realtimex-services-api 0.1.3

Add your description here

RealTimeX Services API

A production-ready FastAPI application providing commonly used services in a maintainable, modular structure. This project is built on a feature-based architecture to ensure scalability and clear separation of concerns.

Features

• Agent Flows: A self-contained module providing testing, validation, and streaming endpoints for flow execution.
• Feature-Based Modular Architecture: Code is organized by business domain (e.g., "agent flows") for high cohesion and low coupling.
• Production Ready: Proper error handling, type hints, and dependency management.
• Health Monitoring: Built-in health check endpoints for observability.
• API Versioning: Endpoints are versioned (e.g., /api/v1/...) for long-term stability.
• OpenAPI Documentation: Auto-generated and interactive API documentation.

Project Structure

The project follows a feature-based (vertical slice) architecture. All code related to a specific business domain is co-located within a "module".

src/realtimex_services_api/
├── __init__.py
├── main.py                 # FastAPI app initialization and router inclusion
├── cli.py                  # CLI entry point
├── core/                   # Shared application-wide logic (config, dependencies)
└── modules/                # Top-level directory for all feature modules
    ├── __init__.py
    └── agent_flows/        # A self-contained module for the "Agent Flows" feature
        ├── __init__.py
        ├── api.py          # FastAPI router and HTTP-related logic
        ├── service.py      # Core business logic, decoupled from the web framework
        ├── schemas.py      # Pydantic models and data contracts
        └── utils/          # Utility functions specific to this module
            ├── __init__.py
            └── ...

Installation

# Install dependencies
uv sync

# Or using pip
pip install -e .

Usage

Running the Server

# Using the CLI command
realtimex-services-api

# Or directly with Python
python -m realtimex_services_api.cli

# Or with uvicorn directly
uvicorn realtimex_services_api.main:app --host 0.0.0.0 --port 8004

API Endpoints

• GET / - Root endpoint with service information
• GET /health - Health check for monitoring
• GET /docs - OpenAPI documentation (Swagger UI)
• GET /redoc - Alternative API documentation
• POST /api/v1/agent-flows/action/test - Execute flow in test mode
• POST /api/v1/agent-flows/action/validate - Validate flow configuration
• POST /api/v1/agent-flows/chat/stream - Stream chat responses

Development

Adding a New Feature Module

The architecture makes adding new, independent features straightforward.

1. Create the Module Directory: Create a new folder inside src/realtimex_services_api/modules/.

   mkdir src/realtimex_services_api/modules/new_feature
   

2. Create Standard Files: Inside the new directory, create the essential files: __init__.py, api.py (for the router), service.py (for business logic), and schemas.py (for Pydantic models).

3. Implement the Logic:

   • Define your FastAPI router in api.py.
   • Write your business logic in service.py, keeping it decoupled from FastAPI.
   • Define your request/response models in schemas.py.

4. Include the Router in main.py: Import and include the new router from your module's api.py file.

   # In main.py
   from .modules.new_feature.api import router as new_feature_router
   
   app.include_router(
       new_feature_router,
       prefix="/api/v1/new-feature",
       tags=["New Feature"]
   )
   

Code Standards

• Type Hints: All functions should include proper type annotations.
• Docstrings: Public functions and modules should have clear docstrings.
• Error Handling: The service layer raises domain-specific exceptions; the API layer translates them into HTTP error responses.
• Import Organization: Follow PEP 8 import ordering.

Configuration

The application reads configuration from ~/.realtimex.ai/Resources/server/.env.development:

• LLM_PROVIDER: openai, realtimexai, or ollama
• OPEN_AI_KEY: OpenAI API key (when using openai provider)
• REALTIMEX_AI_BASE_PATH: Base URL for RealTimeX AI
• REALTIMEX_AI_API_KEY: API key for RealTimeX AI
• OLLAMA_BASE_PATH: Base URL for Ollama

Architecture Decisions

Why This Structure?

1. High Cohesion, Low Coupling: All code for a single feature (API, logic, models) lives in one place, making it self-contained. Modules have minimal dependencies on each other.
2. Scalability: The application grows by adding new, independent modules. This prevents the complexity of the project from growing exponentially.
3. Improved Developer Experience: It's easy to find all code related to a feature. A developer can understand the scope of a feature by looking inside its module directory.
4. Clear Boundaries: The separation between the HTTP layer (api.py) and the business logic layer (service.py) is strictly enforced, improving testability and reusability.
5. Easy to Refactor or Extract: A self-contained feature module is much easier to modify, delete, or extract into its own microservice if needed.

License

Internal RealTimeX project - All rights reserved.