chainix 1.0.3

A client library for executing asynchronous chains on chainix.ai with custom function callbacks

Chainix

A Python client library for executing chains with chainix.ai

Installation

pip install chainix

Quick Start

from chainix import AsyncChainClient

# Initialize the client
client = AsyncChainClient(
    chain_id="your-chains-id-here",
    api_key="your-api-key-here",
)

# Define your custom functions
def refund(inputs):
    try:
        order_id = inputs['order_id']
        print(f"Issuing a refund for order: {order_id}")
    
        # Your business logic here
        # ... process refund ...
        amount = '500'
        
        return {
            'success': True,
            'vars_to_update': {
                'refund_amount': amount
            }
        }
    except Exception as e:
        print(f"Failed to process refund: {e}")
        return {
            'success': False,
            'vars_to_update': {}
        }


def cancel_order(inputs):
    try:
        order_id = inputs['order_id']
        print(f"Cancelling order: {order_id}")
    
        # Your business logic here
        # ... perform cancellation ...
        
        return {
            'success': True,
            'vars_to_update': {}
        }
    except Exception as e:
        print(f"Failed to cancel order: {e}")
        return {
            'success': False,
            'vars_to_update': {}
        }


# Register your functions (use the actual function IDs from the chain on chainix.ai)
client.register_function("your-cancel-function-id", cancel_order)
client.register_function("your-refund-function-id", refund)

# Or, bulk register your functions
functions = {
    "your-cancel-function-id": cancel_order,
    "your-refund-function-id": refund,
}
client.register_functions(functions)

# Execute a chain
# Provide all initial variables needed to start the chain
result = client.run_chain({
    'message_body': 'Hi, please cancel my order',
    'order_id': '33433',
})

print("Chain completed:", result)

Function Requirements

You must register a function corresponding to every function call step in the chain. All registered functions must follow these requirements:

Function Signature

Your functions should accept a single dictionary argument. When the function is called, this dictionary will contain the inputs for that function call step:

def my_function(inputs: dict) -> dict:
    # Your business logic here
    pass

How it works:

1. You define variables in your chain on chainix.ai (e.g., order_id, user_email, action)
2. You create function call steps in your chain and specify which variables should be passed as inputs to each step
3. When the chain reaches a function call step, it stops and calls your registered function via it's id
4. Your function receives a dictionary where each key is a variable you specified as an input for that step, and each value is the current value of that variable in the chain

Example: If you have a function call step with order_id and user_email as inputs, your function will receive a dictionary that looks like this:

{
    'order_id': '12345',
    'user_email': '[e-mail]'
}

Return Value

Your functions must return a dictionary with exactly two keys:

{
    'success': bool,                  # True if function executed successfully, False otherwise
    'vars_to_update': dict[str, str]  # Dictionary of variables to update in the chain (keys and values must be strings, can be empty)
}

Important: Each function must return a dictionary containing these exact two keys. If the return format is incorrect, the chain will fail during client-side execution and won't create any logs to indicate the cause of failure.

Important: The keys in vars_to_update must exactly match the variable names you defined in your chain on chainix.ai. Only variables that exist in your chain can be updated. If you try to update a variable that doesn't exist in your chain, the chain will fail.

Important: The keys and values in vars_to_update must be strings. If a key or value is not a string, the chain will fail.

Example Function Structure

def cancel_order(inputs):
    try:
        # Extract step inputs
        order_id = inputs['order_id']
        customer_upset = inputs['customer_is_upset']

        # Your business logic here
        if customer_upset == 'yes':
            # ... cancellation logic with manager escalation ...
            return {
                'success': True,
                'vars_to_update': {
                    'order_status': 'cancelled',        # Must match variable name in your chain
                    'escalated_to_manager': 'yes'       # Must match variable name in your chain
                }
            }
        elif customer_upset == 'no':
            # ... cancellation logic without manager escalation ...
            return {
                'success': True,
                'vars_to_update': {
                    'order_status': 'cancelled',        # Must match variable name in your chain
                }
            }
        else:
            return {
                'success': False,
                'vars_to_update': {}
            }
    except Exception as e:
        print(f"Error processing order: {e}")
        return {
            'success': False,
            'vars_to_update': {}
        }

Function Registration

You can register functions individually or in bulk:

# Individual registration
client.register_function("function-id-1", my_function)

# Bulk registration
functions = {
    "function-id-1": cancel_order,
    "function-id-2": refund,
    "function-id-3": process_order,
}
client.register_functions(functions)

Configuration

client = AsyncChainClient(
    chain_id="your-chain-id",           # Your unique chain identifier
    api_key="your-api-key",             # Your API key for authentication
    max_wait_time=300,                  # Max wait time in seconds (optional, default 300)
    poll_interval=5,                    # How often to check status in seconds (optional, default 5, minimum 3)
    verbose=True                        # Whether to print status messages (optional, default True)
)

Silent Mode

For production environments or when you don't want status messages, you can disable verbose output:

client = AsyncChainClient(
    chain_id="your-chain-id",
    api_key="your-api-key",
    verbose=False  # Runs silently
)

Running Chains

Basic Usage

To run a chain, call .run_chain() on the client and provide values for all initial variables. Only provide values for initial variables.

result = client.run_chain(
    initial_variables={
        'message_body': 'Hi, please cancel my order',
        'user_email': '[e-mail]',
        'order_id': '12345',
    }
)

Test Mode

You can run chains in test mode for development and debugging:

result = client.run_chain(
    initial_variables={
        'message_body': 'Hi, please cancel my order',
        'user_email': '[e-mail]',
        'order_id': '12345'
    },
    test=True  # Runs in test mode
)

Error Handling

The client automatically handles several types of errors:

• Network errors: Automatically retries with backoff
• Function execution errors: Functions that throw exceptions are treated as failed (success: False)
• Invalid function returns: If functions don't return the required structure, the chain will stop with a clear error message. Note that if your functions encounter errors but don't throw exceptions (e.g., they fail silently), these errors will not be automatically handled and the chain will fail client-side without creating logs.

Best Practices

1. Always wrap the body of your custom function in try-catch blocks, catch any errors and set success to false in the return dictionary
2. Return meaningful error information when functions fail
3. Validate input arguments at the start of your functions
4. Use exact variable names in vars_to_update that match your variables names on the chain configuration on chainix.ai

def robust_function(args):
    try:
        # Validate inputs
        if 'required_field' not in args:
            raise ValueError("Missing required_field")
            
        # Your business logic
        result = perform_business_logic(args)
        
        return {
            'success': True,
            'vars_to_update': {
                'operation_result': result,
                'timestamp': datetime.now().isoformat()
            }
        }
        
    except ValueError as e:
        print(f"Validation error: {e}")
        return {
            'success': False,
            'vars_to_update': {'error_type': 'validation_error'}
        }
    except Exception as e:
        print(f"Unexpected error: {e}")
        return {
            'success': False,
            'vars_to_update': {'error_type': 'unexpected_error'}
        }

Return Values

The run_chain() method returns detailed information about the chain execution. You don't need to handle or use this return value to successfully run chains - it's provided as an optional feature for debugging, monitoring, or accessing final variable values.

The method returns a dictionary with different structures depending on the outcome:

Successful Chain Completion

When a chain completes successfully, it returns a dictionary with the following structure:

{
    'complete': True,      # Chain finished running
    'success': True,       # Chain completed without errors
    'data': {
        'runId': 'unique-run-identifier',
        'initialVars': {
            'variable_name_1': 'initial_value1',
            'variable_name_2': 'initial_value2',
            # ... all variables and their starting values
        },
        'vars': {
            'variable_name_1': 'final_value1',
            'variable_name_2': 'final_value2',
            # ... all variables and their final values
        },
        'log': [
            # Array containing details for each step, in execution order
            {
                'stepType': 'inference',
                'stepTitle': 'Analyze Request',
                'updatedVars': {'request_type': 'cancellation'},
                'res': {'result': {'action': 'cancel'}, 'success': True, ...}
            },
            # ... more steps
        ],
        'timestamp': '2024-01-01T12:00:00Z',
        'errorDiagnosis': '',    # Error details (empty string on success)
        'errorType': '',         # Error type (empty string on success)
    }
}

Example successful result:

result = client.run_chain({
    'message_body': 'Hi, please cancel my order',
    'order_id': '33433',
})

# Result might look like:
{
    'complete': True,
    'success': True,
    'data': {
        'runId': 'run_abc123',
        'initialVars': {
            'message_body': 'Hi, please cancel my order',
            'order_id': '33433'
            'order_status': '',
            'cancellation_date': '',
        },
        'vars': {
            'message_body': 'Hi, please cancel my order',
            'order_id': '33433',
            'order_status': 'cancelled',
            'cancellation_date': '2024-01-01',
        },
        'log': [
            {
                'stepType': 'inference',
                'stepTitle': 'Analyze Message',
                'updatedVars': {'request_type': 'cancellation.'},
                'res': {
                    'result': {'action': 'cancel'},
                    'success': True,
                    'confidence': 0.95,
                    'explanation': 'Customer requested order cancellation'
                }
            },
            {
                'stepType': 'function_call',
                'stepTitle': 'Cancel Order Function',
                'updatedVars': {'order status': 'cancelled', 'cancellation_date': '2024-01-01'},
                'res': {
                    'explanation': 'Order successfully cancelled',
                    'success': True,
                    'updatedVars': {'order status': 'cancelled', 'cancellation_date': '2024-01-01'}
                }
            },
            {
                'stepType': 'endpoint',
                'res': {
                    'explanation': 'Reached endpoint with id of "4c01ea23-di2i-3432-9gkf-38dkf8fcc8e"
                }
            }
        ],
        'timestamp': '2024-01-01T12:00:00Z',
        'errorDiagnosis': '',
        'errorType': ''
    }
}

Failed Chain Completion

When a chain completes but with errors, it returns:

{
    'complete': True,      # Chain finished running
    'success': False,      # Chain completed with errors
    'data': {
        'runId': 'unique-run-identifier',
        'vars': {
            # Variables and their values when the error occurred
        },
        'log': [
            # Steps that were executed before the error
        ],
        'timestamp': '2024-01-01T12:00:00Z',
        'success': False,
        'errorDiagnosis': 'Description of what went wrong',
        'errorType': 'error_category',
        'initialVars': {
            # Starting variable values
        }
    }
}

Error Cases

When errors occur during chain execution, the method returns an empty dictionary {}:

• API errors (invalid API key, chain not found, etc.)
• Network errors that persist after retries
• Client-side errors (connection timeouts, invalid requests)
• Timeout errors when the chain doesn't complete within max_wait_time

Note: When a chain completes but encounters errors during execution, it will still return a structured response with complete: True and success: False, along with error details in errorDiagnosis and errorType. The empty dictionary {} is only returned for client-side or API communication failures.

Server-Side Error Types

When a chain completes with complete: True and success: False, the errorType field will contain one of these specific error categories:

• validation_error: Invalid input provided (HTTP 400 equivalent)

  • Tried to update a variable that doesn't exist in your chain
  • Missing required initial variables
  • Invalid variable names or values

• function_call_error: Your registered function failed during execution

  • Function threw an exception
  • Function returned invalid structure
  • Function returned success: False

• routing_error: Chain execution reached a dead end

  • A step's output doesn't point to any next step
  • Invalid routing configuration in the chain

• circular_error: Infinite loop detected

  • The same step was reached twice during execution
  • Prevents chains from running indefinitely

• low_confidence_error: AI inference didn't meet confidence threshold

  • Model's confidence score was below the required threshold
  • Useful for triggering manual review or alternative workflows

• usage_limit_exceeded_error: API usage limits have been reached

  • Only applies to free accounts that exceed their daily spending limit

• unexpected_error: Internal server error (HTTP 500 equivalent)

  • Catch-all for unexpected system failures
  • Contact support if this occurs frequently

Handling Different Error Types

You can implement different logic based on the error type:

result = client.run_chain(initial_variables)

if result.get('complete') and not result.get('success'):
    error_type = result.get('data', {}).get('errorType')
    error_diagnosis = result.get('data', {}).get('errorDiagnosis')
    
    if error_type == 'low_confidence_error':
        # Model wasn't confident - trigger manual review to update context
        print(f"Model uncertain: {error_diagnosis}")
        send_email_to_chain_maintainers(error_diagnosis)
        queue_for_manual_processing(initial_variables)
    else:
        # Handle other error types as needed
        print(f"Chain failed with {error_type}: {error_diagnosis}")

Understanding the Result Structure

Fields:

• complete: Boolean indicating if the chain finished running (regardless of success/failure)
• success: Boolean indicating if the chain completed without errors
• data.vars: Dictionary of all variables and their final values
• data.runId: Unique identifier for this specific chain execution
• data.log: Array showing each step that was executed and its result (see detailed breakdown below)
• data.initialVars: Dictionary of variables and their starting values (useful for comparison)
• data.errorDiagnosis: Detailed error description (when success: False)
• data.errorType: Category of error that occurred (when success: False)

Understanding the Execution Log

The data.log field contains an array of steps in the order they were executed. Each step is a dictionary with these keys:

• stepType: Type of step executed ('inference', 'function_call', 'endpoint', or 'variable_checker')
• stepTitle: Name/title of the step
• updatedVars: Dictionary of variables that were updated (key = variable name, value = new value)
• res: Result details that vary by step type

Step Types and Results

Inference Steps (stepType: 'inference'): AI model makes decisions or classifications based on data.

{
    'stepType': 'inference',
    'stepTitle': 'Analyze Customer Priority',
    'updatedVars': {'priority_level': 'high'},
    'res': {
        'result': {'priority': 'high'},           # Model's decision outputs
        'success': True,                          # Met confidence threshold
        'threshold': 0.7,                         # Required confidence level
        'confidence': 0.95,                       # Actual confidence score
        'explanation': 'I chose "high" because...',  # Model's reasoning
        'updatedVars': {}                         # Variables updated by model
    }
}

Function Call Steps (stepType: 'function_call'): Executes your registered Python functions.

{
    'stepType': 'function_call',
    'stepTitle': 'Cancel Order Function',
    'updatedVars': {'order_status': 'cancelled'},
    'res': {
        'explanation': 'Function executed successfully',
        'updatedVars': {'order_status': 'cancelled'},  # Variables your function updated
        'success': True                                 # Function execution success
    }
}

Variable Checker Steps (stepType: 'variable_checker'): Checks and validates variable values.

{
    'stepType': 'variable_checker',
    'stepTitle': 'Check Order ID',
    'updatedVars': {},
    'res': {
        'explanation': 'Successfully checked order_id variable',
        'success': True,                          # Check completed successfully
        'result': {'order_id': '12345'}          # Variable name and its value
    }
}

Endpoint Steps (stepType: 'endpoint'): Marks chain completion or stopping points.

{
    'stepType': 'endpoint',
    'stepTitle': 'Chain Complete',
    'updatedVars': {},
    'res': {
        'explanation': 'Reached endpoint with id of "end_success"'
    }
}

Checking Results

Always check the result structure properly:

result = client.run_chain(initial_variables)

if not result:
    print("Chain execution failed - API or network error")
    # Handle client-side error case
elif result.get('complete') and result.get('success'):
    print("Chain completed successfully!")
    final_vars = result['data']['vars']
    print(f"Final variables: {final_vars}")
elif result.get('complete') and not result.get('success'):
    print("Chain completed with errors")
    error_info = result['data']['errorDiagnosis']
    error_type = result['data']['errorType']
    print(f"Error: {error_info} (Type: {error_type})")
else:
    print("Unexpected result format")

Best Practices for Result Handling

1. Always check for empty results first before accessing any data
2. Check both complete and success fields to understand the outcome
3. Use data.vars for final variable values - this is the main output
4. Access nested data safely using .get() method or try/except blocks
5. Enable verbose mode during development to see detailed execution logs

def handle_chain_result(result):
    """Safely handle chain execution results"""
    if not result:
        return {"error": "Chain execution failed - API or network error"}
    
    if not result.get('complete'):
        return {"error": "Chain did not complete"}
    
    if not result.get('success'):
        error_diagnosis = result.get('data', {}).get('errorDiagnosis', 'Unknown error')
        error_type = result.get('data', {}).get('errorType', 'Unknown')
        return {
            "error": f"Chain failed: {error_diagnosis}",
            "error_type": error_type
        }
    
    # Success case
    final_vars = result.get('data', {}).get('vars', {})
    run_id = result.get('data', {}).get('runId')
    
    return {
        "success": True,
        "variables": final_vars,
        "run_id": run_id
    }

# Usage
result = client.run_chain(initial_variables)
processed_result = handle_chain_result(result)

if processed_result.get("success"):
    print("Variables:", processed_result["variables"])
else:
    print("Error:", processed_result["error"])

License

MIT License