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openai

dandy.llm.request.openai

OpenaiRequestBody

Bases: BaseRequestBody

stream = False class-attribute instance-attribute

response_format = {'type': 'json_schema', 'json_schema': {'name': 'response', 'strict': False, 'schema': ...}} class-attribute instance-attribute

max_completion_tokens = None class-attribute instance-attribute

seed = None class-attribute instance-attribute

temperature = None class-attribute instance-attribute

token_usage property

add_message

Source code in dandy/llm/request/openai.py 
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def add_message(
        self,
        role: RoleLiteralStr,
        content: str,
        images: Union[List[str], None] = None
) -> None:
    message_content: List[dict] = [{
        'type': 'text',
        'text': content,
    }]

    if images is not None:
        for image in images:
            message_content.append({
                'type': 'image_url',
                'image_url': {
                    'url': f'data:{get_image_mime_type_from_base64_string(image)};base64,{image}'
                }
            })

    self.messages.append(
        RequestMessage(
            role=role,
            content=message_content,
        )
    )

get_context_length

Source code in dandy/llm/request/openai.py 
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def get_context_length(self) -> int:
    return 0

get_max_completion_tokens

Source code in dandy/llm/request/openai.py 
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def get_max_completion_tokens(self) -> int:
    return self.max_completion_tokens

get_seed

Source code in dandy/llm/request/openai.py 
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def get_seed(self) -> int:
    return self.seed

get_temperature

Source code in dandy/llm/request/openai.py 
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def get_temperature(self) -> float:
    return self.temperature

set_format_to_json_schema

Source code in dandy/llm/request/openai.py 
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def set_format_to_json_schema(self, json_schema: dict):
    self.response_format['json_schema']['schema'] = json_schema

set_format_to_text

Source code in dandy/llm/request/openai.py 
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def set_format_to_text(self):
    self.response_format = {'type': 'text'}

to_dict

Source code in dandy/llm/request/openai.py 
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def to_dict(self) -> dict:
    model_dict = self.model_dump()
    formated_messages = []
    for message in model_dict['messages']:
        for content in message['content']:
            if content['type'] == 'text':
                formated_messages.append({
                    'role': message['role'],
                    'content': content['text'],
                })
            elif content['type'] == 'image_url':
                formated_messages.append({
                    'role': message['role'],
                    'content': content['image_url']['url'].split(';base64,')[1],
                })

    model_dict['messages'] = formated_messages

    return model_dict