This commit completes the cleanup of Google ADK references after migrating to PydanticAI.
Changes:
- Removed ADK agent implementation (adk_agent.py)
- Removed ADK test files (test_06, test_07, test_10)
- Removed ADK diagnostic files
- Updated config to use pydantic_system_prompt_variant instead of adk_system_prompt_variant
- Updated prompts.py to rename adk_agent to pydantic_agent
- Updated tool registry and tools.py docstrings to remove ADK references
- Added new comprehensive PydanticAI tests (test_06, test_07, test_10)
- Marked legacy ADK functions as deprecated for backwards compatibility
The codebase is now clean and stable with PydanticAI as the primary agent framework.
Docker container builds successfully with no ADK import errors.
🤖 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>
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