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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
import json
import uuid
from typing import ClassVar, Optional, Union
from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
from camel.prompts import PersonaHubPrompt, TextPrompt
class Persona(BaseModel):
r"""A persona is a character in the society.
Attributes:
name (Optional[str]): Name of the persona.
description (Optional[str]): Description of the persona.
text_to_persona_prompt (Union[TextPrompt, str]): The prompt to convert
text into a persona.
persona_to_persona_prompt (Union[TextPrompt, str]): Persona-to-Persona
interaction prompt.
id (uuid.UUID): The unique identifier for the persona, automatically
generated.
_id (uuid.UUID): Internal unique identifier for the persona,
generated lazily using `uuid.uuid4`.
model_config (ClassVar[ConfigDict]): Configuration for the Pydantic
model. Allows arbitrary types and includes custom JSON schema
settings.
"""
name: Optional[str] = None
description: Optional[str] = None
_id: uuid.UUID = PrivateAttr(default_factory=uuid.uuid4)
# Field with default_factory to avoid circular import issues
# Union type allows either TextPrompt or str
text_to_persona_prompt: Union[TextPrompt, str] = Field(
default_factory=lambda: PersonaHubPrompt.TEXT_TO_PERSONA,
description="Text to Persona Prompt",
)
# Similar to text_to_persona_prompt, using default_factory for lazy
# evaluation
persona_to_persona_prompt: Union[TextPrompt, str] = Field(
default_factory=lambda: PersonaHubPrompt.PERSONA_TO_PERSONA,
description="Persona to Persona Prompt",
)
# Class-level configuration for Pydantic model
# ClassVar indicates this is a class variable, not an instance variable
model_config: ClassVar[ConfigDict] = ConfigDict(
# Allow the use of custom types TextPrompt
arbitrary_types_allowed=True,
# Custom JSON schema configuration
json_schema_extra={
"properties": {
# Ensure text_to_persona_prompt and persona_to_persona_prompt
# are treated as strings in JSON schema
"text_to_persona_prompt": {"type": "string"},
"persona_to_persona_prompt": {"type": "string"},
}
},
)
@property
def id(self) -> uuid.UUID:
return self._id
@classmethod
def model_json_schema(cls):
schema = super().schema()
schema['properties']['id'] = {'type': 'string', 'format': 'uuid'}
return schema
def dict(self, *args, **kwargs):
# Output: {'name': 'Alice', 'description': None, 'text_to_persona_prompt': '...', 'persona_to_persona_prompt': '...', 'id': 'f47ac10b-58cc-4372-a567-0e02b2c3d479'} # noqa: E501
d = super().model_dump(*args, **kwargs)
d['id'] = str(self.id)
return d
def json(self, *args, **kwargs):
# Output: '{"name": "Alice", "description": null, "text_to_persona_prompt": "...", "persona_to_persona_prompt": "...", "id": "f47ac10b-58cc-4372-a567-0e02b2c3d479"}' # noqa: E501
d = self.dict(*args, **kwargs)
return json.dumps(
d,
indent=4, # Pretty-print with 4 spaces indentation
sort_keys=True, # Sort keys alphabetically
separators=(
",",
": ",
), # Fine-tune separators for better readability
)
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