egutierrez
613cd90662
- Implemented OpenAICredencial class for managing OpenAI API keys. - Created OpenAICredencialModel and OpenAICredencialMapper for SQLAlchemy integration. - Developed OpenAICredencialRepo for CRUD operations on OpenAI credentials. - Established OpenAICliente class for interacting with OpenAI API. - Introduced PostgresCredencial class for managing PostgreSQL connection details. - Created PostgresCredencialModel and PostgresCredencialMapper for SQLAlchemy integration. - Developed PostgresCredencialRepo for CRUD operations on PostgreSQL credentials. - Added base connection class and PostgreSQL connection implementation. - Included environment variable loading for sensitive data management.
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
from llms.Modelos.Base_model import ModeloABC
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from src.ConexionApis.OpenAi_conexion import OpenAICliente
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class ModeloOpenAI(ModeloABC):
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def __init__(
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self,
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cliente: OpenAICliente,
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model: str = "gpt-4o",
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temperature: float = 0.7,
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top_p: float = 1.0,
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top_k: int = None,
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frecuencia_penalizacion: float = 0.0,
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num_tokens_maximos: int = 512,
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use_legacy: bool = False
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):
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super().__init__(
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model=model,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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frecuencia_penalizacion=frecuencia_penalizacion,
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num_tokens_maximos=num_tokens_maximos
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)
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self.cliente = cliente
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self.use_legacy = use_legacy
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async def responder(self, prompt: str, system_prompt: str = "", **kwargs) -> str:
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import asyncio
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if self.use_legacy:
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loop = asyncio.get_event_loop()
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respuesta = await loop.run_in_executor(
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None,
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lambda: self.cliente.completion(
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model=self.model,
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prompt=prompt,
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temperature=self.temperature,
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top_p=self.top_p,
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max_tokens=self.num_tokens_maximos,
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frequency_penalty=self.frecuencia_penalizacion,
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**kwargs
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)
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)
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return respuesta.choices[0].text.strip()
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else:
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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loop = asyncio.get_event_loop()
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respuesta = await loop.run_in_executor(
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None,
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lambda: self.cliente.chat_completion(
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model=self.model,
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messages=messages,
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temperature=self.temperature,
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top_p=self.top_p,
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max_tokens=self.num_tokens_maximos,
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frequency_penalty=self.frecuencia_penalizacion,
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**kwargs
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)
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)
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return respuesta.choices[0].message.content |