Removes the "simple" fallback model and renames "pydantic" to "Tatlock"
to match the agent's British butler persona.
Changes:
- /models endpoint now returns only "Tatlock" model
- Removed "simple" model from advertised models
- Updated default model name from "pydantic" to "Tatlock"
- Updated health endpoint to show "Tatlock" agent status
- Added description: "PydanticAI agent with full tool support - your British butler assistant"
Benefits:
- Clearer model naming that matches agent persona
- Simplified model selection in Open WebUI
- Eliminates confusion between pydantic/simple models
- Consistent branding with Tatlock character
Open WebUI will now show only "Tatlock" as an available model, which uses
the full PydanticAI agent with tool calling capabilities.
Tested:
✅ /models endpoint returns only Tatlock
✅ Health check shows Tatlock as default agent
✅ Chat completions work with model="Tatlock"
✅ Tatlock persona responds correctly
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implement native Ollama agent that bypasses OpenAI-compatible API and uses
Ollama's native /api/chat endpoint for improved tool calling reliability.
Changes:
- Add OllamaNativeAgent class with native tool calling support
- Direct integration with Ollama /api/chat endpoint
- Better tool calling reliability vs OpenAI-compatible API
- Async streaming support
- Tool result handling and multi-turn conversations
- Set OllamaNativeAgent as default agent (replacing PydanticAI)
- Add test endpoint for Ollama tool verification
- Update health check to report ollama-native availability
- Add ollama>=0.4.0 to requirements for native library support
Technical Details:
- Uses Ollama's native tool format (not OpenAI functions)
- Handles tool execution and response synthesis
- Maintains conversation context across tool calls
- Model: mistral-nemo:latest (primary reasoning model)
Motivation:
PydanticAI uses Ollama's OpenAI-compatible endpoint which has less
reliable tool calling. The native API provides better tool support
and more consistent behavior.
🤖 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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