mirror of
https://github.com/crewAIInc/crewAI.git
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Fix nested pydantic model issue (#1905)
* Fix nested pydantic model issue * fix failing tests * add in vcr * cleanup * drop prints * Fix vcr issues * added new recordings * trying to fix vcr * add in fix from lorenze.
This commit is contained in:
Brandon Hancock (bhancock_ai)
committed by
GitHub
GitHub
parent
42311d9c7a
commit
b5779dca12
@@ -25,7 +25,7 @@ class OutputConverter(BaseModel, ABC):
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llm: Any = Field(description="The language model to be used to convert the text.")
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model: Any = Field(description="The model to be used to convert the text.")
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instructions: str = Field(description="Conversion instructions to the LLM.")
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max_attempts: Optional[int] = Field(
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max_attempts: int = Field(
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description="Max number of attempts to try to get the output formatted.",
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default=3,
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)
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@@ -26,17 +26,24 @@ class Converter(OutputConverter):
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if self.llm.supports_function_calling():
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return self._create_instructor().to_pydantic()
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else:
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return self.llm.call(
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response = self.llm.call(
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[
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{"role": "system", "content": self.instructions},
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{"role": "user", "content": self.text},
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]
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)
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return self.model.model_validate_json(response)
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except ValidationError as e:
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if current_attempt < self.max_attempts:
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return self.to_pydantic(current_attempt + 1)
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raise ConverterError(
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f"Failed to convert text into a Pydantic model due to the following validation error: {e}"
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)
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except Exception as e:
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if current_attempt < self.max_attempts:
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return self.to_pydantic(current_attempt + 1)
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return ConverterError(
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f"Failed to convert text into a pydantic model due to the following error: {e}"
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raise ConverterError(
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f"Failed to convert text into a Pydantic model due to the following error: {e}"
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)
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def to_json(self, current_attempt=1):
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@@ -66,7 +73,6 @@ class Converter(OutputConverter):
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llm=self.llm,
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model=self.model,
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content=self.text,
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instructions=self.instructions,
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)
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return inst
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@@ -187,10 +193,15 @@ def convert_with_instructions(
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def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
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instructions = "I'm gonna convert this raw text into valid JSON."
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instructions = "Please convert the following text into valid JSON."
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if llm.supports_function_calling():
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model_schema = PydanticSchemaParser(model=model).get_schema()
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instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
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instructions += (
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f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
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)
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else:
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model_description = generate_model_description(model)
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instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
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return instructions
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@@ -11,12 +11,10 @@ class InternalInstructor:
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model: Type,
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agent: Optional[Any] = None,
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llm: Optional[str] = None,
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instructions: Optional[str] = None,
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):
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self.content = content
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self.agent = agent
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self.llm = llm
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self.instructions = instructions
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self.model = model
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self._client = None
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self.set_instructor()
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@@ -31,10 +29,7 @@ class InternalInstructor:
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import instructor
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from litellm import completion
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self._client = instructor.from_litellm(
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completion,
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mode=instructor.Mode.TOOLS,
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)
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self._client = instructor.from_litellm(completion)
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def to_json(self):
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model = self.to_pydantic()
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@@ -42,8 +37,6 @@ class InternalInstructor:
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def to_pydantic(self):
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messages = [{"role": "user", "content": self.content}]
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if self.instructions:
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messages.append({"role": "system", "content": self.instructions})
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model = self._client.chat.completions.create(
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model=self.llm.model, response_model=self.model, messages=messages
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)
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@@ -1,4 +1,4 @@
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from typing import Type, Union, get_args, get_origin
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from typing import Dict, List, Type, Union, get_args, get_origin
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from pydantic import BaseModel
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@@ -10,40 +10,83 @@ class PydanticSchemaParser(BaseModel):
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"""
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Public method to get the schema of a Pydantic model.
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:param model: The Pydantic model class to generate schema for.
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:return: String representation of the model schema.
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"""
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return self._get_model_schema(self.model)
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return "{\n" + self._get_model_schema(self.model) + "\n}"
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def _get_model_schema(self, model, depth=0) -> str:
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indent = " " * depth
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lines = [f"{indent}{{"]
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for field_name, field in model.model_fields.items():
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field_type_str = self._get_field_type(field, depth + 1)
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lines.append(f"{indent} {field_name}: {field_type_str},")
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lines[-1] = lines[-1].rstrip(",") # Remove trailing comma from last item
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lines.append(f"{indent}}}")
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return "\n".join(lines)
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def _get_model_schema(self, model: Type[BaseModel], depth: int = 0) -> str:
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indent = " " * 4 * depth
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lines = [
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f"{indent} {field_name}: {self._get_field_type(field, depth + 1)}"
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for field_name, field in model.model_fields.items()
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]
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return ",\n".join(lines)
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def _get_field_type(self, field, depth) -> str:
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def _get_field_type(self, field, depth: int) -> str:
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field_type = field.annotation
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if get_origin(field_type) is list:
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origin = get_origin(field_type)
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if origin in {list, List}:
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list_item_type = get_args(field_type)[0]
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if isinstance(list_item_type, type) and issubclass(
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list_item_type, BaseModel
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):
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nested_schema = self._get_model_schema(list_item_type, depth + 1)
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return f"List[\n{nested_schema}\n{' ' * 4 * depth}]"
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return self._format_list_type(list_item_type, depth)
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if origin in {dict, Dict}:
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key_type, value_type = get_args(field_type)
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return f"Dict[{key_type.__name__}, {value_type.__name__}]"
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if origin is Union:
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return self._format_union_type(field_type, depth)
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if isinstance(field_type, type) and issubclass(field_type, BaseModel):
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nested_schema = self._get_model_schema(field_type, depth)
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nested_indent = " " * 4 * depth
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return f"{field_type.__name__}\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}"
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return field_type.__name__
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def _format_list_type(self, list_item_type, depth: int) -> str:
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if isinstance(list_item_type, type) and issubclass(list_item_type, BaseModel):
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nested_schema = self._get_model_schema(list_item_type, depth + 1)
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nested_indent = " " * 4 * (depth)
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return f"List[\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}\n{nested_indent}]"
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return f"List[{list_item_type.__name__}]"
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def _format_union_type(self, field_type, depth: int) -> str:
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args = get_args(field_type)
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if type(None) in args:
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# It's an Optional type
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non_none_args = [arg for arg in args if arg is not type(None)]
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if len(non_none_args) == 1:
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inner_type = self._get_field_type_for_annotation(
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non_none_args[0], depth
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)
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return f"Optional[{inner_type}]"
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else:
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return f"List[{list_item_type.__name__}]"
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elif get_origin(field_type) is Union:
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union_args = get_args(field_type)
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if type(None) in union_args:
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non_none_type = next(arg for arg in union_args if arg is not type(None))
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return f"Optional[{self._get_field_type(field.__class__(annotation=non_none_type), depth)}]"
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else:
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return f"Union[{', '.join(arg.__name__ for arg in union_args)}]"
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elif isinstance(field_type, type) and issubclass(field_type, BaseModel):
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return self._get_model_schema(field_type, depth)
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# Union with None and multiple other types
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inner_types = ", ".join(
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self._get_field_type_for_annotation(arg, depth)
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for arg in non_none_args
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)
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return f"Optional[Union[{inner_types}]]"
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else:
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return getattr(field_type, "__name__", str(field_type))
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# General Union type
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inner_types = ", ".join(
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self._get_field_type_for_annotation(arg, depth) for arg in args
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)
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return f"Union[{inner_types}]"
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def _get_field_type_for_annotation(self, annotation, depth: int) -> str:
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origin = get_origin(annotation)
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if origin in {list, List}:
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list_item_type = get_args(annotation)[0]
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return self._format_list_type(list_item_type, depth)
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if origin in {dict, Dict}:
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key_type, value_type = get_args(annotation)
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return f"Dict[{key_type.__name__}, {value_type.__name__}]"
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if origin is Union:
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return self._format_union_type(annotation, depth)
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if isinstance(annotation, type) and issubclass(annotation, BaseModel):
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nested_schema = self._get_model_schema(annotation, depth)
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nested_indent = " " * 4 * depth
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return f"{annotation.__name__}\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}"
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return annotation.__name__
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114
tests/utilities/cassettes/test_convert_with_instructions.yaml
Normal file
114
tests/utilities/cassettes/test_convert_with_instructions.yaml
Normal file
@@ -0,0 +1,114 @@
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2048
tests/utilities/cassettes/test_converter_with_llama3_1_model.yaml
Normal file
2048
tests/utilities/cassettes/test_converter_with_llama3_1_model.yaml
Normal file
File diff suppressed because it is too large
Load Diff
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\"function_name\", \"arguments\":{\"argument_name\": \"argument_value\"}} The
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following functions are available to you:\n{''type'': ''function'', ''function'':
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{''name'': ''SimpleModel'', ''description'': ''Correctly extracted `SimpleModel`
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with all the required parameters with correct types'', ''parameters'': {''properties'':
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data: SimpleModel
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class Address(BaseModel):
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street: str
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|
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|
||||
def mock_agent():
|
||||
@@ -199,26 +215,23 @@ def test_convert_with_instructions_failure(
|
||||
|
||||
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|
||||
def test_get_conversion_instructions_gpt():
|
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mock_llm = Mock()
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mock_llm.openai_api_base = None
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llm = LLM(model="gpt-4o-mini")
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|
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supports_function_calling.return_value = True
|
||||
instructions = get_conversion_instructions(SimpleModel, mock_llm)
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
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}"
|
||||
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)
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def test_get_conversion_instructions_non_gpt():
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mock_llm = Mock()
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||||
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||||
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|
||||
mock_parser.return_value.get_schema.return_value = "Sample schema"
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||||
instructions = get_conversion_instructions(SimpleModel, mock_llm)
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|
||||
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|
||||
with patch.object(LLM, "supports_function_calling", return_value=False):
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instructions = get_conversion_instructions(SimpleModel, llm)
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|
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assert '"age": int' in instructions
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# Tests for is_gpt
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@@ -232,10 +245,6 @@ def test_supports_function_calling_false():
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||||
assert llm.supports_function_calling() is False
|
||||
|
||||
|
||||
class CustomConverter(Converter):
|
||||
pass
|
||||
|
||||
|
||||
def test_create_converter_with_mock_agent():
|
||||
mock_agent = MagicMock()
|
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mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
|
||||
@@ -255,7 +264,7 @@ def test_create_converter_with_mock_agent():
|
||||
def test_create_converter_with_custom_converter():
|
||||
converter = create_converter(
|
||||
converter_cls=CustomConverter,
|
||||
llm=Mock(),
|
||||
llm=LLM(model="gpt-4o-mini"),
|
||||
text="Sample",
|
||||
model=SimpleModel,
|
||||
instructions="Convert",
|
||||
@@ -313,3 +322,269 @@ def test_generate_model_description_dict_field():
|
||||
description = generate_model_description(ModelWithDictField)
|
||||
expected_description = '{\n "attributes": Dict[str, int]\n}'
|
||||
assert description == expected_description
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_convert_with_instructions():
|
||||
llm = LLM(model="gpt-4o-mini")
|
||||
sample_text = "Name: Alice, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
# Act
|
||||
output = converter.to_pydantic()
|
||||
|
||||
# Assert
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Alice"
|
||||
assert output.age == 30
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_converter_with_llama3_2_model():
|
||||
llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
|
||||
|
||||
sample_text = "Name: Alice Llama, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Alice Llama"
|
||||
assert output.age == 30
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_converter_with_llama3_1_model():
|
||||
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
|
||||
sample_text = "Name: Alice Llama, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Alice Llama"
|
||||
assert output.age == 30
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_converter_with_nested_model():
|
||||
llm = LLM(model="gpt-4o-mini")
|
||||
sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
|
||||
|
||||
instructions = get_conversion_instructions(Person, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=Person,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, Person)
|
||||
assert output.name == "John Doe"
|
||||
assert output.age == 30
|
||||
assert isinstance(output.address, Address)
|
||||
assert output.address.street == "123 Main St"
|
||||
assert output.address.city == "Anytown"
|
||||
assert output.address.zip_code == "12345"
|
||||
|
||||
|
||||
# Tests for error handling
|
||||
def test_converter_error_handling():
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = False
|
||||
llm.call.return_value = "Invalid JSON"
|
||||
sample_text = "Name: Alice, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
with pytest.raises(ConverterError) as exc_info:
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
|
||||
|
||||
|
||||
# Tests for retry logic
|
||||
def test_converter_retry_logic():
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = False
|
||||
llm.call.side_effect = [
|
||||
"Invalid JSON",
|
||||
"Still invalid",
|
||||
'{"name": "Retry Alice", "age": 30}',
|
||||
]
|
||||
sample_text = "Name: Retry Alice, Age: 30"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
instructions=instructions,
|
||||
max_attempts=3,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Retry Alice"
|
||||
assert output.age == 30
|
||||
assert llm.call.call_count == 3
|
||||
|
||||
|
||||
# Tests for optional fields
|
||||
def test_converter_with_optional_fields():
|
||||
class OptionalModel(BaseModel):
|
||||
name: str
|
||||
age: Optional[int]
|
||||
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = False
|
||||
# Simulate the LLM's response with 'age' explicitly set to null
|
||||
llm.call.return_value = '{"name": "Bob", "age": null}'
|
||||
sample_text = "Name: Bob, age: None"
|
||||
|
||||
instructions = get_conversion_instructions(OptionalModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=OptionalModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, OptionalModel)
|
||||
assert output.name == "Bob"
|
||||
assert output.age is None
|
||||
|
||||
|
||||
# Tests for list fields
|
||||
def test_converter_with_list_field():
|
||||
class ListModel(BaseModel):
|
||||
items: List[int]
|
||||
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = False
|
||||
llm.call.return_value = '{"items": [1, 2, 3]}'
|
||||
sample_text = "Items: 1, 2, 3"
|
||||
|
||||
instructions = get_conversion_instructions(ListModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=ListModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, ListModel)
|
||||
assert output.items == [1, 2, 3]
|
||||
|
||||
|
||||
# Tests for enums
|
||||
from enum import Enum
|
||||
|
||||
|
||||
def test_converter_with_enum():
|
||||
class Color(Enum):
|
||||
RED = "red"
|
||||
GREEN = "green"
|
||||
BLUE = "blue"
|
||||
|
||||
class EnumModel(BaseModel):
|
||||
name: str
|
||||
color: Color
|
||||
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = False
|
||||
llm.call.return_value = '{"name": "Alice", "color": "red"}'
|
||||
sample_text = "Name: Alice, Color: Red"
|
||||
|
||||
instructions = get_conversion_instructions(EnumModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=EnumModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, EnumModel)
|
||||
assert output.name == "Alice"
|
||||
assert output.color == Color.RED
|
||||
|
||||
|
||||
# Tests for ambiguous input
|
||||
def test_converter_with_ambiguous_input():
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = False
|
||||
llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
|
||||
sample_text = "Charlie is thirty years old"
|
||||
|
||||
instructions = get_conversion_instructions(SimpleModel, llm)
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text=sample_text,
|
||||
model=SimpleModel,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
with pytest.raises(ConverterError) as exc_info:
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert "validation error" in str(exc_info.value).lower()
|
||||
|
||||
|
||||
# Tests for function calling support
|
||||
def test_converter_with_function_calling():
|
||||
llm = Mock(spec=LLM)
|
||||
llm.supports_function_calling.return_value = True
|
||||
|
||||
instructor = Mock()
|
||||
instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
|
||||
|
||||
converter = Converter(
|
||||
llm=llm,
|
||||
text="Name: Eve, Age: 35",
|
||||
model=SimpleModel,
|
||||
instructions="Convert this text.",
|
||||
)
|
||||
converter._create_instructor = Mock(return_value=instructor)
|
||||
|
||||
output = converter.to_pydantic()
|
||||
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Eve"
|
||||
assert output.age == 35
|
||||
instructor.to_pydantic.assert_called_once()
|
||||
|
||||
94
tests/utilities/test_pydantic_schema_parser.py
Normal file
94
tests/utilities/test_pydantic_schema_parser.py
Normal file
@@ -0,0 +1,94 @@
|
||||
from typing import Any, Dict, List, Optional, Set, Tuple, Union
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
||||
|
||||
|
||||
def test_simple_model():
|
||||
class SimpleModel(BaseModel):
|
||||
field1: int
|
||||
field2: str
|
||||
|
||||
parser = PydanticSchemaParser(model=SimpleModel)
|
||||
schema = parser.get_schema()
|
||||
|
||||
expected_schema = """{
|
||||
field1: int,
|
||||
field2: str
|
||||
}"""
|
||||
assert schema.strip() == expected_schema.strip()
|
||||
|
||||
|
||||
def test_nested_model():
|
||||
class NestedModel(BaseModel):
|
||||
nested_field: int
|
||||
|
||||
class ParentModel(BaseModel):
|
||||
parent_field: str
|
||||
nested: NestedModel
|
||||
|
||||
parser = PydanticSchemaParser(model=ParentModel)
|
||||
schema = parser.get_schema()
|
||||
|
||||
expected_schema = """{
|
||||
parent_field: str,
|
||||
nested: NestedModel
|
||||
{
|
||||
nested_field: int
|
||||
}
|
||||
}"""
|
||||
assert schema.strip() == expected_schema.strip()
|
||||
|
||||
|
||||
def test_model_with_list():
|
||||
class ListModel(BaseModel):
|
||||
list_field: List[int]
|
||||
|
||||
parser = PydanticSchemaParser(model=ListModel)
|
||||
schema = parser.get_schema()
|
||||
|
||||
expected_schema = """{
|
||||
list_field: List[int]
|
||||
}"""
|
||||
assert schema.strip() == expected_schema.strip()
|
||||
|
||||
|
||||
def test_model_with_optional_field():
|
||||
class OptionalModel(BaseModel):
|
||||
optional_field: Optional[str]
|
||||
|
||||
parser = PydanticSchemaParser(model=OptionalModel)
|
||||
schema = parser.get_schema()
|
||||
|
||||
expected_schema = """{
|
||||
optional_field: Optional[str]
|
||||
}"""
|
||||
assert schema.strip() == expected_schema.strip()
|
||||
|
||||
|
||||
def test_model_with_union():
|
||||
class UnionModel(BaseModel):
|
||||
union_field: Union[int, str]
|
||||
|
||||
parser = PydanticSchemaParser(model=UnionModel)
|
||||
schema = parser.get_schema()
|
||||
|
||||
expected_schema = """{
|
||||
union_field: Union[int, str]
|
||||
}"""
|
||||
assert schema.strip() == expected_schema.strip()
|
||||
|
||||
|
||||
def test_model_with_dict():
|
||||
class DictModel(BaseModel):
|
||||
dict_field: Dict[str, int]
|
||||
|
||||
parser = PydanticSchemaParser(model=DictModel)
|
||||
schema = parser.get_schema()
|
||||
|
||||
expected_schema = """{
|
||||
dict_field: Dict[str, int]
|
||||
}"""
|
||||
assert schema.strip() == expected_schema.strip()
|
||||
Reference in New Issue
Block a user