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Add Support for Custom Providers in Vision and Function Call Utils (#5688)

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* Add Support for Custom Providers in Vision and Function Call Utils Lookup

* Remove parallel function call due to missing model info param

* Add Unit Tests for Vision and Function Call Changes
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Cole Murray 2024-09-26 10:26:42 -07:00 • committed by GitHub
parent acd8facf98
commit bcbc44d731
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2 changed files with 62 additions and 15 deletions
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34
litellm/tests/test_utils.py
View file

@ -762,6 +762,40 @@ def test_supports_response_schema(model, expected_bool):
assert expected_bool == response
@pytest.mark.parametrize(
"model, expected_bool",
[
("gpt-3.5-turbo", True),
("gpt-4", True),
("command-nightly", False),
("gemini-pro", True),
],
)
def test_supports_function_calling(model, expected_bool):
"""
Unit test for 'supports_function_calling' helper function.
"""
from litellm.utils import supports_function_calling
response = supports_function_calling(model=model, custom_llm_provider=None)
assert expected_bool == response
@pytest.mark.parametrize(
"model, expected_bool",
[
("gpt-4-vision-preview", True),
("gpt-3.5-turbo", False),
("claude-3-opus-20240229", True),
("gemini-pro-vision", True),
("command-nightly", False),
],
)
def test_supports_vision(model, expected_bool):
"""
Unit test for 'supports_vision' helper function.
"""
from litellm.utils import supports_vision
response = supports_vision(model=model, custom_llm_provider=None)
assert expected_bool == response
def test_usage_object_null_tokens():
"""

43
litellm/utils.py
View file

@ -2148,50 +2148,64 @@ def supports_response_schema(model: str, custom_llm_provider: Optional[str]) ->
return False
def supports_function_calling(model: str) -> bool:
def supports_function_calling(model: str, custom_llm_provider: Optional[str] = None) -> bool:
"""
Check if the given model supports function calling and return a boolean value.
Parameters:
model (str): The model name to be checked.
custom_llm_provider (Optional[str]): The provider to be checked.
Returns:
bool: True if the model supports function calling, False otherwise.
Raises:
Exception: If the given model is not found in model_prices_and_context_window.json.
Exception: If the given model is not found or there's an error in retrieval.
"""
try:
model, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=model, custom_llm_provider=custom_llm_provider
)
model_info = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
if model in litellm.model_cost:
model_info = litellm.model_cost[model]
if model_info.get("supports_function_calling", False) is True:
return True
return False
else:
except Exception as e:
raise Exception(
f"Model not supports function calling. You passed model={model}."
f"Model not found or error in checking function calling support. You passed model={model}, custom_llm_provider={custom_llm_provider}. Error: {str(e)}"
)
def supports_vision(model: str):
def supports_vision(model: str, custom_llm_provider: Optional[str] = None) -> bool:
"""
Check if the given model supports vision and return a boolean value.
Parameters:
model (str): The model name to be checked.
custom_llm_provider (Optional[str]): The provider to be checked.
Returns:
bool: True if the model supports vision, False otherwise.
Raises:
Exception: If the given model is not found in model_prices_and_context_window.json.
"""
if model in litellm.model_cost:
model_info = litellm.model_cost[model]
try:
model, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=model, custom_llm_provider=custom_llm_provider
)
model_info = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
if model_info.get("supports_vision", False) is True:
return True
return False
else:
except Exception as e:
verbose_logger.error(
f"Model not found or error in checking vision support. You passed model={model}, custom_llm_provider={custom_llm_provider}. Error: {str(e)}"
)
return False
@ -2218,7 +2232,6 @@ def supports_parallel_function_calling(model: str):
f"Model not supports parallel function calling. You passed model={model}."
)
####### HELPER FUNCTIONS ################
def _update_dictionary(existing_dict: Dict, new_dict: dict) -> dict:
for k, v in new_dict.items():