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feat: unblock LLM(stream=True) to work with tools
This commit is contained in:
Lucas Gomide
@@ -4,9 +4,12 @@ import os
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import sys
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import threading
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import warnings
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from collections import defaultdict
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from contextlib import contextmanager
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from types import SimpleNamespace
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from typing import (
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Any,
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DefaultDict,
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Dict,
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List,
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Literal,
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@@ -18,7 +21,8 @@ from typing import (
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)
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from dotenv import load_dotenv
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from pydantic import BaseModel
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from litellm.types.utils import ChatCompletionDeltaToolCall
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from pydantic import BaseModel, Field
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from crewai.utilities.events.llm_events import (
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LLMCallCompletedEvent,
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@@ -219,6 +223,15 @@ class StreamingChoices(TypedDict):
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finish_reason: Optional[str]
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class FunctionArgs(BaseModel):
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name: str = ""
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arguments: str = ""
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class AccumulatedToolArgs(BaseModel):
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function: FunctionArgs = Field(default_factory=FunctionArgs)
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class LLM(BaseLLM):
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def __init__(
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self,
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@@ -371,6 +384,11 @@ class LLM(BaseLLM):
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last_chunk = None
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chunk_count = 0
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usage_info = None
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tool_calls = None
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accumulated_tool_args: DefaultDict[int, AccumulatedToolArgs] = defaultdict(
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AccumulatedToolArgs
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)
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# --- 2) Make sure stream is set to True and include usage metrics
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params["stream"] = True
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@@ -428,6 +446,20 @@ class LLM(BaseLLM):
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if chunk_content is None and isinstance(delta, dict):
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# Some models might send empty content chunks
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chunk_content = ""
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# Enable tool calls using streaming
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if "tool_calls" in delta:
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tool_calls = delta["tool_calls"]
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if tool_calls:
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result = self._handle_streaming_tool_calls(
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tool_calls=tool_calls,
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accumulated_tool_args=accumulated_tool_args,
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available_functions=available_functions,
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)
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if result is not None:
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chunk_content = result
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except Exception as e:
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logging.debug(f"Error extracting content from chunk: {e}")
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logging.debug(f"Chunk format: {type(chunk)}, content: {chunk}")
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@@ -442,7 +474,6 @@ class LLM(BaseLLM):
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self,
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event=LLMStreamChunkEvent(chunk=chunk_content),
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)
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# --- 4) Fallback to non-streaming if no content received
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if not full_response.strip() and chunk_count == 0:
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logging.warning(
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@@ -501,7 +532,7 @@ class LLM(BaseLLM):
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)
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# --- 6) If still empty, raise an error instead of using a default response
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if not full_response.strip():
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if not full_response.strip() and len(accumulated_tool_args) == 0:
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raise Exception(
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"No content received from streaming response. Received empty chunks or failed to extract content."
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)
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@@ -533,8 +564,8 @@ class LLM(BaseLLM):
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tool_calls = getattr(message, "tool_calls")
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except Exception as e:
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logging.debug(f"Error checking for tool calls: {e}")
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# --- 8) If no tool calls or no available functions, return the text response directly
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if not tool_calls or not available_functions:
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# Log token usage if available in streaming mode
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self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
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@@ -568,6 +599,47 @@ class LLM(BaseLLM):
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)
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raise Exception(f"Failed to get streaming response: {str(e)}")
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def _handle_streaming_tool_calls(
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self,
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tool_calls: List[ChatCompletionDeltaToolCall],
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accumulated_tool_args: DefaultDict[int, AccumulatedToolArgs],
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available_functions: Optional[Dict[str, Any]] = None,
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) -> None | str:
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for tool_call in tool_calls:
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current_tool_accumulator = accumulated_tool_args[tool_call.index]
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if tool_call.function.name:
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current_tool_accumulator.function.name = tool_call.function.name
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if tool_call.function.arguments:
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current_tool_accumulator.function.arguments += (
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tool_call.function.arguments
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)
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crewai_event_bus.emit(
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self,
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event=LLMStreamChunkEvent(
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tool_call=tool_call.to_dict(),
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chunk=tool_call.function.arguments,
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),
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)
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if (
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current_tool_accumulator.function.name
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and current_tool_accumulator.function.arguments
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and available_functions
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):
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try:
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json.loads(current_tool_accumulator.function.arguments)
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return self._handle_tool_call(
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[current_tool_accumulator],
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available_functions,
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)
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except json.JSONDecodeError:
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continue
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return None
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def _handle_streaming_callbacks(
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self,
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callbacks: Optional[List[Any]],
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@@ -1,6 +1,8 @@
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from enum import Enum
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from typing import Any, Dict, List, Optional, Union
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from pydantic import BaseModel
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from crewai.utilities.events.base_events import BaseEvent
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@@ -41,8 +43,21 @@ class LLMCallFailedEvent(BaseEvent):
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type: str = "llm_call_failed"
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class FunctionCall(BaseModel):
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arguments: str
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name: Optional[str] = None
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class ToolCall(BaseModel):
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id: Optional[str] = None
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function: FunctionCall
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type: Optional[str] = None
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index: int
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class LLMStreamChunkEvent(BaseEvent):
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"""Event emitted when a streaming chunk is received"""
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type: str = "llm_stream_chunk"
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chunk: str
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tool_call: Optional[ToolCall] = None
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133
tests/cassettes/test_handle_streaming_tool_calls.yaml
Normal file
133
tests/cassettes/test_handle_streaming_tool_calls.yaml
Normal file
@@ -0,0 +1,133 @@
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"Get the current weather in a given location", "parameters": {"type": "object",
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'
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CF-RAY:
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- 931461c25bb47df9-GRU
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Connection:
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- keep-alive
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Content-Type:
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Date:
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- Wed, 16 Apr 2025 14:30:40 GMT
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- h3=":443"; ma=86400
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cf-cache-status:
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openai-organization:
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- crewai-iuxna1
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openai-processing-ms:
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openai-version:
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status:
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code: 200
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message: OK
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version: 1
|
||||
@@ -7,8 +7,10 @@ from pydantic import BaseModel
|
||||
|
||||
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
|
||||
from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM
|
||||
from crewai.utilities.events import crewai_event_bus
|
||||
from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
|
||||
from crewai.utilities.events import (
|
||||
LLMCallCompletedEvent,
|
||||
LLMStreamChunkEvent,
|
||||
)
|
||||
from crewai.utilities.token_counter_callback import TokenCalcHandler
|
||||
|
||||
|
||||
@@ -304,6 +306,27 @@ def test_context_window_validation():
|
||||
assert "must be between 1024 and 2097152" in str(excinfo.value)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def get_weather_tool_schema():
|
||||
return {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
}
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@pytest.fixture
|
||||
def anthropic_llm():
|
||||
@@ -395,3 +418,117 @@ def test_deepseek_r1_with_open_router():
|
||||
result = llm.call("What is the capital of France?")
|
||||
assert isinstance(result, str)
|
||||
assert "Paris" in result
|
||||
|
||||
|
||||
def assert_event_count(
|
||||
mock_emit,
|
||||
expected_completed_tool_call: int = 0,
|
||||
expected_stream_chunk: int = 0,
|
||||
expected_completed_llm_call: int = 0,
|
||||
expected_final_chunk_result: str = "",
|
||||
):
|
||||
event_count = {
|
||||
"completed_tool_call": 0,
|
||||
"stream_chunk": 0,
|
||||
"completed_llm_call": 0,
|
||||
}
|
||||
final_chunk_result = ""
|
||||
for _call in mock_emit.call_args_list:
|
||||
event = _call[1]["event"]
|
||||
|
||||
if (
|
||||
isinstance(event, LLMCallCompletedEvent)
|
||||
and event.call_type.value == "tool_call"
|
||||
):
|
||||
event_count["completed_tool_call"] += 1
|
||||
elif isinstance(event, LLMStreamChunkEvent):
|
||||
event_count["stream_chunk"] += 1
|
||||
final_chunk_result += event.chunk
|
||||
elif (
|
||||
isinstance(event, LLMCallCompletedEvent)
|
||||
and event.call_type.value == "llm_call"
|
||||
):
|
||||
event_count["completed_llm_call"] += 1
|
||||
else:
|
||||
continue
|
||||
|
||||
assert event_count["completed_tool_call"] == expected_completed_tool_call
|
||||
assert event_count["stream_chunk"] == expected_stream_chunk
|
||||
assert event_count["completed_llm_call"] == expected_completed_llm_call
|
||||
assert final_chunk_result == expected_final_chunk_result
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_emit() -> MagicMock:
|
||||
from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus
|
||||
|
||||
with patch.object(CrewAIEventsBus, "emit") as mock_emit:
|
||||
yield mock_emit
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_handle_streaming_tool_calls(get_weather_tool_schema, mock_emit):
|
||||
llm = LLM(model="openai/gpt-4o", stream=True)
|
||||
response = llm.call(
|
||||
messages=[
|
||||
{"role": "user", "content": "What is the weather in New York?"},
|
||||
],
|
||||
tools=[get_weather_tool_schema],
|
||||
available_functions={
|
||||
"get_weather": lambda location: f"The weather in {location} is sunny"
|
||||
},
|
||||
)
|
||||
assert response == "The weather in New York, NY is sunny"
|
||||
|
||||
expected_final_chunk_result = (
|
||||
'{"location":"New York, NY"}The weather in New York, NY is sunny'
|
||||
)
|
||||
assert_event_count(
|
||||
mock_emit=mock_emit,
|
||||
expected_completed_tool_call=1,
|
||||
expected_stream_chunk=10,
|
||||
expected_completed_llm_call=1,
|
||||
expected_final_chunk_result=expected_final_chunk_result,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_handle_streaming_tool_calls_no_available_functions(
|
||||
get_weather_tool_schema, mock_emit
|
||||
):
|
||||
llm = LLM(model="openai/gpt-4o", stream=True)
|
||||
response = llm.call(
|
||||
messages=[
|
||||
{"role": "user", "content": "What is the weather in New York?"},
|
||||
],
|
||||
tools=[get_weather_tool_schema],
|
||||
)
|
||||
assert response == ""
|
||||
|
||||
assert_event_count(
|
||||
mock_emit=mock_emit,
|
||||
expected_stream_chunk=9,
|
||||
expected_completed_llm_call=1,
|
||||
expected_final_chunk_result='{"location":"New York, NY"}',
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_handle_streaming_tool_calls_no_tools(mock_emit):
|
||||
llm = LLM(model="openai/gpt-4o", stream=True)
|
||||
response = llm.call(
|
||||
messages=[
|
||||
{"role": "user", "content": "What is the weather in New York?"},
|
||||
],
|
||||
)
|
||||
assert (
|
||||
response
|
||||
== "I'm unable to provide real-time information or current weather updates. For the latest weather information in New York, I recommend checking a reliable weather website or app, such as the National Weather Service, Weather.com, or a similar service."
|
||||
)
|
||||
|
||||
assert_event_count(
|
||||
mock_emit=mock_emit,
|
||||
expected_stream_chunk=46,
|
||||
expected_completed_llm_call=1,
|
||||
expected_final_chunk_result=response,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user