Implements Phase 1: Agent abstraction layer with multiple model support
Features:
- Abstract AgentInterface base class with standard contract
- LoremTesterAgent: Full-featured mock agent with realistic behavior
- Configurable reasoning effort levels (none to xhigh)
- Random tool/function call generation
- Error triggers for testing (rate_limit, context_overflow)
- Temperature-based response variation
- TatlockAgent: Placeholder for future PydanticAI integration
- ModelRegistry: Centralized model management and discovery
Testing:
- 9 unit tests for lorem-tester agent behavior
- 9 unit tests for registry operations
- Coverage: Agent abstraction fully tested
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implement application factory pattern with clean main.py.
Separate routers for each domain, centralized exception handling.
Core Router (src/core/router.py):
- Root endpoint (/)
- Health check endpoint (/health)
- Simple status responses
- No prefix (mounted at root)
Main Application (src/main.py):
- create_application() factory function
- Clean configuration-focused main.py
- CORS middleware setup
- Exception handler registration
- Router registration with proper prefixes
- Global config integration
Application Architecture:
- Application factory pattern for testability
- Routers imported from separate controllers
- Exception handlers in dedicated function
- All routes cleanly separated by domain
Exception Handling:
- OpenAI-compatible error format
- Custom AppException handler
- Validation error handler (422)
- Generic exception handler (500)
Following Best Practices:
- Separation of concerns
- Factory pattern for DI
- Clean main.py (config only)
- Type hints throughout
Status: Production-ready structure
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add OpenAI-compatible models listing endpoint.
Currently returns mock model (mistral-nemo:latest).
Models Router (src/models/router.py):
- GET /v1/models endpoint
- OpenAI-compatible response format
- Lists available models
Models Schemas (src/models/schemas.py):
- Model object with id, created, owned_by
- ModelsListResponse with data array
- Full OpenAI API compatibility
Models Service (src/models/service.py):
- list_models() function
- Mock model listing (ready for Ollama integration)
- Returns mistral-nemo:latest as default
Following Best Practices:
- Business logic in service layer
- Router only handles HTTP concerns
- Type hints throughout
- Async/await pattern
Model: mistral-nemo:latest
Status: Mock implementation (ready for Ollama integration)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add OpenAI-compatible chat completions endpoint with streaming support.
Currently returns mock lorem ipsum responses (Ollama integration pending).
Chat Router (src/chat/router.py):
- POST /v1/chat/completions endpoint
- Streaming and non-streaming support
- SSE format with EventSourceResponse
- 20-second timeout protection
- OpenAI-compatible response format
Chat Schemas (src/chat/schemas.py):
- ChatMessage, ChatCompletionRequest
- ChatCompletionResponse, ChatCompletionChoice
- ChatCompletionChunk for streaming
- Full OpenAI API compatibility
Chat Service (src/chat/service.py):
- create_chat_completion() - non-streaming
- create_chat_completion_stream() - streaming word-by-word
- Mock lorem ipsum responses
- Token usage calculation
Chat Constants (src/chat/constants.py):
- OpenAI API constants for consistency
- Object types, roles, finish reasons
Following Best Practices:
- Business logic in service layer
- Router only handles HTTP concerns
- Async generators for streaming
- Type hints throughout
Model: mistral-nemo:latest
Status: Mock implementation (ready for Ollama integration)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add production-ready async HTTP client for Ollama API communication
with proper error handling and dependency injection.
Ollama Client (src/ollama/client.py):
- Async context manager for connection lifecycle
- Non-streaming chat endpoint
- Streaming chat endpoint with async generator
- Model listing endpoint
- Health check endpoint
- Timeout configuration per request
- Comprehensive error handling with custom exceptions
- FastAPI dependency injection support
Ollama Schemas (src/ollama/schemas.py):
- OllamaMessage: Chat message format
- OllamaChatRequest: Request with model, messages, options
- OllamaChatResponse: Complete chat response
- OllamaModelInfo: Model metadata
- OllamaModelsResponse: Model list response
Features:
- Async/await throughout for non-blocking I/O
- Connection pooling via httpx.AsyncClient
- Configurable timeouts (default: 120s)
- Proper exception mapping (connection errors, timeouts)
- Ready for integration (currently not connected to routes)
Following Best Practices:
- Async context manager pattern
- Dependency injection for FastAPI routes
- Separation of concerns (client vs schemas)
- Type hints throughout
- Comprehensive logging
Status: Ready for integration (mock responses used in routes currently)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implement global application configuration and custom Pydantic models
following FastAPI best practices.
Core Configuration (src/core/config.py):
- BaseSettings with environment variable support
- Split configuration by domain (following best practices)
- Ollama connection settings (host, model, timeouts)
- API configuration (host, port, prefix)
- CORS settings
- 20-second streaming timeout per turn
- Cached configuration with @lru_cache
Custom Base Models (src/core/models.py):
- CustomBaseModel for consistent serialization
- ISO datetime formatting
- Alias population support
- Enum value serialization
- Validation on assignment
- serializable_dict() for logging/debugging
Exception Handling (src/core/exceptions.py):
- Base AppException with status codes
- OllamaConnectionError (503)
- OllamaTimeoutError (504)
- ModelNotFoundError (404)
- ValidationError (422)
- OpenAI-compatible error structure
Benefits:
- Consistent configuration across domains
- Type-safe settings with validation
- Easy environment override via .env
- Predictable error responses
- Better debugging with serializable models
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Set up Python 3.12.11 FastAPI project with security-focused dependency management.
Dependencies:
- FastAPI 0.123.9: Modern web framework
- Uvicorn 0.38.0: ASGI server with standard extras
- Pydantic 2.12.5: Data validation (updated for pydantic-ai)
- PydanticAI 1.27.0: LLM agent framework with Ollama support
- HTTPX 0.28.1: Async HTTP client
- SSE-Starlette 3.0.2: Server-Sent Events for streaming
- python-dotenv 1.2.1: Environment configuration
Security:
- All packages checked for CVEs (as of 2025-12-06)
- Minor version locking (>=X.Y,<X.(Y+1)) for supply chain protection
- CVE status documented in requirements.txt
Development tools:
- pytest 8.3.5 + pytest-asyncio for async testing
- pytest-cov 6.0.0 for coverage reporting
- ruff 0.8.6 for linting and formatting
- mypy 1.14.1 for type checking
Configuration:
- pyproject.toml: Build system, coverage, ruff, and mypy config
- .env.example: Environment variable template
- .gitignore: Comprehensive Python/FastAPI patterns
Target model: mistral-nemo:latest on external Ollama instance
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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