After PydanticAI migration (Dec 3), AI agent functionality was moved to
separate core-ai service. Health check was still trying to validate agent
in core-api, causing persistent unhealthy status (503 errors).
Changes:
- Remove ADK agent import attempts (no longer exists in core-api)
- Update /health/full to only check Ollama connectivity
- Update diagnostics endpoint with service separation notes
- Clarify that core-api is infrastructure/tools API only
Result: Container now reports healthy status consistently (200 OK).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Removed all AI/LLM functionality from core-api as it has been
migrated to the dedicated core-ai service.
Deleted:
- src/controllers/ai_controller.py (chat completions, models, conversations)
- src/agent/ (orchestrator, tools, prompts, streaming)
- src/memory/ (manager, qdrant, buffer, schemas)
- src/api/v1/ (chat, conversations, models, schemas)
- tests/test_memory_*.py (3 test files)
Removed dependencies:
- google-adk, litellm, google-cloud-aiplatform
- qdrant-client
Kept:
- tools_controller.py (web scraper for core-ai REST calls)
- infrastructure_controller.py
- health_controller.py
- static_controller.py
core-api is now purely for infrastructure management.
All AI operations are handled by core-ai service.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major Changes:
- Replace Google ADK with PydanticAI framework for agent orchestration
- Implement OpenAI-compatible API endpoint for Ollama integration
- Fix streaming response to send deltas instead of cumulative text
- Add /chat/completions route alias for Open-WebUI compatibility
- Enable tool calling with 5 local tools (calculate, date/time utilities)
Architecture:
- Core-AI service: Standalone Python service with PydanticAI agent
- PydanticAI: Uses OpenAI-compatible Ollama API at /v1 endpoint
- Tool Registry: Shared tool system between core-ai and core-api
- Streaming: Fixed async context issues and delta calculation
Verified Working:
✅ Chat completion (streaming & non-streaming)
✅ Tool calling with mistral-nemo and mistral-tools models
✅ Open-WebUI integration via core-ai:8086
✅ 5 tools: calculate, get_current_time, get_current_date, calculate_date_difference, add_days_to_date
✅ Proper streaming deltas (no repetition)
Technical Details:
- PydanticAI 1.25.0+ with full Ollama support
- Async context manager issue resolved via chunk collection
- Delta calculation: chunk[len(previous):] to extract new content only
- Routes: /v1/chat/completions and /chat/completions (Open-WebUI compat)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Phase completion and enhancement updates:
## Documentation Added
- Phase 2 completion: Memory system implementation details
- Phase 3 completion: Research capabilities and tool integration
- Session documentation: Model testing, VRAM optimization analysis
- Test results: Comprehensive prompt testing (v1_verbose: 87/100)
- Tool logging implementation guide
## System Prompts
- Added prompts.py with 7 tested variants for A/B testing
- v1_verbose, v2_concise, v3_imperative, v4_minimal, etc.
- Comprehensive testing results for each variant
- Production-ready prompt selection guidance
## Memory System Enhancements
- Multi-tenancy support: Added user_id parameter throughout
- System message filtering: Don't store system messages in history
- Improved conversation turn tracking with user isolation
- Enhanced memory manager for better multi-user support
## AI Controller Improvements
- Better memory integration with user_id support
- Enhanced error handling for memory operations
- Improved token tracking for usage monitoring
- Skip system message storage (part of agent state)
## Portainer Client
- Comprehensive API client (148 lines)
- Stack management and service monitoring
- Container operations with full error handling
- Async support for all operations
## Architecture Documentation
- Updated agent flow diagrams for ADK architecture
- Enhanced core-api README with current setup
- Updated Docker compose stack configuration
- Complete testing and validation documentation
Major architectural changes and improvements:
## ADK Framework Migration (v0.10.0)
- Migrated from LangChain/LangGraph to Google ADK 1.3.0 with LiteLLM 1.80.5
- Improved tool calling reliability with local Ollama models
- Converted all 10 tools to ADK async generator format
- Updated streaming pipeline for ADK event system
- Enhanced error handling and agent initialization
## Model Optimization
- Switched from gemma3:12b (10GB VRAM) to gemma3:4b (4.8GB VRAM)
- Reduced VRAM usage from 91% to 43% (5.4GB freed)
- Optimized for production stability with memory headroom
## Health Check System Overhaul
- Optimized /health/full: 6ms response (was 30s+)
- Added model verification: confirms configured model is available
- New /health/diagnostics endpoint with optional deep testing
- Added currently loaded models tracking
- Clear emoji status indicators (✅/❌/⚠️)
- Fixed AGENT_AVAILABLE flag export for proper health reporting
## Ollama Client Enhancements
- Added list_models() method for model inventory
- Enhanced model verification in health checks
- Better error handling and reporting
## Documentation Updates
- Updated STATUS.md to v0.10.0-adk-migration
- Comprehensive CHANGELOG.md entry with migration details
- Updated PLANS.md showing Phase 4 complete
- Updated ai-orchestrator-plan.md with ADK status
- Added MIGRATION_PLAN_LANGCHAIN_TO_ADK.md
- Added ADK_Ollama_Research.md with implementation analysis
## Technical Details
- 10 tools: 7 infrastructure + 2 research + 1 response tool
- Framework: Google ADK with UnifiedAgent pattern
- System prompt: v7_adk_best_practice
- Container health: Now passing Docker healthchecks
- Response times: Simple queries ~0.3-1s, Research ~4-7s
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