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from __future__ import annotations
import asyncio
from typing import TYPE_CHECKING, Any, Generic, Iterator, Literal, Optional, TypeVar
from pydantic import BaseModel, Field
from pydantic.generics import GenericModel
from autogpt.agents.base import BaseAgentActionProposal
from autogpt.models.utils import ModelWithSummary
from autogpt.processing.text import summarize_text
from autogpt.prompts.utils import format_numbered_list, indent
if TYPE_CHECKING:
from autogpt.config.config import Config
from autogpt.core.resource.model_providers import ChatModelProvider
class ActionSuccessResult(BaseModel):
outputs: Any
status: Literal["success"] = "success"
def __str__(self) -> str:
outputs = str(self.outputs).replace("```", r"\```")
multiline = "\n" in outputs
return f"```\n{self.outputs}\n```" if multiline else str(self.outputs)
class ErrorInfo(BaseModel):
args: tuple
message: str
exception_type: str
repr: str
@staticmethod
def from_exception(exception: Exception) -> ErrorInfo:
return ErrorInfo(
args=exception.args,
message=getattr(exception, "message", exception.args[0]),
exception_type=exception.__class__.__name__,
repr=repr(exception),
)
def __str__(self):
return repr(self)
def __repr__(self):
return self.repr
class ActionErrorResult(BaseModel):
reason: str
error: Optional[ErrorInfo] = None
status: Literal["error"] = "error"
@staticmethod
def from_exception(exception: Exception) -> ActionErrorResult:
return ActionErrorResult(
reason=getattr(exception, "message", exception.args[0]),
error=ErrorInfo.from_exception(exception),
)
def __str__(self) -> str:
return f"Action failed: '{self.reason}'"
class ActionInterruptedByHuman(BaseModel):
feedback: str
status: Literal["interrupted_by_human"] = "interrupted_by_human"
def __str__(self) -> str:
return (
'The user interrupted the action with the following feedback: "%s"'
% self.feedback
)
ActionResult = ActionSuccessResult | ActionErrorResult | ActionInterruptedByHuman
AP = TypeVar("AP", bound=BaseAgentActionProposal)
class Episode(GenericModel, Generic[AP]):
action: AP
result: ActionResult | None
summary: str | None = None
def format(self):
step = f"Executed `{self.action.use_tool}`\n"
reasoning = (
_r.summary()
if isinstance(_r := self.action.thoughts, ModelWithSummary)
else _r
)
step += f'- **Reasoning:** "{reasoning}"\n'
step += (
"- **Status:** "
f"`{self.result.status if self.result else 'did_not_finish'}`\n"
)
if self.result:
if self.result.status == "success":
result = str(self.result)
result = "\n" + indent(result) if "\n" in result else result
step += f"- **Output:** {result}"
elif self.result.status == "error":
step += f"- **Reason:** {self.result.reason}\n"
if self.result.error:
step += f"- **Error:** {self.result.error}\n"
elif self.result.status == "interrupted_by_human":
step += f"- **Feedback:** {self.result.feedback}\n"
return step
def __str__(self) -> str:
executed_action = f"Executed `{self.action.use_tool}`"
action_result = f": {self.result}" if self.result else "."
return executed_action + action_result
class EpisodicActionHistory(GenericModel, Generic[AP]):
"""Utility container for an action history"""
episodes: list[Episode[AP]] = Field(default_factory=list)
cursor: int = 0
_lock = asyncio.Lock()
@property
def current_episode(self) -> Episode[AP] | None:
if self.cursor == len(self):
return None
return self[self.cursor]
def __getitem__(self, key: int) -> Episode[AP]:
return self.episodes[key]
def __iter__(self) -> Iterator[Episode[AP]]:
return iter(self.episodes)
def __len__(self) -> int:
return len(self.episodes)
def __bool__(self) -> bool:
return len(self.episodes) > 0
def register_action(self, action: AP) -> None:
if not self.current_episode:
self.episodes.append(Episode(action=action, result=None))
assert self.current_episode
elif self.current_episode.action:
raise ValueError("Action for current cycle already set")
def register_result(self, result: ActionResult) -> None:
if not self.current_episode:
raise RuntimeError("Cannot register result for cycle without action")
elif self.current_episode.result:
raise ValueError("Result for current cycle already set")
self.current_episode.result = result
self.cursor = len(self.episodes)
def rewind(self, number_of_episodes: int = 0) -> None:
"""Resets the history to an earlier state.
Params:
number_of_cycles (int): The number of cycles to rewind. Default is 0.
When set to 0, it will only reset the current cycle.
"""
# Remove partial record of current cycle
if self.current_episode:
if self.current_episode.action and not self.current_episode.result:
self.episodes.pop(self.cursor)
# Rewind the specified number of cycles
if number_of_episodes > 0:
self.episodes = self.episodes[:-number_of_episodes]
self.cursor = len(self.episodes)
async def handle_compression(
self, llm_provider: ChatModelProvider, app_config: Config
) -> None:
"""Compresses each episode in the action history using an LLM.
This method iterates over all episodes in the action history without a summary,
and generates a summary for them using an LLM.
"""
compress_instruction = (
"The text represents an action, the reason for its execution, "
"and its result. "
"Condense the action taken and its result into one line. "
"Preserve any specific factual information gathered by the action."
)
async with self._lock:
# Gather all episodes without a summary
episodes_to_summarize = [ep for ep in self.episodes if ep.summary is None]
# Parallelize summarization calls
summarize_coroutines = [
summarize_text(
episode.format(),
instruction=compress_instruction,
llm_provider=llm_provider,
config=app_config,
)
for episode in episodes_to_summarize
]
summaries = await asyncio.gather(*summarize_coroutines)
# Assign summaries to episodes
for episode, (summary, _) in zip(episodes_to_summarize, summaries):
episode.summary = summary
def fmt_list(self) -> str:
return format_numbered_list(self.episodes)
def fmt_paragraph(self) -> str:
steps: list[str] = []
for i, episode in enumerate(self.episodes, 1):
step = f"### Step {i}: {episode.format()}\n"
steps.append(step)
return "\n\n".join(steps)
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