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jpmschweitzerandClaude Opus 4.5 40aafb0331 docs: update changelog for v0.12.0-library-desk-enhancements 
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-10 21:44:20 +01:00

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Changelog

All notable changes to the portainer-core project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Planned

  • AI Orchestrator Phases 5-6: Multi-agent workflows, RAG optimization, production hardening
  • Authentik SSO Milestones 4-5: Protect remaining services (deferred)
  • Disaster recovery and offsite backup strategy

[0.12.0-library-desk-enhancements] - 2025-12-10

Added

  • Wiki Change Detection - Real-time page change notifications via PostgreSQL LISTEN/NOTIFY

    • WikiChangeListener service connects to Wiki.js database
    • Automatic re-ingestion when pages are created/updated/deleted
    • Debouncing and loop prevention for automated updates
    • Webhook router as HTTP fallback mechanism
    • Setup scripts and documentation for PostgreSQL triggers

  • Taxonomy-Aware Classification - LLM uses existing wiki structure when classifying content

    • get_taxonomy_structure() extracts category/subcategory paths from wiki
    • Consolidation prompts include existing paths to prefer over creating new ones
    • Prevents duplicate category creation (e.g., reuses reference/political-entities/ instead of creating reference/military-alliances/)

  • WikiJS Client Methods

    • list_all_pages() - Fetch all pages with path prefix filter (replaces stale search index)
    • get_taxonomy_structure() - Extract category hierarchy for a user namespace

  • Test Coverage - New test files for recent features

    • test_graph_service.py - Document node tags, entity-stub skipping
    • test_ingestion.py - list_all_pages usage, batch ingestion
    • test_wiki_change_listener.py - PostgreSQL notification handling
    • Updated test_consolidation.py with taxonomy and ingestion_service tests
    • Updated test_integration.py with list_all_pages and taxonomy tests

Fixed

  • Ingestion Pipeline - Pages created during consolidation now properly indexed

    • ingestion_service was not passed to ConsolidationService in router
    • Added ingestion call to _update_page_with_facts() (was only in create path)

  • Bulk Re-index - /ingest/all endpoint now works reliably

    • Changed from search_pages("") (stale index) to list_all_pages()
    • Successfully re-indexed 95 pages

  • Document Node Tags - Neo4j Document nodes now include tags property

    • Prevents warnings in related documents query
    • Set via d.tags = $tags in MERGE query

  • WikiJS Integration Script - Page ID fetched via GraphQL

    • Uses pages.singleByPath(path, locale) query during initialization
    • Replaces unreliable page list search method

Changed

  • Entity Linking - Improved matching with fuzzy logic

    • Confidence scoring for containment and token overlap matches
    • Self-referential link filtering (entities don't link to current page)
    • Path preservation (keeps full /users/username/path format)
    • Longest-first matching to prevent partial matches

  • Consolidation Processing - Searches marked processed even when skipped/errored

    • Prevents unprocessed searches from accumulating indefinitely

  • Stack Configuration - Updated library-desk environment variables

    • Replaced WIKIJS_API_KEY with WIKIJS_USERNAME/WIKIJS_PASSWORD
    • Added WIKIJS_DB_PASSWORD for PostgreSQL connection

Technical Details

  • Dependencies: Added asyncpg~=0.29.0 for PostgreSQL async driver
  • New Files: 10 files added (services, routers, tests, documentation)
  • Commits: 10 logical commits covering all changes

[0.11.0-pydantic-ai-cleanup] - 2025-12-03

Major Changes

  • Framework Cleanup: PydanticAI Only ✅ ARCHITECTURAL SIMPLIFICATION
    • Removed all obsolete agent implementations (OllamaNativeAgent, ADK, LangChain, LangGraph)
    • Kept only PydanticAgent (primary) and SimpleLiteLLMAgent (fallback)
    • Single framework approach eliminates confusion and improves maintainability
    • All documentation updated to reflect PydanticAI architecture

Removed

  • Obsolete Agent Files:

    • src/agents/ollama_native_agent.py - Replaced by PydanticAgent
    • Diagnostic and phase completion documentation files
    • Obsolete test files (test_ai_flow_quality.py, test_02/03 diagnostic tests)
    • Research documentation about ADK/LangChain

  • Obsolete Documentation:

    • ARCHITECTURE.md, DIAGNOSTIC_RESULTS.md, PHASE*.md files
    • docs/ADK_Ollama_Research.md
    • docs/architecture/agent-flow-diagrams.md (LangGraph references)
    • Session docs with LangChain/LangGraph implementations
    • Completed plans about ADK/LangChain migrations

Changed

  • main.py: Complete refactor to use only PydanticAI (305 lines vs 457 before)

    • Removed chat_completions() endpoint using OllamaNativeAgent
    • Removed test_ollama_tools() diagnostic endpoint
    • Default /v1/chat/completions now routes to PydanticAgent
    • Simplified health checks (removed ollama-native status)

  • agents/__init__.py: Removed OllamaNativeAgent exports

  • Documentation Updates:

    • services/core-ai/README.md - Complete rewrite for PydanticAI architecture
    • plans/active/ai-orchestrator-plan.md - Updated to reference PydanticAI
    • plans/active/unified-agent-architecture.md - Updated to reference PydanticAI
    • STATUS.md - Updated to show PydanticAI implementation (v0.11.0)

  • Plans Cleanup:

    • Deleted 5 completed plans about obsolete frameworks
    • Updated active plans to use PydanticAI terminology

Technical Details

Current Architecture (as of 2025-12-03):

  • Framework: PydanticAI with native Ollama SDK
  • Agents: PydanticAgent (primary) + SimpleLiteLLMAgent (fallback)
  • Model: mistral-nemo:latest
  • Tools: 6 core + 28+ OpenAPI-discovered from core-api
  • Memory: 3-tier system with Qdrant
  • VRAM: ~4-6GB

Files Removed: 13 obsolete files (agents, tests, docs) Files Modified: 8 files (main.py, agents/__init__.py, plans, docs) Lines Removed: ~3000+ lines of obsolete code

[0.10.1-phase-completion] - 2025-11-26

Added

  • Phase 2/3 Completion Documentation
    • Added phase2-memory-system-complete.md with full implementation details
    • Added phase3-multi-agent-workflows-complete.md documenting research capabilities
    • Added system prompts file (prompts.py) with 7 tested variants for A/B testing
    • Session documentation for model testing and VRAM optimization
  • Test Results & Analysis
    • Comprehensive prompt testing results (87/100 score for v1_verbose)
    • Model comparison testing (Mistral, Gemma, tool calling validation)
    • Tool logging implementation documentation
    • Verified test results for production readiness
  • Portainer Client Enhancements
    • Added comprehensive Portainer API client (148 lines)
    • Stack management, service monitoring, container operations
    • Full error handling and async support

Changed

  • Memory System Improvements
    • Added user_id parameter for multi-tenancy support
    • Skip storing system messages (part of agent state_modifier)
    • Enhanced memory manager with better user isolation
    • Improved conversation turn tracking
  • AI Controller Enhancements
    • Better memory integration with user_id support
    • Improved error handling for memory operations
    • Enhanced token tracking for usage monitoring
  • Architecture Documentation
    • Updated agent flow diagrams to reflect ADK architecture
    • Enhanced core-api README with current setup
    • Updated Docker compose stack configuration

Technical Details

  • Files Added:
    • services/core-api/src/agent/prompts.py - 7 system prompt variants
    • plans/completed/phase2-memory-system-complete.md
    • plans/completed/phase3-multi-agent-workflows-complete.md
    • services/core-api/COMPREHENSIVE_PROMPT_TEST_RESULTS.md
    • services/core-api/TOOL_LOGGING_IMPLEMENTATION.md
    • docs/sessions/2025-11-24-*.md - Model testing documentation
  • Memory System:
    • Multi-tenancy support with user_id throughout
    • System message filtering (not stored in history)
    • Improved conversation metadata tracking

[0.10.0-adk-migration] - 2025-11-26

Added

  • AI Orchestrator: Google ADK Framework Migration ✅ MAJOR ARCHITECTURAL CHANGE
    • New Framework: Google ADK 1.3.0
      • Migrated from LangChain/LangGraph to Google's Agent Development Kit
      • LiteLLM 1.80.5 integration for Ollama compatibility
      • Improved tool calling reliability with local models
      • Better streaming support with ADK event system
    • Model Upgrade: gemma3:12b
      • Upgraded from mistral:7b for better capability
      • Optimized for tool calling with ADK framework
      • ~8GB VRAM usage (vs ~4GB with mistral:7b)
    • Tool Migration
      • All 9 tools converted to ADK async generator format
      • Infrastructure tools (7): time, services, stacks, NPM
      • Research tools (2): web_search, web_scrape
      • Improved error handling and streaming progress
    • System Prompt Optimization
      • New v7_adk_best_practice prompt variant
      • Optimized for ADK agent behavior
      • Better tool usage patterns
    • Enhanced Health Checks
      • Agent-specific health monitoring
      • Tool availability verification
      • LiteLLM connection validation

Changed

  • Agent Architecture
    • Replaced LangGraph create_react_agent with ADK Agent
    • Changed from LangChain tools to ADK async generators
    • Updated streaming pipeline for ADK event format
    • Simplified orchestrator.py (more maintainable)
  • Dependencies
    • Removed: langchain, langgraph, langchain-community, langchain-core, langchain-ollama
    • Added: google-adk==1.3.0, google-genai==1.17.0, litellm==1.80.5
    • Updated: pydantic>=2.11.1, uvicorn>=0.34.0, httpx>=0.28.0
  • Configuration
    • Default model: mistral:7b → gemma3:12b
    • Agent model: mistral:7b → gemma3:12b
    • System prompt: v1_verbose → v7_adk_best_practice

Fixed

  • Tool Calling Reliability
    • Issue: LangGraph agents not calling tools with Ollama models
    • Root cause: LangGraph ReAct agent incompatibility with local models
    • Fix: Migrated to Google ADK with proven Ollama support
    • Result: Consistent tool calling across all query types
  • Model Compatibility
    • Issue: Gemma models failing with LangChain (status 400)
    • Fix: ADK supports Gemma family natively via LiteLLM
    • Result: Can now use gemma3:12b, gemma3:4b, and other Gemma variants
  • Streaming Consistency
    • Issue: Inconsistent streaming behavior with LangGraph
    • Fix: ADK provides unified event streaming
    • Result: Clean, consistent SSE output for frontend

Technical Details

  • Files Modified:
    • services/core-api/requirements.txt - Dependency overhaul
    • services/core-api/src/agent/orchestrator.py - Complete rewrite for ADK
    • services/core-api/src/agent/tools.py - Converted 9 tools to ADK format
    • services/core-api/src/agent/streaming.py - Updated for ADK events
    • services/core-api/src/controllers/ai_controller.py - Enhanced error handling
    • services/core-api/src/controllers/health_controller.py - Added agent health checks
    • services/core-api/src/config.py - Updated model and prompt settings
    • services/core-api/Dockerfile - Updated base dependencies
  • Architecture Impact:
    • Migration preserves existing API contracts
    • Memory system (Qdrant) completely unaffected
    • Tool implementations (logic) unchanged, only decorators updated
    • Frontend integration (SSE streaming) maintained
  • Performance:
    • Simple queries: ~0.3-1s (similar to LangChain)
    • Tool-using queries: ~2-5s (improved from LangChain)
    • Research queries: ~4-7s (maintained from Phase 3)
    • VRAM: ~8GB with gemma3:12b (~4GB increase)

Migration Notes

  • Reason for Migration: LangChain/LangGraph's create_react_agent failed to trigger tool calls with Ollama models despite proper configuration
  • Migration Duration: ~6 hours (as estimated in migration plan)
  • Testing: Validated with time queries, service queries, and web searches
  • Rollback: Previous LangChain implementation preserved in git history
  • Documentation: See MIGRATION_PLAN_LANGCHAIN_TO_ADK.md for details

Known Issues

  • None currently identified - monitoring in progress

Performance Monitoring

  • Tool calling success rate: Being tracked post-migration
  • Response latency: Within targets (<5s for research)
  • VRAM utilization: ~8GB (acceptable for RTX 2080 Ti 11GB)
  • Error rate: Monitoring for ADK-specific issues

[0.9.0-ai-memory-system] - 2025-11-23

Added

  • AI Orchestrator Phase 2: Memory System ✅ COMPLETE
    • Tier 1: ConversationBufferMemory
      • In-memory storage for last 10 turns per conversation
      • < 1ms access time, automatic pruning
      • OrderedDict-based storage with conversation metadata
    • Tier 2/3: Unified Qdrant Storage
      • Collection: core_api_conversations
      • Embedding model: nomic-embed-text (768 dimensions via Ollama)
      • Persistent storage + semantic search capabilities
      • Chronological retrieval (Tier 2 mode)
      • Semantic similarity search (Tier 3 mode)
    • Auto-Consolidation Service
      • Triggers at 10 turns (when buffer fills)
      • Moves buffer turns → Qdrant automatically
      • Maintains conversation continuity
    • Dual-Retrieval System
      • Checks both buffer (Tier 1) AND Qdrant (Tier 2/3)
      • Cross-restart persistence working
      • Combines memory tiers via get_full_history()
    • Phase 2.5: Multi-Tenancy
      • Added user_id field to all memory schemas
      • Default user: "llm-testuser" for unauthenticated traffic
      • Single collection with user_id filtering approach
      • Tested with multiple users successfully

Changed

  • Memory Architecture
    • Original plan: Tier 2 (SQLite summaries) + Tier 3 (Qdrant semantic)
    • Implemented: Unified Tier 2/3 in Qdrant (simpler, more efficient)
    • Rationale: Qdrant handles both persistent storage and semantic search
  • Embedding Dimension
    • Changed from 384d (all-MiniLM-L6-v2) to 768d (nomic-embed-text)
    • Better semantic quality, still efficient for local deployment
  • Logging Level
    • Changed memory storage logs from DEBUG to INFO for visibility
    • Helps verify storage execution without verbose output

Fixed

  • Memory Storage Integration (Priority 1.1)
    • Issue: Memory storage code reached but not executing
    • Root cause: Log level set to WARNING, logger.debug() invisible
    • Fix: Changed logger.debug() → logger.info() for storage paths
    • Verification: 32 points successfully stored in Qdrant
  • Memory Persistence (Priority 2.1)
    • Issue: Agent didn't recall conversations after restart
    • Root cause: Only checking buffer (Tier 1), empty after restart
    • Fix: Added dual-check (buffer_exists OR qdrant_exists)
    • Verification: Agent correctly recalled "purple" after restart

Technical Details

Deferred

  • Optional Future Enhancements:
    • Time-based consolidation for short conversations (< 10 turns)
    • User filtering in Qdrant queries
    • User management API (list users, delete user data)
    • Migration to LangGraph checkpointers (Phase 3 roadmap)

[0.8.1-authentik-organizr] - 2025-11-21

Added

  • Standalone Authentik Proxy Outpost
    • Container: authentik-proxy (port 9445:9443)
    • Redis configuration: redis-shared:6379/0
    • Memory usage: ~150MB
    • API token authentication with Authentik server
    • WebSocket connection to Authentik for config updates
  • Forward Authentication for Organizr
    • NPM configuration for home.schweitz.net
    • auth_request directive pointing to standalone outpost
    • Authentication header forwarding (X-authentik-username, email, groups, name, uid)
    • Signin redirect handler for unauthenticated requests
    • WebSocket support enabled
  • Documentation

Fixed

  • Embedded Outpost Issue: Authentik 2024.8.4 embedded outpost not initializing auth endpoint (version-specific bug)
  • Network Connectivity: NPM on host network cannot resolve docker-dataplane container names - use localhost:9445
  • NPM Config Generation: API updates don't generate config files - manually created /data/nginx/proxy_host/2.conf
  • Redirect Loop: Initial redirect to /outpost.goauthentik.io/start returned 404 - changed to use application domain
  • Post-Login Redirect: Direct flow redirect sent users to /if/user/#/library - use outpost start endpoint instead
  • Organizr Auto-Login: Headers set at server level don't forward - moved proxy_set_header to location / block

Changed

  • Outpost Architecture: Moved from embedded to standalone for reliability (port 9445:9443)

[0.8.0-authentik-sso] - 2025-11-20

Added

  • Authentik Identity Provider (version 2024.8.4)
    • Server container (port 9000) with 512MB memory limit
    • Worker container with 384MB memory limit
    • Total memory usage: 563MB (80-90% reduction vs previous attempt)
    • Embedded outpost on port 9444
  • Shared Infrastructure Integration
    • PostgreSQL: authentik database with authentik_user
    • Redis: Database 0 for sessions and cache
    • Docker network: docker-dataplane
  • Google OAuth Integration
    • OAuth source configured via API
    • Google login button on authentication flow
    • Automatic user creation for external OAuth users
    • Successful test: jpmschweitzer@gmail.com user created
  • NPM Configuration
    • Reverse proxy for https://auth.schweitz.net
    • Let's Encrypt SSL with HSTS
    • No forward auth on auth.schweitz.net (prevents redirect loops)
  • API Automation
    • Created proxy provider "Organizr Proxy" via API
    • Created application "Organizr" via API
    • Assigned provider to embedded outpost via API
  • Documentation
    • Session summary: docs/sessions/2025-11-20-authentik-deployment.md
    • Updated STATUS.md with SSO progress
    • Updated security implementation plan

Fixed

  • Health check failing due to missing wget/curl - switched to Python urllib
  • Database user authentik_user not created - manually created with grants
  • Port 9443 conflict - mapped to 9444 on host
  • NPM proxy host marked as deleted - recreated via UI
  • Google OAuth enrollment flow error - cleared browser cookies

Changed

  • Container count: 19 → 21 (added authentik-server, authentik-worker)
  • Active priority: AI Orchestrator → Security & SSO Implementation
  • Deferred AI Orchestrator Phase 2 to focus on security

Known Issues

  • Embedded outpost auth endpoint returns 404
    • Endpoint: /outpost.goauthentik.io/auth/nginx not available
    • Ping endpoint works, but auth endpoint not initialized
    • Blocking forward authentication for Organizr
    • Investigating provider mode and initialization sequence

[0.7.1-gitea-deployment] - 2025-11-14

Added

  • Gitea Git repository hosting service (port 3002, SSH port 2222)
  • PostgreSQL database backend for Gitea
  • NPM reverse proxy configuration for https://git.schweitz.net with Let's Encrypt SSL
  • Uptime Kuma monitoring integration for Gitea
  • Organizr dashboard integration for Gitea
  • Complete Gitea documentation in CONTAINERS.md

Changed

  • Updated infrastructure status to reflect 19 deployed services

[0.7.0-ai-orchestrator-phase1] - 2025-11-13

Added

  • AI Orchestrator Phase 1: OpenAI-Compatible API
    • /v1/chat/completions endpoint with streaming and non-streaming support
    • /v1/models endpoint for model discovery
    • Model aliasing system (gpt-3.5-turbo → gemma:7b, etc.)
    • Ollama client with connection pooling
    • Pydantic request/response schemas
    • Server-Sent Events (SSE) streaming format
  • Comprehensive Phase 1 testing suite
    • 10/10 tests passing, zero issues
    • 245ms average response time
    • 100% OpenAI API compatibility verified
  • Phase 1 implementation guide and test results documentation

Fixed

  • Model ID formatting issue (removed extra quotes in model names)

Security

  • Deployed on isolated ai-dataplane network

[0.6.0-applications] - 2025-11-13

Added

  • Nextcloud cloud storage and collaboration platform (port 8082)
    • MariaDB database backend
    • Redis caching
    • NPM reverse proxy with https://cloud.schweitz.net
    • Database optimization (indices, bigint conversion)
    • Cron background jobs via maintenance container
  • Samba network file sharing (ports 139/445)
    • Media share (R/W)
    • Downloads share (R/W)
    • Backups share (R/O)
  • UFW firewall rules for Samba ports
  • Uptime Kuma multi-network bridge for monitoring all services

Changed

  • Disabled host Samba service to prevent port conflicts
  • Relocated Nextcloud cron to maintenance container for centralized scheduling

Fixed

  • Uptime Kuma network connectivity issues (added bridges to all service networks)

[0.5.2-core-api] - 2025-11-13

Added

  • Core API service for Open WebUI integration (port 8083)
  • Web scraper module with Trafilatura and BeautifulSoup
  • Infrastructure management API (Portainer/NPM/Kuma integration)
  • OpenAPI documentation at /docs endpoint
  • Health check endpoint
  • Uptime Kuma monitoring integration
  • Organizr dashboard integration

Changed

  • Upgraded system Python from 3.8 (EOL) to 3.12

Security

  • Runs as non-root user (uid 1000)
  • CORS configured for same-network access only

[0.5.1-open-webui] - 2025-11-12

Added

  • Open WebUI LLM chat interface (port 8081)
  • Built-in voice capabilities (local STT/TTS)
  • Ollama integration for local model inference
  • Uptime Kuma monitoring integration
  • Organizr dashboard integration (tab + homepage)
  • Complete Open WebUI documentation in CONTAINERS.md

[0.5.0-optimization] - 2025-11-11

Added

  • Phase 4: Optimization & Security
    • Watchtower for automatic container updates (daily at 4 AM)
    • Maintenance container for automated backups and scheduled tasks
    • Automated Docker config backups (daily at 3 AM, 30-day retention)
  • Docker log rotation configuration (10MB max, 3 files per container)
  • UFW firewall rules (SSH, Tailscale, infrastructure services)

Security

  • Firewall enabled and configured for all public-facing services
  • Automated backup system with 30-day retention (~94MB per backup)

[0.4.0-monitoring] - 2025-11-11

Added

  • Phase 3: Monitoring Stack
    • Uptime Kuma service monitoring (port 3001)
    • Netdata real-time system metrics (port 19999)
    • Heimdall unified dashboard (port 8888)
  • Complete monitoring documentation

[0.3.0-networking] - 2025-11-11

Added

  • Phase 2: Networking & External Access
    • Headscale mesh VPN control server (port 8085)
    • Custom 10.99.0.0/16 network range
    • Homelab user and pre-auth key system
    • Device connection procedures for all platforms
  • Headscale setup documentation

[0.2.0-foundation] - 2025-11-11

Added

  • Phase 1: Foundation Setup
    • Portainer container management (port 8001, host networking)
    • Nginx Proxy Manager reverse proxy (port 81, host networking)
    • Ollama ML model serving (port 11434, GPU-enabled)
    • NVIDIA Container Toolkit (v1.17.9-1 for driver 470 compatibility)
    • GPU management via docker-compose deploy configuration
    • 4TB media drive mounted at /mnt/media
    • User added to docker group

Fixed

  • Docker networking issues (iptables FORWARD chain, host networking solution)
  • AMP integration (kept on port 8080, no conflicts)

Security

  • GPU passthrough configured securely
  • Storage permissions set for dual-disk strategy

[0.1.0-planning] - 2025-11-11

Added

  • Initial project structure and documentation
  • Comprehensive research document (containers/research.md)
    • Evaluated 8 different home server solutions
    • Identified Portainer + Docker Compose as optimal choice
    • Researched SDN solutions (Headscale vs Tailscale)
  • Detailed implementation plan (containers/implementation-plan.md)
    • 4-phase deployment strategy
    • Phase 1: Foundation (Portainer, NPM, Ollama, storage)
    • Phase 2: Networking (Headscale)
    • Phase 3: Monitoring (Uptime Kuma, Netdata, Heimdall)
    • Phase 4: Optimization (Watchtower, backups, security)
    • Application backlog (Jellyfin, Nextcloud, Samba)
  • System documentation (SYSTEM.md)
    • Hardware specifications
    • Dual-disk storage configuration
    • Software inventory
  • Agent guidelines (AGENTS.md)
    • Project-specific conventions
    • Docker Compose standards
    • GPU service requirements
    • Testing procedures
    • Commit message format
  • Project status tracking (STATUS.md)
  • Version-controlled infrastructure (stacks/ directory)
  • Maintenance automation (scripts/ directory, Makefile)

Documented

  • Storage architecture: SSD (489GB) for configs, HDD (3.7TB) for content
  • Port allocation strategy
  • AMP game server integration approach
  • GPU passthrough requirements for Jellyfin and Ollama
  • Security considerations (Headscale, UFW, credentials management)

Decisions

  • Architecture: Portainer + Docker Compose (chosen over TrueNAS Scale, Unraid, Proxmox)
    • Reason: No OS reinstall required, leverages existing Docker, minimal storage footprint
  • Reverse Proxy: Nginx Proxy Manager on port 8000 (unified web interface)
  • SDN: Headscale (self-hosted Tailscale control server)
  • ML Infrastructure: Ollama with GPU support (RTX 2080 Ti)
  • Monitoring: Uptime Kuma + Netdata + Heimdall
  • Storage Strategy: Dual-disk approach (SSD for performance, HDD for capacity)

Changelog Guidelines

Categories

Use these categories for changes:

  • Added - New features, services, or capabilities
  • Changed - Changes to existing functionality
  • Deprecated - Soon-to-be-removed features
  • Removed - Removed features
  • Fixed - Bug fixes
  • Security - Security improvements

Version Numbering

  • Major (X.0.0): Breaking changes, major architecture changes
  • Minor (0.X.0): New features, service additions, phase completions
  • Patch (0.0.X): Bug fixes, configuration tweaks, documentation updates
  • Suffix: -planning, -alpha, -beta for pre-release stages

Example Entry Template

## [X.Y.Z] - YYYY-MM-DD

### Added
- feat(stack): deployed nginx proxy manager for unified web interface
- feat(ollama): configured GPU passthrough for ML model inference

### Changed
- config(amp): moved from port 8080 to 8081 to avoid conflicts

### Fixed
- fix(storage): corrected permissions on media drive mount

### Security
- chore(firewall): configured UFW rules for service isolation

This changelog is updated as features are implemented and phases are completed

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