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devin/1751
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devin/1741
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81c8dd4e1c | ||
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997462db6d | ||
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1145900b39 |
@@ -829,22 +829,57 @@ class LLM:
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Derives the custom_llm_provider from the model string.
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- For example, if the model is "openrouter/deepseek/deepseek-chat", returns "openrouter".
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- If the model is "gemini/gemini-1.5-pro", returns "gemini".
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- If there is no '/', defaults to "openai".
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- If the model is "azure/gpt-4", returns "azure".
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- If Azure-specific parameters are provided (api_key, api_base, api_version), returns "azure".
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- If there is no '/' and no Azure parameters, returns None.
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"""
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# Check if model explicitly has a provider prefix
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if "/" in self.model:
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return self.model.split("/")[0]
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# Check if all Azure parameters are present
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if self.api_key and self.api_base and self.api_version:
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return "azure"
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return None
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def _validate_azure_credentials(self) -> bool:
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"""
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Validates that all required Azure credentials are present and of the correct type.
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Returns:
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bool: True if all Azure credentials are valid, False otherwise.
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"""
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return bool(
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self.api_key
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and self.api_base
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and self.api_version
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and all(isinstance(x, str) for x in [self.api_key, self.api_base, self.api_version])
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)
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def _validate_call_params(self) -> None:
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"""
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Validate parameters before making a call. Currently this only checks if
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a response_format is provided and whether the model supports it.
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Validate parameters before making a call.
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The custom_llm_provider is dynamically determined from the model:
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- E.g., "openrouter/deepseek/deepseek-chat" yields "openrouter"
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- "gemini/gemini-1.5-pro" yields "gemini"
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- If no slash is present, "openai" is assumed.
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- "azure/gpt-4" yields "azure"
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- If Azure parameters (api_key, api_base, api_version) are present, "azure" is used
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- If no slash is present and no Azure parameters, None is returned
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Raises:
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ValueError: If response_format is not supported by the model or if Azure credentials are incomplete.
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"""
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provider = self._get_custom_llm_provider()
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# Validate Azure credentials if provider is Azure
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if provider == "azure" and not self._validate_azure_credentials():
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raise ValueError(
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"Incomplete Azure credentials. Please provide api_key, api_base, and api_version as strings."
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)
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# Validate response_format
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if self.response_format is not None and not supports_response_schema(
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model=self.model,
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custom_llm_provider=provider,
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37
test_azure_integration.py
Normal file
37
test_azure_integration.py
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@@ -0,0 +1,37 @@
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# test_azure_integration.py
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from src.crewai.llm import LLM
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# Test with Azure parameters but without azure/ prefix
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llm = LLM(
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api_key='test_key',
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api_base='test_base',
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model='gpt-4o-mini-2024-07-18',
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api_version='test_version'
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)
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# Print the detected provider
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provider = llm._get_custom_llm_provider()
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print(f"Detected provider: {provider}")
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print(f"Is Azure detected correctly: {provider == 'azure'}")
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# Prepare parameters that would be passed to LiteLLM
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params = llm._prepare_completion_params(messages=[{"role": "user", "content": "test"}])
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print(f"Parameters passed to LiteLLM: {params}")
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# Test with Azure parameters and with azure/ prefix for comparison
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llm_with_prefix = LLM(
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api_key='test_key',
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api_base='test_base',
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model='azure/gpt-4o-mini-2024-07-18',
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api_version='test_version'
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)
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# Print the detected provider
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provider_with_prefix = llm_with_prefix._get_custom_llm_provider()
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print(f"\nWith azure/ prefix:")
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print(f"Detected provider: {provider_with_prefix}")
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print(f"Is Azure detected correctly: {provider_with_prefix == 'azure'}")
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# Prepare parameters that would be passed to LiteLLM
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params_with_prefix = llm_with_prefix._prepare_completion_params(messages=[{"role": "user", "content": "test"}])
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print(f"Parameters passed to LiteLLM: {params_with_prefix}")
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40
tests/integration/test_azure_credentials.py
Normal file
40
tests/integration/test_azure_credentials.py
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@@ -0,0 +1,40 @@
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import pytest
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from src.crewai.llm import LLM
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def test_azure_detection_with_credentials():
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"""Test that Azure is detected correctly when credentials are provided but model lacks azure/ prefix."""
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# Create LLM instance with Azure parameters but without azure/ prefix
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llm = LLM(
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api_key='test_key',
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api_base='test_base',
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model='gpt-4o-mini-2024-07-18', # Model from issue #2358
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api_version='test_version'
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)
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# Check if provider is detected correctly
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provider = llm._get_custom_llm_provider()
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assert provider == 'azure', "Azure provider should be detected based on credentials"
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# Prepare parameters that would be passed to LiteLLM
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params = llm._prepare_completion_params(messages=[{"role": "user", "content": "test"}])
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assert params.get('api_key') == 'test_key', "API key should be included in params"
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assert params.get('api_base') == 'test_base', "API base should be included in params"
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assert params.get('api_version') == 'test_version', "API version should be included in params"
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def test_azure_validation_error():
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"""Test that validation error is raised when Azure credentials are incomplete."""
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# Create LLM instance with incomplete Azure parameters
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llm = LLM(
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model='azure/gpt-4',
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api_key='test_key',
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# Missing api_base and api_version
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)
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# Validation should fail
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with pytest.raises(ValueError) as excinfo:
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llm._validate_call_params()
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assert "Incomplete Azure credentials" in str(excinfo.value)
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@@ -221,6 +221,20 @@ def test_get_custom_llm_provider_openai():
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llm = LLM(model="gpt-4")
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assert llm._get_custom_llm_provider() == None
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def test_get_custom_llm_provider_azure_with_prefix():
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llm = LLM(model="azure/gpt-4")
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assert llm._get_custom_llm_provider() == "azure"
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def test_get_custom_llm_provider_azure_without_prefix():
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llm = LLM(
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model="gpt-4",
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api_key="test_key",
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api_base="test_base",
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api_version="test_version"
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)
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assert llm._get_custom_llm_provider() == "azure"
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def test_validate_call_params_supported():
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class DummyResponse(BaseModel):
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@@ -253,6 +267,51 @@ def test_validate_call_params_no_response_format():
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llm._validate_call_params()
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def test_validate_azure_credentials_valid():
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llm = LLM(
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model="gpt-4",
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api_key="test_key",
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api_base="test_base",
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api_version="test_version"
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)
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assert llm._validate_azure_credentials() == True
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def test_validate_azure_credentials_invalid():
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# Missing api_version
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llm = LLM(
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model="gpt-4",
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api_key="test_key",
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api_base="test_base"
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)
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assert llm._validate_azure_credentials() == False
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# Non-string value
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llm = LLM(
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model="gpt-4",
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api_key="test_key",
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api_base="test_base",
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api_version=123 # Not a string
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)
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assert llm._validate_azure_credentials() == False
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def test_validate_call_params_azure_invalid():
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# Test with incomplete Azure credentials
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llm = LLM(
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model="azure/gpt-4",
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api_key="test_key",
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# Missing api_base and api_version
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)
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# Should raise ValueError due to incomplete credentials
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with pytest.raises(ValueError) as excinfo:
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llm._validate_call_params()
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# Check error message
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assert "Incomplete Azure credentials" in str(excinfo.value)
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_o3_mini_reasoning_effort_high():
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llm = LLM(
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