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af9e749edb
* Adding tool caching a loop execution prevention. This adds some guardrails, to both prevent the same tool to be used consecutively and also caching tool's results across the entire crew so it cuts down execution time and eventual LLM calls. This plays a huge role for smaller opensource models that usually fall into those behaviors patterns. It also includes some smaller improvements around the tool prompt and agent tools, all with the same intention of guiding models into better conform with agent instructions.
82 lines
2.6 KiB
Python
82 lines
2.6 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 .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]:
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includes_answer = 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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if includes_answer:
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raise OutputParserException(
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f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
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)
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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 knwo 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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return AgentFinish({"output": result}, text)
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return super().parse(text)
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