83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
from __future__ import annotations
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from typing import Any
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from pydantic import BaseModel
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from skyvern.forge.sdk.settings_manager import SettingsManager
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from skyvern.webeye.actions.actions import Action, DecisiveAction, UserDefinedError
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from skyvern.webeye.actions.responses import ActionResult
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from skyvern.webeye.scraper.scraper import ScrapedPage
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class AgentStepOutput(BaseModel):
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"""
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Output of the agent step, this is recorded in the database.
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"""
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# Will be deprecated once we move to the new format below
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action_results: list[ActionResult] | None = None
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# Nullable for backwards compatibility, once backfill is done, this won't be nullable anymore
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actions_and_results: list[tuple[Action, list[ActionResult]]] | None = None
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errors: list[UserDefinedError] = []
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def __repr__(self) -> str:
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return f"AgentStepOutput({self.model_dump()})"
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def __str__(self) -> str:
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return self.__repr__()
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class DetailedAgentStepOutput(BaseModel):
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"""
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Output of the agent step, this is not recorded in the database, only used for debugging in the Jupyter notebook.
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"""
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scraped_page: ScrapedPage | None
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extract_action_prompt: str | None
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llm_response: dict[str, Any] | None
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actions: list[Action] | None
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action_results: list[ActionResult] | None
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actions_and_results: list[tuple[Action, list[ActionResult]]] | None
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class Config:
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exclude = ["scraped_page", "extract_action_prompt"]
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def __repr__(self) -> str:
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if SettingsManager.get_settings().DEBUG_MODE:
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return f"DetailedAgentStepOutput({self.model_dump()})"
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else:
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return f"AgentStepOutput({self.to_agent_step_output().model_dump()})"
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def __str__(self) -> str:
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return self.__repr__()
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def extract_errors(self) -> list[UserDefinedError]:
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errors = []
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if self.actions_and_results:
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for action, action_results in self.actions_and_results:
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if isinstance(action, DecisiveAction):
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errors.extend(action.errors)
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return errors
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def get_clean_detailed_output(self) -> DetailedAgentStepOutput:
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return DetailedAgentStepOutput(
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scraped_page=self.scraped_page,
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extract_action_prompt=self.extract_action_prompt,
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llm_response=self.llm_response,
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actions=self.actions,
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action_results=self.action_results,
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actions_and_results=None
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if self.actions_and_results is None
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else [(action, result) for action, result in self.actions_and_results if result],
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)
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def to_agent_step_output(self) -> AgentStepOutput:
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clean_output = self.get_clean_detailed_output()
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return AgentStepOutput(
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action_results=clean_output.action_results if clean_output.action_results else [],
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actions_and_results=(clean_output.actions_and_results if clean_output.actions_and_results else []),
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errors=clean_output.extract_errors(),
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)
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