# LLM Agent Instructions

This document contains instructions and documentation references for AI assistants working with this codebase.

## Project Overview

This project implements an OpenAI-compatible API endpoint using FastAPI, with streaming support. Currently returns mock responses - infrastructure prepared for future Ollama/PydanticAI integration.

**Current State**: Production-ready mock API with OpenAI-compatible format
**Future Integration**: Ollama and PydanticAI (client code ready, not connected)

### Components

- **FastAPI**: Web framework for the API layer
- **SSE-Starlette**: Server-Sent Events for streaming responses
- **Pydantic**: Request/response validation
- **PydanticAI**: Dependency installed, ready for future LLM integration
- **Ollama**: Async client implemented, ready for future connection

## Documentation References

### Core Framework Documentation

#### FastAPI
- **Official Documentation**: https://fastapi.tiangolo.com/
- **Version**: 0.123.9 (Dec 2025)
- **Key Topics**:
  - Path operations and routing
  - Request/response models with Pydantic
  - Dependency injection
  - Background tasks
  - WebSocket and streaming support
- **PyPI**: https://pypi.org/project/fastapi/

#### Uvicorn
- **Official Documentation**: https://www.uvicorn.org/
- **Version**: 0.38.0 (Oct 2025)
- **Key Topics**:
  - ASGI server configuration
  - Deployment settings
  - Logging and monitoring
  - SSL/TLS configuration

### AI/LLM Integration

#### PydanticAI
- **Official Documentation**: https://ai.pydantic.dev/
- **Version**: 1.27.0 (Dec 2025)
- **Status**: Dependency installed, ready for future integration
- **Key Topics** (for future implementation):
  - Agent creation and configuration
  - LLM provider integration (Ollama support)
  - Structured outputs with Pydantic
  - Streaming responses
  - Tool/function calling
  - RunContext and dynamic configuration
  - MCP server integration
- **GitHub**: https://github.com/pydantic/pydantic-ai
- **PyPI**: https://pypi.org/project/pydantic-ai/

#### Pydantic
- **Official Documentation**: https://docs.pydantic.dev/latest/
- **Version**: 2.11+ (Required for PydanticAI, currently using >=2.11,<2.13)
- **Key Topics**:
  - Data validation and serialization
  - Field types and validators
  - Model configuration
  - JSON schema generation

### HTTP and Streaming

#### HTTPX
- **Official Documentation**: https://www.python-httpx.org/
- **Version**: 0.28.1
- **Key Topics**:
  - Async HTTP client for Ollama communication
  - Streaming responses
  - Timeout configuration
  - Connection pooling

#### SSE-Starlette
- **GitHub**: https://github.com/sysid/sse-starlette
- **Version**: 3.0.2 (Oct 2025)
- **Key Topics**:
  - Server-Sent Events implementation
  - Streaming event responses
  - Integration with FastAPI/Starlette

### Ollama Integration

#### Ollama API
- **Official Documentation**: https://github.com/ollama/ollama/blob/main/docs/api.md
- **Status**: Async client implemented in `src/ollama/client.py`, ready for future integration
- **Key Topics** (for future implementation):
  - REST API endpoints
  - Streaming responses
  - Model management
  - Generate and chat endpoints
  - Model configuration
- **Current Model Target**: mistral-nemo:latest

### OpenAI API Compatibility

#### OpenAI API Reference
- **Official Documentation**: https://platform.openai.com/docs/api-reference
- **Implemented Endpoints**:
  - ✅ `/v1/chat/completions` - Chat completion with streaming (mock responses)
  - ✅ `/v1/models` - List available models (mock listing)
- **Future Endpoints**:
  - 🚧 `/v1/completions` - Text completion (legacy)
  - 🚧 `/v1/embeddings` - Text embeddings
- **Implemented Features**:
  - ✅ Streaming with Server-Sent Events
  - ✅ Message format compatibility
  - ✅ Response structure compatibility
  - ✅ OpenAI error format
  - ✅ Request validation with Pydantic

## FastAPI Best Practices

This project follows best practices from [github.com/zhanymkanov/fastapi-best-practices](https://github.com/zhanymkanov/fastapi-best-practices)

### Project Structure

**Domain-Based Organization**: Code is organized by domain/feature rather than by file type:

```
src/
├── chat/                  # Chat completions domain
│   ├── router.py          # FastAPI routes
│   ├── schemas.py         # Pydantic request/response models
│   ├── service.py         # Business logic
│   ├── dependencies.py    # Domain-specific dependencies
│   ├── constants.py       # Domain constants
│   └── __init__.py
├── models/                # Models listing domain
│   ├── router.py
│   ├── schemas.py
│   ├── service.py
│   └── __init__.py
├── core/                  # Shared utilities
│   ├── config.py          # Global configuration
│   ├── models.py          # Custom base Pydantic models
│   ├── exceptions.py      # Global exceptions
│   ├── dependencies.py    # Shared dependencies
│   └── router.py          # Core routes (health, root)
├── ollama/                # Ollama client layer
│   ├── client.py          # Async Ollama HTTP client
│   ├── schemas.py         # Ollama API models
│   └── __init__.py
└── main.py                # Application factory & configuration
```

**Key Principles**:
- Each domain has its own router, schemas, models, service, etc.
- Cross-domain imports use explicit naming: `from src.auth import constants as auth_constants`
- Main.py focuses on configuration, middleware, and exception handlers
- Business logic stays in service modules
- Routes delegate to services for all business logic

### Async/Await Best Practices

**Critical Understanding**: FastAPI handles sync and async routes differently:

- **Async routes** (`async def`): Called directly in event loop
  - Use ONLY for non-blocking operations
  - Perfect for `await httpx.get()`, database queries, file I/O
  - **NEVER** use blocking calls like `time.sleep()` - this blocks entire server

- **Sync routes** (`def`): Run in thread pool
  - Use for CPU-intensive work or blocking SDKs
  - Blocking I/O won't freeze the event loop
  - Example: `time.sleep(10)` is safe here

**Example**:
```python
@router.get("/terrible")
async def terrible():
    time.sleep(10)  # ❌ BLOCKS ENTIRE SERVER

@router.get("/good")
def good():
    time.sleep(10)  # ✅ Runs in thread pool

@router.get("/perfect")
async def perfect():
    await asyncio.sleep(10)  # ✅ Non-blocking async
```

**For CPU-intensive tasks**: Use separate worker processes (not threads) due to Python's GIL.

### Pydantic Configuration

**Custom Base Model**: All schemas inherit from `CustomBaseModel` for consistent behavior:

```python
# src/core/models.py
class CustomBaseModel(BaseModel):
    model_config = ConfigDict(
        json_encoders={datetime: datetime_to_iso_str},
        populate_by_name=True,
        use_enum_values=True,
        validate_assignment=True,
    )

    def serializable_dict(self, **kwargs):
        """Return dict with only JSON-serializable fields."""
        return jsonable_encoder(self.model_dump(**kwargs))
```

**Benefits**:
- Consistent datetime serialization across all responses
- Alias support for field name flexibility
- Easy JSON encoding for logging/debugging

**Decoupled Settings**: Split configuration by domain instead of one monolithic file:

```python
# src/core/config.py - Global settings
class Config(BaseSettings):
    DATABASE_URL: PostgresDsn
    ENVIRONMENT: Environment

# src/chat/config.py - Chat-specific settings
class ChatConfig(BaseSettings):
    MAX_TOKENS: int
    DEFAULT_TEMPERATURE: float
```

### Dependency Injection Patterns

**Validation with Dependencies**: Use dependencies for complex validations:

```python
async def valid_post_id(post_id: UUID4) -> dict:
    """Validate post exists in database."""
    post = await service.get_by_id(post_id)
    if not post:
        raise PostNotFound()
    return post

@router.get("/posts/{post_id}")
async def get_post(post: dict = Depends(valid_post_id)):
    return post  # Already validated!
```

**Chaining Dependencies**: Build reusable validation layers:

```python
async def valid_owned_post(
    post: dict = Depends(valid_post_id),
    token_data: dict = Depends(parse_jwt_data),
) -> dict:
    if post["creator_id"] != token_data["user_id"]:
        raise UserNotOwner()
    return post
```

**Dependency Caching**: Dependencies are cached within request scope - FastAPI only executes each dependency once per request, even if used multiple times.

### Application Factory Pattern

Main.py uses factory pattern for testability and configuration:

```python
def create_application() -> FastAPI:
    """Create and configure FastAPI app."""
    app = FastAPI(title=config.APP_NAME)

    # Add middleware
    app.add_middleware(CORSMiddleware, ...)

    # Register exception handlers
    register_exception_handlers(app)

    # Include routers
    app.include_router(chat_router, prefix="/v1")

    return app

app = create_application()
```

## Development Guidelines

### Code Structure (Current Implementation)
- ✅ Use async/await for ALL I/O operations (database, HTTP, file access)
- ✅ Use sync (def) for blocking SDKs or CPU-intensive work
- ✅ Implement proper error handling and logging
- ✅ Follow dependency injection for validation and shared resources
- ✅ Use Pydantic models for ALL request/response validation
- ✅ Keep business logic in service modules, not routers
- ✅ Domain-based project structure (not file-type based)

### Security Considerations
- ✅ Validate all inputs using Pydantic models
- ✅ Use environment variables for sensitive configuration
- ✅ Keep dependencies updated (all CVE-checked as of 2025-12-06)
- ✅ Minor version locking for supply chain protection
- 🚧 Implement rate limiting for API endpoints (future)
- 🚧 Add authentication/API keys (future)

### Testing (Current Coverage: 62%)
- ✅ Integration tests for API endpoints
- ✅ Streaming functionality with 20s timeout protection
- ✅ Async test support with pytest-asyncio
- ✅ Validate OpenAI API compatibility
- ✅ Mock responses for all endpoints
- 🚧 Future: Mock Ollama responses when integrated

### Configuration
- ✅ Use `.env` files for local development
- ✅ Document all environment variables in README
- ✅ Provide sensible defaults where possible
- ✅ BaseSettings from pydantic-settings
- 🚧 Support container-based configuration (future)

## Common Patterns

### Streaming Response Pattern (✅ Implemented)

See `src/chat/router.py` for the current implementation:

```python
from sse_starlette.sse import EventSourceResponse
from fastapi import FastAPI

async def event_generator():
    # Currently yields mock lorem ipsum chunks
    # Future: Stream from Ollama/PydanticAI
    yield {"data": chunk.model_dump_json()}
    yield {"data": "[DONE]"}

@app.post("/stream")
async def stream():
    return EventSourceResponse(event_generator())
```

### PydanticAI Agent Pattern (🚧 Future Reference)

For future integration when connecting to Ollama:

```python
from pydantic_ai import Agent

agent = Agent(
    'ollama:mistral-nemo',  # Target model
    # Configuration here
)

# Use the agent
result = await agent.run('Your prompt')
```

### OpenAI-Compatible Response Format (✅ Implemented)

Current implementation in `src/chat/schemas.py`:

```python
{
    "id": "chatcmpl-123",
    "object": "chat.completion.chunk",
    "created": 1234567890,
    "model": "mistral-nemo:latest",
    "choices": [{
        "index": 0,
        "delta": {"content": "response"},
        "finish_reason": None
    }]
}
```

## Update Policy

This document should be updated when:
- Package versions are upgraded
- New major features are added
- Breaking API changes occur
- Security vulnerabilities are discovered

Last updated: 2025-12-06