mirror of
https://github.com/csunny/DB-GPT.git
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158 lines
6.2 KiB
Python
158 lines
6.2 KiB
Python
"""This is an auto-generated model file
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You can define your own models and DAOs here
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"""
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import json
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import uuid
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from datetime import datetime
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from typing import Any, Dict, Union
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from sqlalchemy import Column, DateTime, Integer, String, Text, UniqueConstraint
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from dbgpt.agent.core.schema import Status
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from dbgpt.storage.metadata import BaseDao, Model
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from ..api.schemas import EvaluateServeRequest, EvaluateServeResponse
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from ..config import ServeConfig
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class ServeEntity(Model):
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__tablename__ = "evaluate_manage"
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__table_args__ = (
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UniqueConstraint(
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"evaluate_code",
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name="uk_evaluate_code",
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),
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)
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id = Column(Integer, primary_key=True, comment="Auto increment id")
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evaluate_code = Column(String(256), comment="evaluate Code")
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scene_key = Column(String(100), comment="evaluate scene key")
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scene_value = Column(String(256), comment="evaluate scene value")
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context = Column(Text, comment="evaluate scene run context")
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evaluate_metrics = Column(String(599), comment="evaluate metrics")
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datasets_name = Column(String(256), comment="datasets name")
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datasets = Column(Text, comment="datasets")
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storage_type = Column(String(256), comment="datasets storage type")
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parallel_num = Column(Integer, comment="datasets run parallel num")
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state = Column(String(100), comment="evaluate state")
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result = Column(Text, comment="evaluate result")
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log_info = Column(Text, comment="evaluate log info")
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average_score = Column(Text, comment="evaluate average score")
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user_id = Column(String(100), index=True, nullable=True, comment="User id")
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user_name = Column(String(128), index=True, nullable=True, comment="User name")
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sys_code = Column(String(128), index=True, nullable=True, comment="System code")
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gmt_create = Column(DateTime, default=datetime.now, comment="Record creation time")
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gmt_modified = Column(
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DateTime,
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default=datetime.now,
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onupdate=datetime.now,
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comment="Record update time",
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)
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def __repr__(self):
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return f"ServeEntity(id={self.id}, evaluate_code='{self.evaluate_code}', scene_key='{self.scene_key}', scene_value='{self.scene_value}', datasets='{self.datasets}', user_id='{self.user_id}', user_name='{self.user_name}', sys_code='{self.sys_code}', gmt_created='{self.gmt_created}', gmt_modified='{self.gmt_modified}')"
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class ServeDao(BaseDao[ServeEntity, EvaluateServeRequest, EvaluateServeResponse]):
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"""The DAO class for Prompt"""
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def __init__(self, serve_config: ServeConfig):
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super().__init__()
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self._serve_config = serve_config
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def from_request(
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self, request: Union[EvaluateServeRequest, Dict[str, Any]]
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) -> ServeEntity:
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"""Convert the request to an entity
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Args:
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request (Union[EvaluateServeRequest, Dict[str, Any]]): The request
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Returns:
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T: The entity
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"""
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request_dict = (
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request.dict() if isinstance(request, EvaluateServeRequest) else request
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)
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entity = ServeEntity(
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evaluate_code=request_dict.get("evaluate_code", None),
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scene_key=request_dict.get("scene_key", None),
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scene_value=request_dict.get("scene_value", None),
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context=json.dumps(request_dict.get("context", None))
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if request_dict.get("context", None)
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else None,
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evaluate_metrics=request_dict.get("evaluate_metrics", None),
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datasets_name=request_dict.get("datasets_name", None),
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datasets=request_dict.get("datasets", None),
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storage_type=request_dict.get("storage_type", None),
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parallel_num=request_dict.get("parallel_num", 1),
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state=request_dict.get("state", Status.TODO.value),
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result=request_dict.get("result", None),
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average_score=request_dict.get("average_score", None),
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log_info=request_dict.get("log_info", None),
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user_id=request_dict.get("user_id", None),
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user_name=request_dict.get("user_name", None),
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sys_code=request_dict.get("sys_code", None),
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)
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if not entity.evaluate_code:
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entity.evaluate_code = uuid.uuid1().hex
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return entity
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def to_request(self, entity: ServeEntity) -> EvaluateServeRequest:
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"""Convert the entity to a request
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Args:
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entity (T): The entity
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Returns:
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REQ: The request
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"""
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return EvaluateServeRequest(
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evaluate_code=entity.evaluate_code,
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scene_key=entity.scene_key,
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scene_value=entity.scene_value,
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datasets_name=entity.datasets_name,
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datasets=entity.datasets,
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storage_type=entity.storage_type,
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evaluate_metrics=entity.evaluate_metrics,
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context=json.loads(entity.context) if entity.context else None,
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user_name=entity.user_name,
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user_id=entity.user_id,
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sys_code=entity.sys_code,
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state=entity.state,
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result=entity.result,
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average_score=entity.average_score,
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log_info=entity.log_info,
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)
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def to_response(self, entity: ServeEntity) -> EvaluateServeResponse:
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"""Convert the entity to a response
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Args:
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entity (T): The entity
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Returns:
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RES: The response
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"""
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gmt_created_str = entity.gmt_create.strftime("%Y-%m-%d %H:%M:%S")
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gmt_modified_str = entity.gmt_modified.strftime("%Y-%m-%d %H:%M:%S")
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return EvaluateServeResponse(
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evaluate_code=entity.evaluate_code,
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scene_key=entity.scene_key,
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scene_value=entity.scene_value,
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datasets_name=entity.datasets_name,
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datasets=entity.datasets,
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storage_type=entity.storage_type,
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evaluate_metrics=entity.evaluate_metrics,
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context=json.loads(entity.context) if entity.context else None,
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user_name=entity.user_name,
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user_id=entity.user_id,
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sys_code=entity.sys_code,
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state=entity.state,
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result=entity.result,
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average_score=entity.average_score,
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log_info=entity.log_info,
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gmt_create=gmt_created_str,
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gmt_modified=gmt_modified_str,
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)
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