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import json
from unittest.mock import patch
import pytest
from workflows.errors import CyclicDependencyError, WorkflowError
from workflows.executors import (
create_processed_inputs,
execute_model_step,
execute_workflow,
lower,
upper,
)
from workflows.structs import InputField, ModelStep, OutputField, Workflow
# Tests for utility functions
def test_upper():
"""Test the upper function with different input types."""
assert upper("hello") == "HELLO"
assert upper("Hello World") == "HELLO WORLD"
assert upper("") == ""
# Non-string inputs should be returned unchanged
assert upper(123) == 123
assert upper([1, 2, 3]) == [1, 2, 3]
assert upper(None) is None
def test_lower():
"""Test the lower function with different input types."""
assert lower("HELLO") == "hello"
assert lower("Hello World") == "hello world"
assert lower("") == ""
# Non-string inputs should be returned unchanged
assert lower(123) == 123
assert lower([1, 2, 3]) == [1, 2, 3]
assert lower(None) is None
# Tests for create_processed_inputs
def test_create_processed_inputs_basic():
"""Test basic input processing without transformations."""
step = ModelStep(
id="test_step",
name="Test Step",
model="gpt-4",
provider="openai",
call_type="llm",
system_prompt="Test prompt",
input_fields=[InputField(name="text", description="Input text", variable="input_text")],
output_fields=[],
)
available_vars = {"input_text": "Hello World"}
result = create_processed_inputs(step, available_vars)
assert result == {"text": "Hello World"}
def test_create_processed_inputs_with_transformation():
"""Test input processing with transformation functions."""
step = ModelStep(
id="test_step",
name="Test Step",
model="gpt-4",
provider="openai",
call_type="llm",
system_prompt="Test prompt",
input_fields=[
InputField(name="upper_text", description="Uppercase text", variable="input_text", func="upper"),
InputField(name="lower_text", description="Lowercase text", variable="input_caps", func="lower"),
],
output_fields=[],
)
available_vars = {"input_text": "hello", "input_caps": "WORLD"}
result = create_processed_inputs(step, available_vars)
assert result == {"upper_text": "HELLO", "lower_text": "world"}
def test_create_processed_inputs_missing_var():
"""Test that appropriate error is raised when a variable is missing."""
step = ModelStep(
id="test_step",
name="Test Step",
model="gpt-4",
provider="openai",
call_type="llm",
system_prompt="Test prompt",
input_fields=[InputField(name="text", description="Input text", variable="missing_var")],
output_fields=[],
)
available_vars = {"input_text": "Hello World"}
with pytest.raises(KeyError):
create_processed_inputs(step, available_vars)
def test_create_processed_inputs_unknown_func():
"""Test that appropriate error is raised when an unknown function is specified."""
step = ModelStep(
id="test_step",
name="Test Step",
model="gpt-4",
provider="openai",
call_type="llm",
system_prompt="Test prompt",
input_fields=[InputField(name="text", description="Input text", variable="input_text", func="unknown_func")],
output_fields=[],
)
available_vars = {"input_text": "Hello World"}
# This should raise an error when the function isn't found
with pytest.raises(Exception):
create_processed_inputs(step, available_vars)
# Tests for execute_model_step
@patch("workflows.executors.completion")
def test_execute_model_step_success(mock_completion):
"""Test successful execution of a model step with mocked litellm response."""
# Mock the litellm response
mock_response = {
"content": json.dumps({"summary": "This is a summary"}),
"output": {"summary": "This is a summary"},
}
mock_completion.return_value = mock_response
# Create a test step
step = ModelStep(
id="summarize",
name="Summarize Text",
model="gpt-3.5-turbo",
provider="OpenAI",
call_type="llm",
system_prompt="Summarize the text",
input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
)
# Execute the step
result = execute_model_step(step, {"input_text": "Long text to be summarized..."})
# Verify the results
assert result == {"summary": "This is a summary"}
# Verify the litellm call was made correctly
mock_completion.assert_called_once()
args, kwargs = mock_completion.call_args
assert kwargs["model"] == "OpenAI/gpt-3.5-turbo"
assert "Summarize the text" in kwargs["system"]
@patch("workflows.executors.completion")
def test_execute_model_step_error(mock_completion):
"""Test handling of errors in model step execution."""
# Make litellm raise an exception
mock_completion.side_effect = Exception("API Error")
# Create a test step
step = ModelStep(
id="summarize",
name="Summarize Text",
model="gpt-3.5-turbo",
provider="openai",
call_type="llm",
system_prompt="Summarize the text",
input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
)
# Execute the step - should raise an exception
with pytest.raises(Exception):
execute_model_step(step, {"input_text": "Long text to be summarized..."})
# Tests for execute_workflow
@patch("workflows.executors.execute_model_step")
def test_execute_workflow_simple(mock_execute_step):
"""Test execution of a simple workflow with a single step."""
# Configure mock to return expected outputs
mock_execute_step.return_value = {"summary": "This is a summary"}
# Create a simple workflow
step = ModelStep(
id="summarize",
name="Summarize Text",
model="gpt-3.5-turbo",
provider="openai",
call_type="llm",
system_prompt="Summarize the text",
input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
)
workflow = Workflow(steps={"summarize": step}, inputs=["input_text"], outputs={"summary": "summarize.summary"})
# Execute the workflow
final_outputs, computed_values, step_contents = execute_workflow(
workflow, {"input_text": "Long text to be summarized..."}
)
# Verify the results
assert final_outputs == {"summary": "This is a summary"}
assert computed_values == {"input_text": "Long text to be summarized...", "summarize.summary": "This is a summary"}
assert step_contents == {}
# Verify execute_model_step was called correctly
mock_execute_step.assert_called_once()
@patch("workflows.executors.execute_model_step")
def test_execute_workflow_multi_step(mock_execute_step):
"""Test execution of a multi-step workflow with dependencies."""
# Configure mock to return different values based on the step
def side_effect(step, available_vars, return_full_content=False):
if step.id == "extract":
return {"entities": ["Apple", "product"]}
elif step.id == "analyze":
return {"sentiment": "positive"}
return {}
mock_execute_step.side_effect = side_effect
# Create extract step
extract_step = ModelStep(
id="extract",
name="Extract Entities",
model="gpt-3.5-turbo",
provider="openai",
call_type="llm",
system_prompt="Extract entities",
input_fields=[InputField(name="text", description="Text to analyze", variable="input_text")],
output_fields=[OutputField(name="entities", description="Extracted entities", type="list[str]")],
)
# Create analyze step that depends on extract step
analyze_step = ModelStep(
id="analyze",
name="Analyze Sentiment",
model="gpt-4",
provider="openai",
call_type="llm",
system_prompt="Analyze sentiment",
input_fields=[InputField(name="entities", description="Entities to analyze", variable="extract.entities")],
output_fields=[OutputField(name="sentiment", description="Sentiment analysis", type="str")],
)
workflow = Workflow(
steps={"extract": extract_step, "analyze": analyze_step},
inputs=["input_text"],
outputs={"entities": "extract.entities", "sentiment": "analyze.sentiment"},
)
# Execute the workflow
final_outputs, computed_values, step_contents = execute_workflow(
workflow, {"input_text": "Apple is launching a new product tomorrow."}
)
# Verify the results
assert final_outputs == {"entities": ["Apple", "product"], "sentiment": "positive"}
assert computed_values == {
"input_text": "Apple is launching a new product tomorrow.",
"extract.entities": ["Apple", "product"],
"analyze.sentiment": "positive",
}
assert step_contents == {}
# Verify execute_model_step was called twice (once for each step)
assert mock_execute_step.call_count == 2
def test_execute_workflow_missing_input():
"""Test that an error is raised when a required input is missing."""
step = ModelStep(
id="summarize",
name="Summarize Text",
model="gpt-3.5-turbo",
provider="openai",
call_type="llm",
system_prompt="Summarize the text",
input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
)
workflow = Workflow(steps={"summarize": step}, inputs=["input_text"], outputs={"summary": "summarize.summary"})
# Execute with missing input
with pytest.raises(WorkflowError, match="Missing required workflow input"):
execute_workflow(workflow, {})
@patch("workflows.executors.create_dependency_graph")
def test_execute_workflow_cyclic_dependency(mock_dependency_graph):
"""Test that a cyclic dependency in the workflow raises an appropriate error."""
# Make create_dependency_graph raise a CyclicDependencyError
mock_dependency_graph.side_effect = CyclicDependencyError()
step = ModelStep(
id="test",
name="Test Step",
model="gpt-3.5-turbo",
provider="openai",
call_type="llm",
system_prompt="Test",
input_fields=[],
output_fields=[],
)
workflow = Workflow(steps=[step], inputs=[], outputs={})
# This should propagate the CyclicDependencyError
with pytest.raises(CyclicDependencyError):
execute_workflow(workflow, {})
@patch("workflows.executors.execute_model_step")
def test_execute_workflow_with_full_content(mock_execute_step):
"""Test execution of a workflow with return_full_content=True."""
# Configure mock to return expected outputs and content
mock_execute_step.return_value = ({"summary": "This is a summary"}, "Full model response content")
# Create a simple workflow
step = ModelStep(
id="summarize",
name="Summarize Text",
model="gpt-3.5-turbo",
provider="openai",
call_type="llm",
system_prompt="Summarize the text",
input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
)
workflow = Workflow(steps=[step], inputs=["input_text"], outputs={"summary": "summarize.summary"})
# Execute the workflow with return_full_content=True
final_outputs, computed_values, step_contents = execute_workflow(
workflow, {"input_text": "Long text to be summarized..."}, return_full_content=True
)
# Verify the results
assert final_outputs == {"summary": "This is a summary"}
assert computed_values == {"input_text": "Long text to be summarized...", "summarize.summary": "This is a summary"}
assert step_contents == {"summarize": "Full model response content"}
# Verify execute_model_step was called correctly with return_full_content=True
mock_execute_step.assert_called_once_with(step, computed_values, return_full_content=True)
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