
-**Primary tier** обеспечивает высокую производительность для frequently accessed данных.
-
-**Содержимое:**
-- AI models (300 GB) - fast loading критичен для UX
-- Vector DB indices (200 GB) - intensive I/O для каждого query
-- Recent conversations (100 GB) - frequent access
-
-**Характеристики:**
-- NVMe интерфейс: несколько GB/sec throughput
-- Latency: <100 microseconds
-- RAID 1: fault tolerance без downtime
-
-### Warm Storage: SATA SSD
-
-**Secondary tier** предоставляет больший объем за меньшую цену.
-
-**Содержимое:**
-- Vector DB payload (300 GB)
-- Source documents (200 GB)
-- Older conversations (200 GB)
-- Daily backups (1 TB)
-
-**Характеристики:**
-- SATA интерфейс: достаточная скорость
-- Cost-effective для large volumes
-- Acceptable latency для less frequent access
-
-### Cold Storage: Object Storage
-
-**Tertiary tier** для archival data и compliance.
-
-**Содержимое:**
-- Very old sessions (500 GB)
-- Weekly backups (500 GB)
-- Long-term analytics (variable)
-
-**Характеристики:**
-- S3-compatible storage
-- Dramatically lower cost
-- Retrieval latency в секундах
-
-### Lifecycle Management
-
-**Automated policies:**
-- Hot→Warm после месяца inactivity
-- Warm→Cold после трех месяцев
-- Deletion согласно retention policy
-- Compression older data
-
-### Backup Strategy
-
-**Continuous WAL archiving** в PostgreSQL для point-in-time recovery.
-
-**Daily full backups:**
-- Qdrant snapshots
-- PostgreSQL dumps
-- На warm и cold tiers
-
-**Weekly full backups:**
-- AI models (rarely change)
-- Configuration
-- На cold tier
-
-**Testing:** Automated restoration tests в test environment.
-
-### Таблица Storage Tier Allocation
-
-| Данные | Volume | Tier | Access pattern | Latency | Retention |
-|--------|--------|------|----------------|---------|-----------|
-| AI models | 300 GB | Hot | На load | <1s | Indefinite |
-| Vector indices | 200 GB | Hot | На query | <100ms | Indefinite |
-| Vector payload | 300 GB | Warm | На retrieval | <500ms | Indefinite |
-| Recent sessions | 100 GB | Hot | Very frequent | <50ms | Indefinite |
-| Old sessions | 200 GB | Warm | Occasional | <1s | До deletion |
-| Archived | 500 GB | Cold | Rare | <10s | До deletion |
-| Source docs | 200 GB | Warm | На reindex | <2s | Indefinite |
-
-### Таблица Backup Strategy
-
-| Тип | Frequency | Retention | Location | RTO | RPO |
-|-----|-----------|-----------|----------|-----|-----|
-| PostgreSQL WAL | Continuous | 7d | Object | 1h | 5min |
-| PostgreSQL full | Daily | 30d | Warm+Cold | 2h | 24h |
-| Qdrant snapshot | Daily | 30d | Warm | 3h | 24h |
-| Qdrant snapshot | Weekly | 90d | Cold | 6h | 7d |
-| AI models | Weekly | Indefinite | Cold | 1h | 7d |
-| Configuration | On change | Indefinite | Git | 30min | Last commit |
-
----
-
-## Безопасность и Compliance
-
-### Network Isolation
-
-**Firewall rules** implement least privilege:
-
-**Inbound:**
-- 443 (HTTPS) из Corporate VPN
-- 11434 (Ollama) только с MCP Orchestrator
-- 6333 (Qdrant) только с Ollama server
-
-**Outbound:**
-- 3000 (Gitea API)
-- 2377 (Docker Swarm API)
-- 6443 (Kubernetes API)
-- 3100 (Loki API)
-- Default: DENY ALL
-
-**IDS/IPS** мониторит traffic для suspicious patterns, используя ML-based anomaly detection.
-
-### Authentication и Authorization
-
-**LDAP integration** для enterprises:
-- Аутентификация с corporate credentials
-- Group membership определяет access levels
-- Centralized password management
-
-**OIDC** для modern cloud-native auth:
-- Integration с Okta, Auth0, Azure AD
-- SSO capabilities
-- MFA support
-
-**RBAC (Role-Based Access Control):**
-- **devops role**: query:*, mcp:*:read
-- **developer role**: query:code, mcp:gitea:read
-- **viewer role**: query:docs
-
-### Secrets Masking
-
-**Automated patterns:**
-```
-password:\s*"?([^"\s]+)"?     → password: "[REDACTED]"
-token:\s*"?([^"\s]+)"?        → token: "[REDACTED]"
-\b\d{16}\b                    → [CARD_REDACTED]
-\b\d{3}-\d{2}-\d{4}\b         → [SSN_REDACTED]
-```
-
-**Application в:**
-- MCP server responses
-- Логах системы
-- Conversation histories
-- Export files
-
-### Audit Logging
-
-**Все операции логируются:**
-```
-Timestamp | User | Action | Details | Result
-2026-01-12 14:23:45 | user@company.com | query | model=qwen2.5-coder | success
-2026-01-12 14:23:46 | user@company.com | mcp_k8s | get_pods | success
-```
-
-**Retention:** 1 год для compliance.
-
-**Analysis:** Регулярный review для suspicious patterns.
-
-### Data Protection
-
-**Encryption at rest:**
-- Database encryption (PostgreSQL TDE)
-- Filesystem encryption (LUKS)
-- Vector DB encryption
-
-**Encryption in transit:**
-- TLS 1.3 для всех connections
-- Certificate management через Let's Encrypt или internal CA
-
-**DLP (Data Loss Prevention):**
-- Content inspection на egress
-- Block передачи sensitive patterns
-- Alert на suspicious exports
-
-### Compliance
-
-**PCI DSS:** Данные не покидают secured network.
-
-**GDPR:** 
-- Right to deletion implemented
-- Data minimization principles
-- Consent management
-- Data portability через exports
-
-**SOC 2:**
-- Comprehensive audit trails
-- Access controls documented
-- Regular security reviews
-- Incident response procedures
-
-### Security Monitoring
-
-**Metrics tracked:**
-- Failed authentication attempts
-- Unusual access patterns
-- MCP server errors
-- Rate limit hits
-- Secrets exposure attempts
-
-**Alerting:**
-- Slack integration для security team
-- PagerDuty для critical alerts
-- Email для regular notifications
-
-### Таблица Security Controls
-
-| Контроль | Тип | Уровень | Мониторинг |
-|----------|-----|---------|------------|
-| Network firewall | Preventive | Infrastructure | 24/7 |
-| TLS encryption | Preventive | Transport | Certificate monitoring |
-| LDAP auth | Detective | Application | Login success rate |
-| RBAC | Preventive | Application | Access patterns |
-| Secrets masking | Preventive | Application | Exposure attempts |
-| Audit logging | Detective | All layers | Log analysis |
-| IDS/IPS | Detective/Preventive | Network | Alert monitoring |
-| Backup encryption | Preventive | Storage | Backup verification |
-
----
-
-## Мониторинг и Observability
-
-### Key Metrics
-
-**GPU Metrics:**
-- nvidia_gpu_temperature_celsius
-- nvidia_gpu_utilization_percent
-- nvidia_gpu_memory_used_bytes
-- nvidia_gpu_power_usage_watts
-
-**Ollama Metrics:**
-- ollama_requests_total
-- ollama_request_duration_seconds
-- ollama_tokens_per_second
-- ollama_active_models
-
-**MCP Metrics:**
-- mcp_requests_total{service="gitea"}
-- mcp_request_duration_seconds
-- mcp_errors_total
-- mcp_cache_hit_ratio
-
-**RAG Metrics:**
-- qdrant_collection_size
-- qdrant_query_duration_seconds
-- embedding_generation_duration
-- reranking_duration
-
-**Storage Metrics:**
-- disk_usage_percent{tier="hot"}
-- disk_iops{tier="hot"}
-- disk_throughput_bytes
-- backup_last_success_timestamp
-
-### Grafana Dashboards
-
-**Dashboard 1: Ollama Overview**
-- GPU utilization timeline
-- Request rate по моделям
-- Response time percentiles (p50, p95, p99)
-- Active users count
-- Token generation rate
-
-**Dashboard 2: MCP Services**
-- Request distribution pie chart
-- Success/error rates по сервисам
-- Latency heatmap
-- Cache hit rates
-- Top users by requests
-
-**Dashboard 3: Vector DB**
-- Collection sizes growth
-- Query performance trends
-- Cache effectiveness
-- Index rebuild status
-
-**Dashboard 4: User Experience**
-- Average response time
-- User satisfaction ratings
-- Session duration distribution
-- Popular query types
-- Error rate по типам
-
-**Dashboard 5: Infrastructure Health**
-- CPU/RAM utilization
-- Disk I/O patterns
-- Network throughput
-- Temperature monitoring
-- Power consumption
-
-### Alerting Strategy
-
-**Critical Alerts (PagerDuty):**
-- Ollama service down
-- GPU temperature >85°C
-- Disk usage >90%
-- Authentication system unavailable
-- Backup failed
-
-**Warning Alerts (Slack):**
-- High error rate (>5%)
-- Slow response times (p95 >10s)
-- GPU utilization consistently >95%
-- MCP service degraded
-- Cache miss rate >50%
-
-**Info Alerts (Email):**
-- Scheduled maintenance reminders
-- Usage statistics weekly digest
-- Capacity planning recommendations
-
-### Logging Strategy
-
-**Structured logging** JSON format для всех компонентов:
-```json
-{
-  "timestamp": "2026-01-12T14:23:45Z",
-  "level": "INFO",
-  "service": "ollama",
-  "message": "Model loaded",
-  "model": "qwen2.5-coder:32b",
-  "load_time_ms": 2341
-}
-```
-
-**Log aggregation** через Loki:
-- Central collection
-- Retention: 30 days hot, 90 days warm
-- Full-text search capability
-- Correlation with metrics
-
-**Log levels:**
-- ERROR: Failures requiring attention
-- WARN: Degraded performance
-- INFO: Normal operations
-- DEBUG: Detailed troubleshooting (disabled in production)
-
-### Distributed Tracing
-
-OpenTelemetry для end-to-end request tracing:
-- User request → API Gateway
-- Gateway → Ollama
-- Ollama → MCP services
-- MCP → Backend systems
-- RAG → Vector DB
-
-Jaeger UI для visualizing traces, identifying bottlenecks.
-
-### Health Checks
-
-**Liveness probes:**
-- Ollama /health endpoint
-- Qdrant readiness
-- PostgreSQL connectivity
-- MCP services status
-
-**Readiness probes:**
-- Models loaded
-- Indices ready
-- Database connections available
-
-**Периодичность:** Every 30 seconds.
-
-### Capacity Planning
-
-**Trend analysis:**
-- Usage growth rate
-- Storage consumption trends
-- Peak load patterns
-- Resource saturation points
-
-**Forecasting:**
-- When additional GPU needed
-- Storage expansion timeline
-- Network bandwidth requirements
-- Team growth accommodation
-
-### Таблица мониторинга
-
-| Компонент | Метрика | Threshold Warning | Threshold Critical | Action |
-|-----------|---------|-------------------|-------------------|--------|
-| GPU | Temperature | >75°C | >85°C | Check cooling |
-| GPU | Utilization | >85% | >95% | Consider scaling |
-| GPU | Memory | >20GB | >23GB | Model optimization |
-| Storage | Disk usage | >75% | >90% | Cleanup/expansion |
-| Storage | IOPS | >80% max | >95% max | Storage upgrade |
-| API | Error rate | >2% | >5% | Investigate logs |
-| API | Latency p95 | >5s | >10s | Performance tuning |
-| RAG | Query time | >1s | >2s | Index optimization |
-
----
-
-## Экономическое обоснование
-
-### Капитальные затраты (CapEx)
-
-| Компонент | Стоимость |
-|-----------|-----------|
-| GPU (RTX 4090 24GB) | $1,600-2,000 |
-| CPU (Ryzen 9 7950X) | $500-600 |
-| RAM (128GB DDR5 ECC) | $600-800 |
-| Storage (NVMe + SATA) | $800-1,000 |
-| Motherboard (High-end) | $400-500 |
-| PSU (1600W Titanium) | $300-400 |
-| Case/Cooling | $300-400 |
-| Network (2x 10GbE) | $200-300 |
-| **TOTAL CapEx** | **$12,000-15,000** |
-
-### Операционные затраты (OpEx) годовые
-
-| Статья | Стоимость |
-|--------|-----------|
-| Электричество (~500W 24/7) | $650/год |
-| Охлаждение | $200/год |
-| Maintenance | $500/год |
-| Training/Documentation | $2,000/год |
-| **TOTAL OpEx** | **$3,350/год** |
-
-### Софт (бесплатно)
-
-Все программные компоненты open source:
-- Ubuntu Server: FREE
-- Ollama: FREE
-- Qdrant: FREE
-- PostgreSQL: FREE
-- Все MCP services: FREE (self-developed)
-- Prometheus/Grafana: FREE
-
-### ROI Analysis
-
-**Экономия времени команды 10 инженеров:**
-
-| Активность | Сэкономлено | Часов/год | Ценность ($100/час) |
-|------------|-------------|-----------|---------------------|
-| Поиск информации | 40% | 832 часов | $83,200 |
-| Написание документации | 50% | 520 часов | $52,000 |
-| Troubleshooting | 30% | 624 часов | $62,400 |
-| Code review | 20% | 208 часов | $20,800 |
-| **TOTAL** | | **2,184 часов** | **$218,400/год** |
-
-**ROI расчет:**
-```
-Total Investment: $15,000 (CapEx) + $3,350 (OpEx год 1) = $18,350
-Annual Benefit: $218,400
-Payback Period: 18,350 / 218,400 = 0.08 года = 1 месяц
-3-Year ROI: (3 × $218,400 - $18,350 - 2 × $3,350) / $18,350 = 3,458%
-```
-
-### Сравнение с облачными AI API
-
-**OpenAI GPT-4 pricing:**
-- Prompt: $0.03 per 1K tokens
-- Completion: $0.06 per 1K tokens
-
-**Типичный query:**
-- 2K tokens prompt (context + question)
-- 1K tokens completion
-- Cost per query: $0.12
-
-**Monthly cost для 10 пользователей:**
-- 50 queries/day per user = 500 queries/day
-- 500 × 30 days = 15,000 queries/month
-- 15,000 × $0.12 = $1,800/month = $21,600/year
-
-**Self-hosted advantages:**
-- Lower cost after year 1
-- Complete data control
-- No API rate limits
-- Customizable models
-- No vendor lock-in
-
-### Таблица TCO (Total Cost of Ownership) 3 года
-
-| Год | CapEx | OpEx | Total Annual | Cumulative | Cloud Alternative |
-|-----|-------|------|--------------|------------|-------------------|
-| 1 | $15,000 | $3,350 | $18,350 | $18,350 | $21,600 |
-| 2 | $0 | $3,350 | $3,350 | $21,700 | $43,200 |
-| 3 | $0 | $3,350 | $3,350 | $25,050 | $64,800 |
-| **Savings** | | | | | **$39,750** |
-
----
-
-## Deployment Roadmap
-
-### Phase 1: Foundation (Weeks 1-2)
-
-**Infrastructure setup:**
-- Server assembly и OS installation
-- Network configuration
-- GPU drivers installation
-- Docker setup
-
-**Deliverables:**
-- Working server с GPU functional
-- Network connectivity verified
-- Monitoring baseline established
-
-### Phase 2: Core Services (Weeks 3-4)
-
-**AI infrastructure:**
-- Ollama installation
-- Models download и testing
-- Basic API Gateway setup
-
-**Deliverables:**
-- Models responding to queries
-- Simple web interface functional
-- Performance benchmarks completed
-
-### Phase 3: MCP Integration (Weeks 5-6)
-
-**MCP services deployment:**
-- Gitea MCP server
-- Docker Swarm MCP server
-- Kubernetes MCP server (if applicable)
-
-**Deliverables:**
-- Models accessing corporate systems
-- Read-only access verified
-- Security controls tested
-
-### Phase 4: RAG Implementation (Weeks 7-8)
-
-**Knowledge base setup:**
-- Qdrant deployment
-- Embedding service
-- Initial document indexing
-
-**Deliverables:**
-- Vector DB operational
-- Initial corpus indexed
-- Search quality validated
-
-### Phase 5: Production Readiness (Weeks 9-10)
-
-**Finalization:**
-- Authentication integration
-- Monitoring dashboards
-- Backup automation
-- Documentation
-
-**Deliverables:**
-- Production-ready system
-- Team training completed
-- Operational runbooks
-- Go-live approval
-
-### Phase 6: Rollout (Week 11-12)
-
-**Gradual adoption:**
-- Pilot group (2-3 users)
-- Feedback collection
-- Issue resolution
-- Full team rollout
-
----
-
-## Operational Excellence
-
-### Daily Operations
-
-**Health checks:**
-- Morning review dashboards
-- Check overnight alerts
-- Verify backup success
-- Monitor disk usage
-
-**User support:**
-- Answer questions in Slack
-- Collect feedback
-- Document common issues
-
-### Weekly Tasks
-
-**Performance review:**
-- Analyze usage trends
-- Review slow queries
-- Check error patterns
-- Optimize as needed
-
-**Content updates:**
-- Reindex modified documents
-- Update code snippets
-- Refresh runbooks
-
-**Capacity planning:**
-- Review storage trends
-- Analyze GPU utilization
-- Forecast growth
-
-### Monthly Tasks
-
-**Security review:**
-- Audit logs analysis
-- Access patterns review
-- Update firewall rules
-- Vulnerability scanning
-
-**System maintenance:**
-- OS updates
-- Driver updates
-- Dependency updates
-- Performance tuning
-
-**Reporting:**
-- Usage statistics
-- ROI tracking
-- User satisfaction
-- Improvement recommendations
-
-### Quarterly Tasks
-
-**Major upgrades:**
-- Model updates
-- Infrastructure upgrades
-- Feature additions
-
-**Strategy review:**
-- Roadmap adjustment
-- Budget review
-- Team expansion planning
-
-**Training:**
-- Advanced features training
-- New team members onboarding
-- Best practices sharing
-
----
 
 ## Best Practices
 
@@ -1366,103 +646,6 @@ Payback Period: 18,350 / 218,400 = 0.08 года = 1 месяц
 4. **Test backups** regularly
 5. **Plan for growth** from day one
 
----
-
-## Troubleshooting Guide
-
-### GPU Issues
-
-**Symptom:** Model loading fails
-**Causes:** 
-- Insufficient VRAM
-- Driver issues
-- Cooling problems
-
-**Resolution:**
-1. Check nvidia-smi output
-2. Verify model size vs VRAM
-3. Update drivers if needed
-4. Check temperatures
-
-**Symptom:** Slow inference
-**Causes:**
-- GPU throttling due to heat
-- CPU bottleneck
-- Insufficient RAM
-
-**Resolution:**
-1. Monitor GPU temperature
-2. Check cooling system
-3. Verify CPU usage
-4. Check RAM availability
-
-### MCP Service Issues
-
-**Symptom:** MCP timeouts
-**Causes:**
-- Backend system slow/down
-- Network issues
-- Rate limiting
-
-**Resolution:**
-1. Check backend system health
-2. Verify network connectivity
-3. Review rate limit settings
-4. Check MCP logs
-
-**Symptom:** Incorrect data returned
-**Causes:**
-- Cache staleness
-- Backend API changes
-- Parsing errors
-
-**Resolution:**
-1. Clear MCP cache
-2. Verify backend API format
-3. Check MCP server logs
-4. Update parsers if needed
-
-### RAG Issues
-
-**Symptom:** Poor search quality
-**Causes:**
-- Outdated index
-- Poor chunk strategy
-- Embedding model issues
-
-**Resolution:**
-1. Trigger reindexing
-2. Review chunk configuration
-3. Test embedding service
-4. Analyze user feedback
-
-**Symptom:** Slow searches
-**Causes:**
-- Index size too large
-- Insufficient resources
-- Network latency
-
-**Resolution:**
-1. Optimize index parameters
-2. Add more RAM/storage
-3. Check Qdrant configuration
-4. Review network latency
-
-### Storage Issues
-
-**Symptom:** Disk full
-**Causes:**
-- Uncontrolled growth
-- Failed cleanup jobs
-- Backup accumulation
-
-**Resolution:**
-1. Run cleanup scripts
-2. Archive old data
-3. Verify retention policies
-4. Plan capacity expansion
-
----
 
 ## Заключение
 
@@ -1480,29 +663,4 @@ Self-hosted AI-инфраструктура на базе Ollama с интегр
 
 **История для контекста**. Persistent storage и intelligent management истории диалогов критичны для user experience и continuous improvement системы.
 
-### Путь вперед
 
-Развертывание такой инфраструктуры - не одноразовый проект, а начало journey continuous improvement. Система будет evolve вместе с:
-- Появлением новых, более мощных моделей
-- Расширением интеграций с корпоративными системами
-- Ростом knowledge base
-- Увеличением команды пользователей
-- Развитием best practices
-
-### Следующие шаги
-
-1. **Оценка готовности** вашей организации к внедрению
-2. **Планирование бюджета** и получение approvals
-3. **Формирование команды** для deployment и support
-4. **Pilot deployment** с small group пользователей
-5. **Iterative improvement** на основе feedback
-6. **Gradual rollout** ко всей команде
-
-С правильной стратегией, инвестициями и commitment, self-hosted AI-инфраструктура становится мощным enabler productivity, качества работы и innovation в вашей организации.
-
----
-
-**Версия документа:** 1.0
-**Дата:** Январь 2026
-**Автор:** Based on infrastructure requirements для k3s-gitops
-**Статус:** Comprehensive Guide