1807435339
- Introduction of dynamic Processors - Introduction of caching system - Introduction of a better template manager - Adaptation of ModelVariables to support dynamic Processors / Retrievers / Specialists - Start adaptation of chat client
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
from abc import ABC, abstractmethod
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from typing import Dict, Any
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from flask import current_app
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from config.logging_config import TuningLogger
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from eveai_chat_workers.specialists.specialist_typing import SpecialistArguments, SpecialistResult
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class BaseSpecialist(ABC):
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"""Base class for all specialists"""
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def __init__(self, tenant_id: int, specialist_id: int, session_id: str):
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self.tenant_id = tenant_id
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self.specialist_id = specialist_id
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self.session_id = session_id
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self.tuning = False
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self.tuning_logger = None
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self._setup_tuning_logger()
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@property
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@abstractmethod
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def type(self) -> str:
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"""The type of the specialist"""
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pass
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def _setup_tuning_logger(self):
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try:
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self.tuning_logger = TuningLogger(
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'tuning',
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tenant_id=self.tenant_id,
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specialist_id=self.specialist_id,
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)
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# Verify logger is working with a test message
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if self.tuning:
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self.tuning_logger.log_tuning('specialist', "Tuning logger initialized")
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except Exception as e:
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current_app.logger.error(f"Failed to setup tuning logger: {str(e)}")
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raise
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def _log_tuning(self, message: str, data: Dict[str, Any] = None) -> None:
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if self.tuning and self.tuning_logger:
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try:
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self.tuning_logger.log_tuning('specialist', message, data)
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except Exception as e:
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current_app.logger.error(f"Processor: Error in tuning logging: {e}")
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@abstractmethod
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def execute(self, arguments: SpecialistArguments) -> SpecialistResult:
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"""Execute the specialist's logic"""
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pass
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