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import re
from typing import Union
from langchain.agents.output_parsers import ReActSingleInputOutputParser
from langchain_core.agents import AgentAction, AgentFinish
from .cache import CacheHandler, CacheHit
from .exceptions import TaskRepeatedUsageException
from .tools_handler import ToolsHandler
FINAL_ANSWER_ACTION = "Final Answer:"
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
"Parsing LLM output produced both a final answer and a parse-able action:"
)
class CrewAgentOutputParser(ReActSingleInputOutputParser):
"""Parses ReAct-style LLM calls that have a single tool input.
Expects output to be in one of two formats.
If the output signals that an action should be taken,
should be in the below format. This will result in an AgentAction
being returned.
```
Thought: agent thought here
Action: search
Action Input: what is the temperature in SF?
```
If the output signals that a final answer should be given,
should be in the below format. This will result in an AgentFinish
being returned.
```
Thought: agent thought here
Final Answer: The temperature is 100 degrees
```
It also prevents tools from being reused in a roll.
"""
class Config:
arbitrary_types_allowed = True
tools_handler: ToolsHandler
cache: CacheHandler
def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
FINAL_ANSWER_ACTION in text
regex = (
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
)
action_match = re.search(regex, text, re.DOTALL)
if action_match:
action = action_match.group(1).strip()
action_input = action_match.group(2)
tool_input = action_input.strip(" ")
tool_input = tool_input.strip('"')
last_tool_usage = self.tools_handler.last_used_tool
if last_tool_usage:
usage = {
"tool": action,
"input": tool_input,
}
if usage == last_tool_usage:
raise TaskRepeatedUsageException(tool=action, tool_input=tool_input)
result = self.cache.read(action, tool_input)
if result:
action = AgentAction(action, tool_input, text)
return CacheHit(action=action, cache=self.cache)
return super().parse(text)
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