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adjust tests

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Brandon Hancock
2025-03-04 15:40:28 -05:00
parent 26e6106fe2
commit cee7b11d47
5 changed files with 488 additions and 342 deletions
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68
tests/agent_test.py
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@@ -18,6 +18,7 @@ from crewai.tools.tool_calling import InstructorToolCalling
from crewai.tools.tool_usage import ToolUsage
from crewai.utilities import RPMController
from crewai.utilities.events import crewai_event_bus
from crewai.utilities.events.llm_events import LLMStreamChunkEvent
from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
@@ -259,9 +260,7 @@ def test_cache_hitting():
def handle_tool_end(source, event):
received_events.append(event)
with (
patch.object(CacheHandler, "read") as read,
):
with (patch.object(CacheHandler, "read") as read,):
read.return_value = "0"
task = Task(
description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -1798,3 +1797,66 @@ def test_litellm_anthropic_error_handling():
# Verify the LLM call was only made once (no retries)
mock_llm_call.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_streaming_with_tool_calling():
"""Test that streaming works correctly with tool calling."""
received_chunks = []
tool_called = False
# Set up event listener to capture streaming chunks
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(LLMStreamChunkEvent)
def handle_stream_chunk(source, event):
received_chunks.append(event.chunk)
# Define a tool that will be called
@tool
def calculator(expression: str) -> str:
"""Calculate the result of a mathematical expression."""
nonlocal tool_called
tool_called = True
try:
result = eval(expression)
return f"The result of {expression} is {result}"
except Exception as e:
return f"Error calculating {expression}: {str(e)}"
# Create an agent with streaming enabled
agent = Agent(
role="Math Assistant",
goal="Help users with mathematical calculations",
backstory="I am an AI assistant specialized in mathematics.",
verbose=True,
llm=LLM(model="gpt-4o", stream=True), # Enable streaming
)
# Create a task that will require tool calling
task = Task(
description="Calculate 23 * 45 using the calculator tool.",
agent=agent,
expected_output="The result of the calculation.",
)
# Execute the task with the calculator tool
output = agent.execute_task(task=task, tools=[calculator])
# Verify that streaming chunks were received
assert len(received_chunks) > 0, "No streaming chunks were received"
# Verify that the tool was called
assert tool_called, "Calculator tool was not called"
# Verify that the output contains the correct result
assert "1035" in output, f"Output does not contain the correct result: {output}"
# Verify that the streaming chunks form a coherent response
combined_chunks = "".join(received_chunks)
assert len(combined_chunks) > 0, "Combined chunks are empty"
# Log for debugging
print(f"Received {len(received_chunks)} chunks")
print(f"First few chunks: {received_chunks[:3]}")
print(f"Output: {output}")

420
tests/cassettes/test_agent_streaming_with_tool_calling.yaml Normal file
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89
tests/utilities/test_events.py
View File

@@ -630,7 +630,7 @@ def test_llm_emits_stream_chunk_events():
received_chunks.append(event.chunk)
# Create an LLM with streaming enabled
llm = LLM(model="gpt-3.5-turbo", stream=True)
llm = LLM(model="gpt-4o", stream=True)
# Call the LLM with a simple message
response = llm.call("Tell me a short joke")
@@ -654,7 +654,7 @@ def test_llm_no_stream_chunks_when_streaming_disabled():
received_chunks.append(event.chunk)
# Create an LLM with streaming disabled
llm = LLM(model="gpt-3.5-turbo", stream=False)
llm = LLM(model="gpt-4o", stream=False)
# Call the LLM with a simple message
response = llm.call("Tell me a short joke")
@@ -666,89 +666,6 @@ def test_llm_no_stream_chunks_when_streaming_disabled():
assert response and isinstance(response, str)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_tool_calling_with_streaming():
"""Test that tool calling works correctly with streaming enabled."""
received_chunks = []
tool_called = False
def sample_tool(text: str) -> str:
nonlocal tool_called
tool_called = True
return f"Tool processed: {text}"
available_functions = {"sample_tool": sample_tool}
tools = [
{
"type": "function",
"function": {
"name": "sample_tool",
"description": "A sample tool that processes text",
"parameters": {
"type": "object",
"properties": {
"text": {"type": "string", "description": "The text to process"}
},
"required": ["text"],
},
},
}
]
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(LLMStreamChunkEvent)
def handle_stream_chunk(source, event):
received_chunks.append(event.chunk)
# Create an LLM with streaming enabled
llm = LLM(model="gpt-4", stream=True)
# Store original methods
original_call = llm.call
original_handle_tool_call = llm._handle_tool_call
# Create a mock call method that simulates streaming and tool calling
def mock_call(messages, tools=None, callbacks=None, available_functions=None):
# Emit some chunks first
crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="I'll process "))
crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="that text "))
crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="for you."))
# Call the tool
if available_functions and "sample_tool" in available_functions:
result = available_functions["sample_tool"]("Hello, world!")
return result
return "No tool was called"
# Replace the methods with our mocks
llm.call = mock_call
try:
# Call the LLM with a message that should trigger tool use
response = llm.call(
"Process this text with the sample tool: 'Hello, world!'",
tools=tools,
available_functions=available_functions,
)
# Verify that we received chunks
assert len(received_chunks) == 3
assert "".join(received_chunks) == "I'll process that text for you."
# Verify that the tool was called
assert tool_called
# Verify the response contains the tool's output
assert response == "Tool processed: Hello, world!"
finally:
# Restore the original methods
llm.call = original_call
@pytest.mark.vcr(filter_headers=["authorization"])
def test_streaming_fallback_to_non_streaming():
"""Test that streaming falls back to non-streaming when there's an error."""
@@ -762,7 +679,7 @@ def test_streaming_fallback_to_non_streaming():
received_chunks.append(event.chunk)
# Create an LLM with streaming enabled
llm = LLM(model="gpt-3.5-turbo", stream=True)
llm = LLM(model="gpt-4o", stream=True)
# Store original methods
original_call = llm.call