- Fixed: Change `result.data` to `result.output` (correct PydanticAI API)
- Increased analysis_timeout from 3s to 10s (mistral-nemo needs more time)
**Status:** Steward now initializes correctly but there's a remaining issue
with the async generator merging logic in two_stage_agent.py causing
requests to hang. This needs further investigation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Introduces a comprehensive, multi-tiered memory system to provide conversation history and context for the AI agent. This lays the foundation for more stateful and intelligent interactions.
Key components of this implementation:
- **Multi-Tiered Memory Architecture:**
- **Tier 1 (Working Memory):** A fast, in-memory buffer (`ConversationBufferMemory`) that holds the most recent turns of a conversation for immediate access.
- **Tier 3 (Long-Term Memory):** A persistent, semantic search-based memory store using Qdrant (`QdrantConversationMemory`). It stores all conversation turns as vector embeddings, enabling long-term recall and similarity search.
- **Qdrant Integration:**
- The `qdrant-client` is added to manage collections and perform vector search operations.
- Each user is assigned a dedicated Qdrant collection for multi-tenancy.
- **Ollama Embedding Client:**
- A new `OllamaEmbeddingClient` generates text embeddings via the Ollama API, replacing the need for local sentence-transformer models. This significantly reduces the service's dependency footprint.
- **Configuration and Stack Updates:**
- The `config.py` and `core-ai.yml` stack file are updated with new settings for enabling memory, configuring Qdrant, and specifying the embedding model.
- **Utility and Schema Additions:**
- New Pydantic schemas (`memory/schemas.py`) define the data structures for conversation turns and memory management.
- Utility functions (`utils.py`) are added for user ID sanitization and collection naming.
This feature enhances the agent's capabilities by allowing it to maintain context across multiple turns and sessions, leading to more coherent and relevant responses.
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
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