mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-08 23:58:34 +00:00
Merge branch 'main' into fix/cli-create-provider-flag
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@@ -290,25 +290,18 @@ class Crew(BaseModel):
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else EntityMemory(crew=self, embedder_config=self.embedder)
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)
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if (
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self.memory_config and "user_memory" in self.memory_config and self.memory_config.get('provider') == 'mem0'
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self.memory_config
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and "user_memory" in self.memory_config
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and self.memory_config.get("provider") == "mem0"
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): # Check for user_memory in config
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user_memory_config = self.memory_config["user_memory"]
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if isinstance(
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user_memory_config, dict
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): # Check if it's a configuration dict
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self._user_memory = UserMemory(
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crew=self
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)
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self._user_memory = UserMemory(crew=self)
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else:
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raise TypeError(
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"user_memory must be a configuration dictionary"
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)
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raise TypeError("user_memory must be a configuration dictionary")
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else:
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self._logger.log(
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"warning",
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"User memory initialization failed. For setup instructions, please refer to the memory documentation: https://docs.crewai.com/concepts/memory#integrating-mem0-for-enhanced-user-memory",
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color="yellow"
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)
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self._user_memory = None # No user memory if not in config
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return self
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@@ -1159,7 +1152,7 @@ class Crew(BaseModel):
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def copy(self):
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"""
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Creates a deep copy of the Crew instance.
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Returns:
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Crew: A new instance with copied components
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"""
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@@ -1181,7 +1174,6 @@ class Crew(BaseModel):
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"knowledge",
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"manager_agent",
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"manager_llm",
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}
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cloned_agents = [agent.copy() for agent in self.agents]
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@@ -7,29 +7,27 @@ from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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PydanticDeprecatedSince20,
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create_model,
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validator,
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field_validator,
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)
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from pydantic import BaseModel as PydanticBaseModel
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from crewai.tools.structured_tool import CrewStructuredTool
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# Ignore all "PydanticDeprecatedSince20" warnings globally
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warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
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class BaseTool(BaseModel, ABC):
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class _ArgsSchemaPlaceholder(PydanticBaseModel):
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pass
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model_config = ConfigDict()
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model_config = ConfigDict(arbitrary_types_allowed=True)
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name: str
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"""The unique name of the tool that clearly communicates its purpose."""
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description: str
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"""Used to tell the model how/when/why to use the tool."""
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args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
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args_schema: Type[PydanticBaseModel] = Field(
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default_factory=_ArgsSchemaPlaceholder, validate_default=True
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)
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"""The schema for the arguments that the tool accepts."""
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description_updated: bool = False
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"""Flag to check if the description has been updated."""
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@@ -38,7 +36,8 @@ class BaseTool(BaseModel, ABC):
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result_as_answer: bool = False
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"""Flag to check if the tool should be the final agent answer."""
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@validator("args_schema", always=True, pre=True)
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@field_validator("args_schema", mode="before")
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@classmethod
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def _default_args_schema(
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cls, v: Type[PydanticBaseModel]
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) -> Type[PydanticBaseModel]:
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@@ -287,8 +287,9 @@ def generate_model_description(model: Type[BaseModel]) -> str:
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else:
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return str(field_type)
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fields = model.__annotations__
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fields = model.model_fields
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field_descriptions = [
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f'"{name}": {describe_field(type_)}' for name, type_ in fields.items()
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f'"{name}": {describe_field(field.annotation)}'
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for name, field in fields.items()
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]
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return "{\n " + ",\n ".join(field_descriptions) + "\n}"
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