# Sim Python SDK The official Python SDK for [Sim](https://sim.ai), allowing you to execute workflows programmatically from your Python applications. ## Installation ```bash pip install simstudio-sdk ``` ## Quick Start ```python import os from simstudio import SimStudioClient # Initialize the client client = SimStudioClient( api_key=os.getenv("SIM_API_KEY", "your-api-key-here"), base_url="https://sim.ai" # optional, defaults to https://sim.ai ) # Execute a workflow try: result = client.execute_workflow("workflow-id") print("Workflow executed successfully:", result) except Exception as error: print("Workflow execution failed:", error) ``` ## API Reference ### SimStudioClient #### Constructor ```python SimStudioClient(api_key: str, base_url: str = "https://sim.ai") ``` - `api_key` (str): Your Sim API key - `base_url` (str, optional): Base URL for the Sim API (defaults to `https://sim.ai`) #### Methods ##### execute_workflow(workflow_id, input_data=None, timeout=30.0) Execute a workflow with optional input data. ```python result = client.execute_workflow( "workflow-id", input_data={"message": "Hello, world!"}, timeout=30.0 # 30 seconds ) ``` **Parameters:** - `workflow_id` (str): The ID of the workflow to execute - `input_data` (dict, optional): Input data to pass to the workflow. File objects are automatically converted to base64. - `timeout` (float): Timeout in seconds (default: 30.0) **Returns:** `WorkflowExecutionResult` ##### get_workflow_status(workflow_id) Get the status of a workflow (deployment status, etc.). ```python status = client.get_workflow_status("workflow-id") print("Is deployed:", status.is_deployed) ``` **Parameters:** - `workflow_id` (str): The ID of the workflow **Returns:** `WorkflowStatus` ##### validate_workflow(workflow_id) Validate that a workflow is ready for execution. ```python is_ready = client.validate_workflow("workflow-id") if is_ready: # Workflow is deployed and ready pass ``` **Parameters:** - `workflow_id` (str): The ID of the workflow **Returns:** `bool` ##### execute_workflow_sync(workflow_id, input_data=None, timeout=30.0) Execute a workflow and poll for completion (useful for long-running workflows). ```python result = client.execute_workflow_sync( "workflow-id", input_data={"data": "some input"}, timeout=60.0 ) ``` **Parameters:** - `workflow_id` (str): The ID of the workflow to execute - `input_data` (dict, optional): Input data to pass to the workflow - `timeout` (float): Timeout for the initial request in seconds **Returns:** `WorkflowExecutionResult` ##### set_api_key(api_key) Update the API key. ```python client.set_api_key("new-api-key") ``` ##### set_base_url(base_url) Update the base URL. ```python client.set_base_url("https://my-custom-domain.com") ``` ##### close() Close the underlying HTTP session. ```python client.close() ``` ## Data Classes ### WorkflowExecutionResult ```python @dataclass class WorkflowExecutionResult: success: bool output: Optional[Any] = None error: Optional[str] = None logs: Optional[list] = None metadata: Optional[Dict[str, Any]] = None trace_spans: Optional[list] = None total_duration: Optional[float] = None ``` ### WorkflowStatus ```python @dataclass class WorkflowStatus: is_deployed: bool deployed_at: Optional[str] = None needs_redeployment: bool = False ``` ### SimStudioError ```python class SimStudioError(Exception): def __init__(self, message: str, code: Optional[str] = None, status: Optional[int] = None): super().__init__(message) self.code = code self.status = status ``` ## Examples ### Basic Workflow Execution ```python import os from simstudio import SimStudioClient client = SimStudioClient(api_key=os.getenv("SIM_API_KEY")) def run_workflow(): try: # Check if workflow is ready is_ready = client.validate_workflow("my-workflow-id") if not is_ready: raise Exception("Workflow is not deployed or ready") # Execute the workflow result = client.execute_workflow( "my-workflow-id", input_data={ "message": "Process this data", "user_id": "12345" } ) if result.success: print("Output:", result.output) print("Duration:", result.metadata.get("duration") if result.metadata else None) else: print("Workflow failed:", result.error) except Exception as error: print("Error:", error) run_workflow() ``` ### Error Handling ```python from simstudio import SimStudioClient, SimStudioError import os client = SimStudioClient(api_key=os.getenv("SIM_API_KEY")) def execute_with_error_handling(): try: result = client.execute_workflow("workflow-id") return result except SimStudioError as error: if error.code == "UNAUTHORIZED": print("Invalid API key") elif error.code == "TIMEOUT": print("Workflow execution timed out") elif error.code == "USAGE_LIMIT_EXCEEDED": print("Usage limit exceeded") elif error.code == "INVALID_JSON": print("Invalid JSON in request body") else: print(f"Workflow error: {error}") raise except Exception as error: print(f"Unexpected error: {error}") raise ``` ### Context Manager Usage ```python from simstudio import SimStudioClient import os # Using context manager to automatically close the session with SimStudioClient(api_key=os.getenv("SIM_API_KEY")) as client: result = client.execute_workflow("workflow-id") print("Result:", result) # Session is automatically closed here ``` ### Environment Configuration ```python import os from simstudio import SimStudioClient # Using environment variables client = SimStudioClient( api_key=os.getenv("SIM_API_KEY"), base_url=os.getenv("SIM_BASE_URL", "https://sim.ai") ) ``` ### File Upload File objects are automatically detected and converted to base64 format. Include them in your input under the field name matching your workflow's API trigger input format: The SDK converts file objects to this format: ```python { 'type': 'file', 'data': 'data:mime/type;base64,base64data', 'name': 'filename', 'mime': 'mime/type' } ``` Alternatively, you can manually provide files using the URL format: ```python { 'type': 'url', 'data': 'https://example.com/file.pdf', 'name': 'file.pdf', 'mime': 'application/pdf' } ``` ```python from simstudio import SimStudioClient import os client = SimStudioClient(api_key=os.getenv("SIM_API_KEY")) # Upload a single file - include it under the field name from your API trigger with open('document.pdf', 'rb') as f: result = client.execute_workflow( 'workflow-id', input_data={ 'documents': [f], # Must match your workflow's "files" field name 'instructions': 'Analyze this document' } ) # Upload multiple files with open('doc1.pdf', 'rb') as f1, open('doc2.pdf', 'rb') as f2: result = client.execute_workflow( 'workflow-id', input_data={ 'attachments': [f1, f2], # Must match your workflow's "files" field name 'query': 'Compare these documents' } ) ``` ### Batch Workflow Execution ```python from simstudio import SimStudioClient import os client = SimStudioClient(api_key=os.getenv("SIM_API_KEY")) def execute_workflows_batch(workflow_data_pairs): """Execute multiple workflows with different input data.""" results = [] for workflow_id, input_data in workflow_data_pairs: try: # Validate workflow before execution if not client.validate_workflow(workflow_id): print(f"Skipping {workflow_id}: not deployed") continue result = client.execute_workflow(workflow_id, input_data) results.append({ "workflow_id": workflow_id, "success": result.success, "output": result.output, "error": result.error }) except Exception as error: results.append({ "workflow_id": workflow_id, "success": False, "error": str(error) }) return results # Example usage workflows = [ ("workflow-1", {"type": "analysis", "data": "sample1"}), ("workflow-2", {"type": "processing", "data": "sample2"}), ] results = execute_workflows_batch(workflows) for result in results: print(f"Workflow {result['workflow_id']}: {'Success' if result['success'] else 'Failed'}") ``` ## Getting Your API Key 1. Log in to your [Sim](https://sim.ai) account 2. Navigate to your workflow 3. Click on "Deploy" to deploy your workflow 4. Select or create an API key during the deployment process 5. Copy the API key to use in your application ## Development ### Running Tests To run the tests locally: 1. Clone the repository and navigate to the Python SDK directory: ```bash cd packages/python-sdk ``` 2. Create and activate a virtual environment: ```bash python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install the package in development mode with test dependencies: ```bash pip install -e ".[dev]" ``` 4. Run the tests: ```bash pytest tests/ -v ``` ### Code Quality Run code quality checks: ```bash # Code formatting black simstudio/ # Linting flake8 simstudio/ --max-line-length=100 # Type checking mypy simstudio/ # Import sorting isort simstudio/ ``` ## Requirements - Python 3.8+ - requests >= 2.25.0 ## License Apache-2.0