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feat: enhance GeminiCompletion class with client parameter support (#3717)

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* feat: enhance GeminiCompletion class with client parameter support

- Added support for client_params in the GeminiCompletion class to allow for additional client configuration.
- Refactored client initialization into a dedicated method for improved parameter handling.
- Introduced a new method to retrieve client parameters, ensuring compatibility with the base class.
- Enhanced error handling during client initialization to provide clearer messages for missing configuration.
- Updated documentation to reflect the changes in client parameter usage.

* add optional dependancies

* refactor: update test fixture to mock GOOGLE_API_KEY

- Renamed the fixture from `mock_anthropic_api_key` to `mock_google_api_key` to reflect the change in the environment variable being mocked.
- This update ensures that all tests in the module can run with a mocked GOOGLE_API_KEY, improving test isolation and reliability.

* fix tests
This commit is contained in:
Lorenze Jay
2025-10-16 11:42:46 -07:00
committed by GitHub
parent 06a45b29db
commit 02d7ce7621
4 changed files with 807 additions and 75 deletions
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3
lib/crewai/pyproject.toml
View File

@@ -87,6 +87,9 @@ litellm = [
boto3 = [
"boto3>=1.40.45",
]
google-genai = [
"google-genai>=1.2.0",
]
[project.scripts]

107
lib/crewai/src/crewai/llms/providers/gemini/completion.py
View File

@@ -11,9 +11,9 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
try:
from google import genai # type: ignore
from google.genai import types # type: ignore
from google.genai.errors import APIError # type: ignore
from google import genai
from google.genai import types
from google.genai.errors import APIError
except ImportError:
raise ImportError(
"Google Gen AI native provider not available, to install: `uv add google-genai`"
@@ -40,6 +40,7 @@ class GeminiCompletion(BaseLLM):
stop_sequences: list[str] | None = None,
stream: bool = False,
safety_settings: dict[str, Any] | None = None,
client_params: dict[str, Any] | None = None,
**kwargs,
):
"""Initialize Google Gemini chat completion client.
@@ -56,35 +57,27 @@ class GeminiCompletion(BaseLLM):
stop_sequences: Stop sequences
stream: Enable streaming responses
safety_settings: Safety filter settings
client_params: Additional parameters to pass to the Google Gen AI Client constructor.
Supports parameters like http_options, credentials, debug_config, etc.
**kwargs: Additional parameters
"""
super().__init__(
model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
)
# Get API configuration
# Store client params for later use
self.client_params = client_params or {}
# Get API configuration with environment variable fallbacks
self.api_key = (
api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
)
self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
# Initialize client based on available configuration
if self.project:
# Use Vertex AI
self.client = genai.Client(
vertexai=True,
project=self.project,
location=self.location,
)
elif self.api_key:
# Use Gemini Developer API
self.client = genai.Client(api_key=self.api_key)
else:
raise ValueError(
"Either GOOGLE_API_KEY/GEMINI_API_KEY (for Gemini API) or "
"GOOGLE_CLOUD_PROJECT (for Vertex AI) must be set"
)
use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
self.client = self._initialize_client(use_vertexai)
# Store completion parameters
self.top_p = top_p
@@ -99,6 +92,78 @@ class GeminiCompletion(BaseLLM):
self.is_gemini_1_5 = "gemini-1.5" in model.lower()
self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:
"""Initialize the Google Gen AI client with proper parameter handling.
Args:
use_vertexai: Whether to use Vertex AI (from environment variable)
Returns:
Initialized Google Gen AI Client
"""
client_params = {}
if self.client_params:
client_params.update(self.client_params)
if use_vertexai or self.project:
client_params.update(
{
"vertexai": True,
"project": self.project,
"location": self.location,
}
)
client_params.pop("api_key", None)
elif self.api_key:
client_params["api_key"] = self.api_key
client_params.pop("vertexai", None)
client_params.pop("project", None)
client_params.pop("location", None)
else:
try:
return genai.Client(**client_params)
except Exception as e:
raise ValueError(
"Either GOOGLE_API_KEY/GEMINI_API_KEY (for Gemini API) or "
"GOOGLE_CLOUD_PROJECT (for Vertex AI) must be set"
) from e
return genai.Client(**client_params)
def _get_client_params(self) -> dict[str, Any]:
"""Get client parameters for compatibility with base class.
Note: This method is kept for compatibility but the Google Gen AI SDK
uses a different initialization pattern via the Client constructor.
"""
params = {}
if (
hasattr(self, "client")
and hasattr(self.client, "vertexai")
and self.client.vertexai
):
# Vertex AI configuration
params.update(
{
"vertexai": True,
"project": self.project,
"location": self.location,
}
)
elif self.api_key:
params["api_key"] = self.api_key
if self.client_params:
params.update(self.client_params)
return params
def call(
self,
messages: str | list[dict[str, str]],
@@ -427,7 +492,7 @@ class GeminiCompletion(BaseLLM):
def supports_stop_words(self) -> bool:
"""Check if the model supports stop words."""
return self._supports_stop_words_implementation()
return True
def get_context_window_size(self) -> int:
"""Get the context window size for the model."""

645
lib/crewai/tests/llms/google/test_google.py Normal file
View File

@@ -0,0 +1,645 @@
import os
import sys
import types
from unittest.mock import patch, MagicMock
import pytest
from crewai.llm import LLM
from crewai.crew import Crew
from crewai.agent import Agent
from crewai.task import Task
@pytest.fixture(autouse=True)
def mock_google_api_key():
"""Automatically mock GOOGLE_API_KEY for all tests in this module."""
with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
yield
def test_gemini_completion_is_used_when_google_provider():
"""
Test that GeminiCompletion from completion.py is used when LLM uses provider 'google'
"""
llm = LLM(model="google/gemini-2.0-flash-001")
assert llm.__class__.__name__ == "GeminiCompletion"
assert llm.provider == "google"
assert llm.model == "gemini-2.0-flash-001"
def test_gemini_completion_is_used_when_gemini_provider():
"""
Test that GeminiCompletion is used when provider is 'gemini'
"""
llm = LLM(model="gemini/gemini-2.0-flash-001")
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion)
assert llm.provider == "gemini"
assert llm.model == "gemini-2.0-flash-001"
def test_gemini_tool_use_conversation_flow():
"""
Test that the Gemini completion properly handles tool use conversation flow
"""
from unittest.mock import Mock, patch
from crewai.llms.providers.gemini.completion import GeminiCompletion
# Create GeminiCompletion instance
completion = GeminiCompletion(model="gemini-2.0-flash-001")
# Mock tool function
def mock_weather_tool(location: str) -> str:
return f"The weather in {location} is sunny and 75°F"
available_functions = {"get_weather": mock_weather_tool}
# Mock the Google Gemini client responses
with patch.object(completion.client.models, 'generate_content') as mock_generate:
# Mock function call in response
mock_function_call = Mock()
mock_function_call.name = "get_weather"
mock_function_call.args = {"location": "San Francisco"}
mock_part = Mock()
mock_part.function_call = mock_function_call
mock_content = Mock()
mock_content.parts = [mock_part]
mock_candidate = Mock()
mock_candidate.content = mock_content
mock_response = Mock()
mock_response.candidates = [mock_candidate]
mock_response.text = "Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature."
mock_response.usage_metadata = Mock()
mock_response.usage_metadata.prompt_token_count = 100
mock_response.usage_metadata.candidates_token_count = 50
mock_response.usage_metadata.total_token_count = 150
mock_generate.return_value = mock_response
# Test the call
messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
result = completion.call(
messages=messages,
available_functions=available_functions
)
# Verify the tool was executed and returned the result
assert result == "The weather in San Francisco is sunny and 75°F"
# Verify that the API was called
assert mock_generate.called
def test_gemini_completion_module_is_imported():
"""
Test that the completion module is properly imported when using Google provider
"""
module_name = "crewai.llms.providers.gemini.completion"
# Remove module from cache if it exists
if module_name in sys.modules:
del sys.modules[module_name]
# Create LLM instance - this should trigger the import
LLM(model="google/gemini-2.0-flash-001")
# Verify the module was imported
assert module_name in sys.modules
completion_mod = sys.modules[module_name]
assert isinstance(completion_mod, types.ModuleType)
# Verify the class exists in the module
assert hasattr(completion_mod, 'GeminiCompletion')
def test_fallback_to_litellm_when_native_gemini_fails():
"""
Test that LLM falls back to LiteLLM when native Gemini completion fails
"""
# Mock the _get_native_provider to return a failing class
with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider:
class FailingCompletion:
def __init__(self, *args, **kwargs):
raise Exception("Native Google Gen AI SDK failed")
mock_get_provider.return_value = FailingCompletion
# This should fall back to LiteLLM
llm = LLM(model="google/gemini-2.0-flash-001")
# Check that it's using LiteLLM
assert hasattr(llm, 'is_litellm')
assert llm.is_litellm == True
def test_gemini_completion_initialization_parameters():
"""
Test that GeminiCompletion is initialized with correct parameters
"""
llm = LLM(
model="google/gemini-2.0-flash-001",
temperature=0.7,
max_output_tokens=2000,
top_p=0.9,
top_k=40,
api_key="test-key"
)
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion)
assert llm.model == "gemini-2.0-flash-001"
assert llm.temperature == 0.7
assert llm.max_output_tokens == 2000
assert llm.top_p == 0.9
assert llm.top_k == 40
def test_gemini_specific_parameters():
"""
Test Gemini-specific parameters like stop_sequences, streaming, and safety settings
"""
safety_settings = {
"HARM_CATEGORY_HARASSMENT": "BLOCK_MEDIUM_AND_ABOVE",
"HARM_CATEGORY_HATE_SPEECH": "BLOCK_MEDIUM_AND_ABOVE"
}
llm = LLM(
model="google/gemini-2.0-flash-001",
stop_sequences=["Human:", "Assistant:"],
stream=True,
safety_settings=safety_settings,
project="test-project",
location="us-central1"
)
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion)
assert llm.stop_sequences == ["Human:", "Assistant:"]
assert llm.stream == True
assert llm.safety_settings == safety_settings
assert llm.project == "test-project"
assert llm.location == "us-central1"
def test_gemini_completion_call():
"""
Test that GeminiCompletion call method works
"""
llm = LLM(model="google/gemini-2.0-flash-001")
# Mock the call method on the instance
with patch.object(llm, 'call', return_value="Hello! I'm Gemini, ready to help.") as mock_call:
result = llm.call("Hello, how are you?")
assert result == "Hello! I'm Gemini, ready to help."
mock_call.assert_called_once_with("Hello, how are you?")
def test_gemini_completion_called_during_crew_execution():
"""
Test that GeminiCompletion.call is actually invoked when running a crew
"""
# Create the LLM instance first
gemini_llm = LLM(model="google/gemini-2.0-flash-001")
# Mock the call method on the specific instance
with patch.object(gemini_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call:
# Create agent with explicit LLM configuration
agent = Agent(
role="Research Assistant",
goal="Find population info",
backstory="You research populations.",
llm=gemini_llm,
)
task = Task(
description="Find Tokyo population",
expected_output="Population number",
agent=agent,
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
# Verify mock was called
assert mock_call.called
assert "14 million" in str(result)
def test_gemini_completion_call_arguments():
"""
Test that GeminiCompletion.call is invoked with correct arguments
"""
# Create LLM instance first
gemini_llm = LLM(model="google/gemini-2.0-flash-001")
# Mock the instance method
with patch.object(gemini_llm, 'call') as mock_call:
mock_call.return_value = "Task completed successfully."
agent = Agent(
role="Test Agent",
goal="Complete a simple task",
backstory="You are a test agent.",
llm=gemini_llm # Use same instance
)
task = Task(
description="Say hello world",
expected_output="Hello world",
agent=agent,
)
crew = Crew(agents=[agent], tasks=[task])
crew.kickoff()
# Verify call was made
assert mock_call.called
# Check the arguments passed to the call method
call_args = mock_call.call_args
assert call_args is not None
# The first argument should be the messages
messages = call_args[0][0] # First positional argument
assert isinstance(messages, (str, list))
# Verify that the task description appears in the messages
if isinstance(messages, str):
assert "hello world" in messages.lower()
elif isinstance(messages, list):
message_content = str(messages).lower()
assert "hello world" in message_content
def test_multiple_gemini_calls_in_crew():
"""
Test that GeminiCompletion.call is invoked multiple times for multiple tasks
"""
# Create LLM instance first
gemini_llm = LLM(model="google/gemini-2.0-flash-001")
# Mock the instance method
with patch.object(gemini_llm, 'call') as mock_call:
mock_call.return_value = "Task completed."
agent = Agent(
role="Multi-task Agent",
goal="Complete multiple tasks",
backstory="You can handle multiple tasks.",
llm=gemini_llm # Use same instance
)
task1 = Task(
description="First task",
expected_output="First result",
agent=agent,
)
task2 = Task(
description="Second task",
expected_output="Second result",
agent=agent,
)
crew = Crew(
agents=[agent],
tasks=[task1, task2]
)
crew.kickoff()
# Verify multiple calls were made
assert mock_call.call_count >= 2 # At least one call per task
# Verify each call had proper arguments
for call in mock_call.call_args_list:
assert len(call[0]) > 0 # Has positional arguments
messages = call[0][0]
assert messages is not None
def test_gemini_completion_with_tools():
"""
Test that GeminiCompletion.call is invoked with tools when agent has tools
"""
from crewai.tools import tool
@tool
def sample_tool(query: str) -> str:
"""A sample tool for testing"""
return f"Tool result for: {query}"
# Create LLM instance first
gemini_llm = LLM(model="google/gemini-2.0-flash-001")
# Mock the instance method
with patch.object(gemini_llm, 'call') as mock_call:
mock_call.return_value = "Task completed with tools."
agent = Agent(
role="Tool User",
goal="Use tools to complete tasks",
backstory="You can use tools.",
llm=gemini_llm, # Use same instance
tools=[sample_tool]
)
task = Task(
description="Use the sample tool",
expected_output="Tool usage result",
agent=agent,
)
crew = Crew(agents=[agent], tasks=[task])
crew.kickoff()
assert mock_call.called
call_args = mock_call.call_args
call_kwargs = call_args[1] if len(call_args) > 1 else {}
if 'tools' in call_kwargs:
assert call_kwargs['tools'] is not None
assert len(call_kwargs['tools']) > 0
def test_gemini_raises_error_when_model_not_supported():
"""Test that GeminiCompletion raises ValueError when model not supported"""
# Mock the Google client to raise an error
with patch('crewai.llms.providers.gemini.completion.genai') as mock_genai:
mock_client = MagicMock()
mock_genai.Client.return_value = mock_client
from google.genai.errors import ClientError # type: ignore
mock_response = MagicMock()
mock_response.body_segments = [{
'error': {
'code': 404,
'message': 'models/model-doesnt-exist is not found for API version v1beta, or is not supported for generateContent.',
'status': 'NOT_FOUND'
}
}]
mock_response.status_code = 404
mock_client.models.generate_content.side_effect = ClientError(404, mock_response)
llm = LLM(model="google/model-doesnt-exist")
with pytest.raises(Exception): # Should raise some error for unsupported model
llm.call("Hello")
def test_gemini_vertex_ai_setup():
"""
Test that Vertex AI configuration is properly handled
"""
with patch.dict(os.environ, {
"GOOGLE_CLOUD_PROJECT": "test-project",
"GOOGLE_CLOUD_LOCATION": "us-west1"
}):
llm = LLM(
model="google/gemini-2.0-flash-001",
project="test-project",
location="us-west1"
)
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion)
assert llm.project == "test-project"
assert llm.location == "us-west1"
def test_gemini_api_key_configuration():
"""
Test that API key configuration works for both GOOGLE_API_KEY and GEMINI_API_KEY
"""
# Test with GOOGLE_API_KEY
with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-google-key"}):
llm = LLM(model="google/gemini-2.0-flash-001")
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion)
assert llm.api_key == "test-google-key"
# Test with GEMINI_API_KEY
with patch.dict(os.environ, {"GEMINI_API_KEY": "test-gemini-key"}, clear=True):
llm = LLM(model="google/gemini-2.0-flash-001")
assert isinstance(llm, GeminiCompletion)
assert llm.api_key == "test-gemini-key"
def test_gemini_model_capabilities():
"""
Test that model capabilities are correctly identified
"""
# Test Gemini 2.0 model
llm_2_0 = LLM(model="google/gemini-2.0-flash-001")
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm_2_0, GeminiCompletion)
assert llm_2_0.is_gemini_2 == True
assert llm_2_0.supports_tools == True
# Test Gemini 1.5 model
llm_1_5 = LLM(model="google/gemini-1.5-pro")
assert isinstance(llm_1_5, GeminiCompletion)
assert llm_1_5.is_gemini_1_5 == True
assert llm_1_5.supports_tools == True
def test_gemini_generation_config():
"""
Test that generation config is properly prepared
"""
llm = LLM(
model="google/gemini-2.0-flash-001",
temperature=0.7,
top_p=0.9,
top_k=40,
max_output_tokens=1000
)
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion)
# Test config preparation
config = llm._prepare_generation_config()
# Verify config has the expected parameters
assert hasattr(config, 'temperature') or 'temperature' in str(config)
assert hasattr(config, 'top_p') or 'top_p' in str(config)
assert hasattr(config, 'top_k') or 'top_k' in str(config)
assert hasattr(config, 'max_output_tokens') or 'max_output_tokens' in str(config)
def test_gemini_model_detection():
"""
Test that various Gemini model formats are properly detected
"""
# Test Gemini model naming patterns that actually work with provider detection
gemini_test_cases = [
"google/gemini-2.0-flash-001",
"gemini/gemini-2.0-flash-001",
"google/gemini-1.5-pro",
"gemini/gemini-1.5-flash"
]
for model_name in gemini_test_cases:
llm = LLM(model=model_name)
from crewai.llms.providers.gemini.completion import GeminiCompletion
assert isinstance(llm, GeminiCompletion), f"Failed for model: {model_name}"
def test_gemini_supports_stop_words():
"""
Test that Gemini models support stop sequences
"""
llm = LLM(model="google/gemini-2.0-flash-001")
assert llm.supports_stop_words() == True
def test_gemini_context_window_size():
"""
Test that Gemini models return correct context window sizes
"""
# Test Gemini 2.0 Flash
llm_2_0 = LLM(model="google/gemini-2.0-flash-001")
context_size_2_0 = llm_2_0.get_context_window_size()
assert context_size_2_0 > 500000 # Should be substantial (1M tokens)
# Test Gemini 1.5 Pro
llm_1_5 = LLM(model="google/gemini-1.5-pro")
context_size_1_5 = llm_1_5.get_context_window_size()
assert context_size_1_5 > 1000000 # Should be very large (2M tokens)
def test_gemini_message_formatting():
"""
Test that messages are properly formatted for Gemini API
"""
llm = LLM(model="google/gemini-2.0-flash-001")
# Test message formatting
test_messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"},
{"role": "user", "content": "How are you?"}
]
formatted_contents, system_instruction = llm._format_messages_for_gemini(test_messages)
# System message should be extracted
assert system_instruction == "You are a helpful assistant."
# Remaining messages should be Content objects
assert len(formatted_contents) >= 3 # Should have user, model, user messages
# First content should be user role
assert formatted_contents[0].role == "user"
# Second should be model (converted from assistant)
assert formatted_contents[1].role == "model"
def test_gemini_streaming_parameter():
"""
Test that streaming parameter is properly handled
"""
# Test non-streaming
llm_no_stream = LLM(model="google/gemini-2.0-flash-001", stream=False)
assert llm_no_stream.stream == False
# Test streaming
llm_stream = LLM(model="google/gemini-2.0-flash-001", stream=True)
assert llm_stream.stream == True
def test_gemini_tool_conversion():
"""
Test that tools are properly converted to Gemini format
"""
llm = LLM(model="google/gemini-2.0-flash-001")
# Mock tool in CrewAI format
crewai_tools = [{
"type": "function",
"function": {
"name": "test_tool",
"description": "A test tool",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"}
},
"required": ["query"]
}
}
}]
# Test tool conversion
gemini_tools = llm._convert_tools_for_interference(crewai_tools)
assert len(gemini_tools) == 1
# Gemini tools are Tool objects with function_declarations
assert hasattr(gemini_tools[0], 'function_declarations')
assert len(gemini_tools[0].function_declarations) == 1
func_decl = gemini_tools[0].function_declarations[0]
assert func_decl.name == "test_tool"
assert func_decl.description == "A test tool"
def test_gemini_environment_variable_api_key():
"""
Test that Google API key is properly loaded from environment
"""
with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-google-key"}):
llm = LLM(model="google/gemini-2.0-flash-001")
assert llm.client is not None
assert hasattr(llm.client, 'models')
assert llm.api_key == "test-google-key"
def test_gemini_token_usage_tracking():
"""
Test that token usage is properly tracked for Gemini responses
"""
llm = LLM(model="google/gemini-2.0-flash-001")
# Mock the Gemini response with usage information
with patch.object(llm.client.models, 'generate_content') as mock_generate:
mock_response = MagicMock()
mock_response.text = "test response"
mock_response.candidates = []
mock_response.usage_metadata = MagicMock(
prompt_token_count=50,
candidates_token_count=25,
total_token_count=75
)
mock_generate.return_value = mock_response
result = llm.call("Hello")
# Verify the response
assert result == "test response"
# Verify token usage was extracted
usage = llm._extract_token_usage(mock_response)
assert usage["prompt_token_count"] == 50
assert usage["candidates_token_count"] == 25
assert usage["total_token_count"] == 75
assert usage["total_tokens"] == 75

127
uv.lock generated
View File

@@ -1019,6 +1019,9 @@ docling = [
embeddings = [
{ name = "tiktoken" },
]
google-genai = [
{ name = "google-genai" },
]
litellm = [
{ name = "litellm" },
]
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