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Code Issues Actions 4 Packages Projects Releases Wiki Activity

Checking supports_function_calling isntead of gpt models

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This commit is contained in:
João Moura
2024-09-23 16:23:38 -03:00
parent 3b6d1838b4
commit 493f046c03
8 changed files with 34 additions and 61 deletions
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6
src/crewai/agents/agent_builder/utilities/base_output_converter.py
View File

@@ -39,9 +39,3 @@ class OutputConverter(BaseModel, ABC):
def to_json(self, current_attempt=1):
"""Convert text to json."""
pass
@property
@abstractmethod
def is_gpt(self) -> bool:
"""Return if llm provided is of gpt from openai."""
pass

9
src/crewai/llm.py
View File

@@ -1,6 +1,7 @@
from typing import Any, Dict, List, Optional, Union
import logging
import litellm
from litellm import get_supported_openai_params
class LLM:
@@ -85,3 +86,11 @@ class LLM:
except Exception as e:
logging.error(f"LiteLLM call failed: {str(e)}")
raise # Re-raise the exception after logging
def supports_function_calling(self) -> bool:
try:
params = get_supported_openai_params(model=self.model)
return "response_format" in params
except Exception as e:
logging.error(f"Failed to get supported params: {str(e)}")
return False

14
src/crewai/tools/tool_usage.py
View File

@@ -73,7 +73,6 @@ class ToolUsage:
# Set the maximum parsing attempts for bigger models
if (
self.function_calling_llm
and self._is_gpt(self.function_calling_llm)
and self.function_calling_llm in OPENAI_BIGGER_MODELS
):
self._max_parsing_attempts = 2
@@ -299,13 +298,6 @@ class ToolUsage:
)
return "\n--\n".join(descriptions)
def _is_gpt(self, llm) -> bool:
return (
"gpt" in str(llm.model).lower()
or "o1-preview" in str(llm.model).lower()
or "o1-mini" in str(llm.model).lower()
)
def _tool_calling(
self, tool_string: str
) -> Union[ToolCalling, InstructorToolCalling]:
@@ -314,13 +306,9 @@ class ToolUsage:
print("self.function_calling_llm")
model = (
InstructorToolCalling
if self._is_gpt(self.function_calling_llm)
if self.function_calling_llm.supports_function_calling()
else ToolCalling
)
print("model", model)
print(
"self.function_calling_llm.model", self.function_calling_llm.model
)
converter = Converter(
text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
llm=self.function_calling_llm,

25
src/crewai/utilities/converter.py
View File

@@ -2,7 +2,6 @@ import json
import re
from typing import Any, Optional, Type, Union
from crewai.llm import LLM
from pydantic import BaseModel, ValidationError
from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
@@ -24,7 +23,7 @@ class Converter(OutputConverter):
def to_pydantic(self, current_attempt=1):
"""Convert text to pydantic."""
try:
if self.is_gpt:
if self.llm.supports_function_calling():
return self._create_instructor().to_pydantic()
else:
return self.llm.call(
@@ -43,7 +42,7 @@ class Converter(OutputConverter):
def to_json(self, current_attempt=1):
"""Convert text to json."""
try:
if self.is_gpt:
if self.llm.supports_function_calling():
return self._create_instructor().to_json()
else:
return json.dumps(
@@ -86,15 +85,6 @@ class Converter(OutputConverter):
)
return parser.parse_result(result)
@property
def is_gpt(self) -> bool:
"""Return if llm provided is of gpt from openai."""
return (
"gpt" in str(self.llm.model).lower()
or "o1-preview" in str(self.llm.model).lower()
or "o1-mini" in str(self.llm.model).lower()
)
def convert_to_model(
result: str,
@@ -202,21 +192,12 @@ def convert_with_instructions(
def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
instructions = "I'm gonna convert this raw text into valid JSON."
if not is_gpt(llm):
if llm.supports_function_calling():
model_schema = PydanticSchemaParser(model=model).get_schema()
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
return instructions
def is_gpt(llm: LLM) -> bool:
"""Return if llm provided is of gpt from openai."""
return (
"gpt" in str(llm.model).lower()
or "o1-preview" in str(llm.model).lower()
or "o1-mini" in str(llm.model).lower()
)
def create_converter(
agent: Optional[Any] = None,
converter_cls: Optional[Type[Converter]] = None,

11
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -78,7 +78,7 @@ class TaskEvaluator:
instructions = "Convert all responses into valid JSON output."
if not self._is_gpt(self.llm):
if not self.llm.supports_function_calling():
model_schema = PydanticSchemaParser(model=TaskEvaluation).get_schema()
instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
@@ -91,13 +91,6 @@ class TaskEvaluator:
return converter.to_pydantic()
def _is_gpt(self, llm) -> bool:
return (
"gpt" in str(self.llm.model).lower()
or "o1-preview" in str(self.llm.model).lower()
or "o1-mini" in str(self.llm.model).lower()
)
def evaluate_training_data(
self, training_data: dict, agent_id: str
) -> TrainingTaskEvaluation:
@@ -128,7 +121,7 @@ class TaskEvaluator:
)
instructions = "I'm gonna convert this raw text into valid JSON."
if not self._is_gpt(self.llm):
if not self.llm.supports_function_calling():
model_schema = PydanticSchemaParser(
model=TrainingTaskEvaluation
).get_schema()

2
tests/agent_test.py
View File

@@ -816,7 +816,7 @@ def test_agent_step_callback():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_function_calling_llm():
llm = "gpt-4"
llm = "gpt-4o"
@tool
def learn_about_AI() -> str:

1
tests/utilities/evaluators/test_task_evaluator.py
View File

@@ -25,6 +25,7 @@ def test_evaluate_training_data(converter_mock):
}
agent_id = "agent_id"
original_agent = MagicMock()
original_agent.llm.supports_function_calling.return_value = False
function_return_value = TrainingTaskEvaluation(
suggestions=[
"The initial output was already good, having a detailed explanation. However, the improved output "

27
tests/utilities/test_converter.py
View File

@@ -11,11 +11,12 @@ from crewai.utilities.converter import (
create_converter,
get_conversion_instructions,
handle_partial_json,
is_gpt,
validate_model,
)
from pydantic import BaseModel
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
# Sample Pydantic models for testing
class EmailResponse(BaseModel):
@@ -198,14 +199,20 @@ def test_convert_with_instructions_failure(
def test_get_conversion_instructions_gpt():
mock_llm = Mock()
mock_llm.openai_api_base = None
with patch("crewai.utilities.converter.is_gpt", return_value=True):
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
supports_function_calling.return_value = True
instructions = get_conversion_instructions(SimpleModel, mock_llm)
assert instructions == "I'm gonna convert this raw text into valid JSON."
model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
assert (
instructions
== f"I'm gonna convert this raw text into valid JSON.\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
)
def test_get_conversion_instructions_non_gpt():
mock_llm = Mock()
with patch("crewai.utilities.converter.is_gpt", return_value=False):
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
supports_function_calling.return_value = False
with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser:
mock_parser.return_value.get_schema.return_value = "Sample schema"
instructions = get_conversion_instructions(SimpleModel, mock_llm)
@@ -213,14 +220,14 @@ def test_get_conversion_instructions_non_gpt():
# Tests for is_gpt
def test_is_gpt_true():
llm = LLM(model="gpt-4")
assert is_gpt(llm) is True
def test_supports_function_calling_true():
llm = LLM(model="gpt-4o")
assert llm.supports_function_calling() is True
def test_is_gpt_false():
llm = LLM(model="lol-4")
assert is_gpt(llm) is False
def test_supports_function_calling_false():
llm = LLM(model="non-existent-model")
assert llm.supports_function_calling() is False
class CustomConverter(Converter):