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https://github.com/crewAIInc/crewAI.git
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6b054651a7
* Refactoring task cache to be a tool The previous implementation of the task caching system was early exiting the agent executor due to the fact it was returning an AgentFinish object. This now refactors it to use a cache specific tool that is dynamically added and forced into the agent in case of a task execution that was already executed with the same input.
80 lines
2.5 KiB
Python
80 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 langchain_core.exceptions import OutputParserException
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from .cache_handler import CacheHandler
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from .cache_hit import CacheHit
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from .tools_handler import ToolsHandler
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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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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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action_match = re.search(regex, text, re.DOTALL)
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if action_match:
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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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last_tool_usage = self.tools_handler.last_used_tool
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if last_tool_usage:
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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 OutputParserException(
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f"""\nI just used the {action} tool with input {tool_input}. So I already know the result of that."""
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)
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result = self.cache.read(action, tool_input)
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if result:
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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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