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2bf924b732
* moving file into utilities * creating Logger and RPMController * Adding support for RPM to agents and crew
81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
import re
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from typing import Union
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from langchain.agents.output_parsers import ReActSingleInputOutputParser
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from langchain_core.agents import AgentAction, AgentFinish
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from crewai.agents.cache import CacheHandler, CacheHit
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from crewai.agents.exceptions import TaskRepeatedUsageException
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from crewai.agents.tools_handler import ToolsHandler
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from crewai.utilities import I18N
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FINAL_ANSWER_ACTION = "Final Answer:"
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FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
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"Parsing LLM output produced both a final answer and a parse-able action:"
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)
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class CrewAgentOutputParser(ReActSingleInputOutputParser):
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"""Parses ReAct-style LLM calls that have a single tool input.
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Expects output to be in one of two formats.
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If the output signals that an action should be taken,
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should be in the below format. This will result in an AgentAction
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being returned.
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```
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Thought: agent thought here
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Action: search
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Action Input: what is the temperature in SF?
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```
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If the output signals that a final answer should be given,
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should be in the below format. This will result in an AgentFinish
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being returned.
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```
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Thought: agent thought here
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Final Answer: The temperature is 100 degrees
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```
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It also prevents tools from being reused in a roll.
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"""
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class Config:
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arbitrary_types_allowed = True
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tools_handler: ToolsHandler
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cache: CacheHandler
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i18n: I18N
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def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
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FINAL_ANSWER_ACTION in text
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regex = (
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r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
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)
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if action_match := re.search(regex, text, re.DOTALL):
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action = action_match.group(1).strip()
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action_input = action_match.group(2)
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tool_input = action_input.strip(" ")
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tool_input = tool_input.strip('"')
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if last_tool_usage := self.tools_handler.last_used_tool:
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usage = {
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"tool": action,
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"input": tool_input,
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}
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if usage == last_tool_usage:
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raise TaskRepeatedUsageException(
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text=text,
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tool=action,
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tool_input=tool_input,
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i18n=self.i18n,
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)
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if self.cache.read(action, tool_input):
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action = AgentAction(action, tool_input, text)
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return CacheHit(action=action, cache=self.cache)
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return super().parse(text)
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