Source code for langchain.agents.output_parsers.react_single_input

import re
from typing import Union

from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException

from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS

    "Invalid Format: Missing 'Action:' after 'Thought:"
    "Invalid Format: Missing 'Action Input:' after 'Action:'"
    "Parsing LLM output produced both a final answer and a parse-able action:"

[docs]class ReActSingleInputOutputParser(AgentOutputParser): """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 ``` """
[docs] def get_format_instructions(self) -> str: return FORMAT_INSTRUCTIONS
[docs] def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text regex = ( r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)" ) action_match =, text, re.DOTALL) if action_match: if includes_answer: raise OutputParserException( f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}" ) action = action_input = tool_input = action_input.strip(" ") tool_input = tool_input.strip('"') return AgentAction(action, tool_input, text) elif includes_answer: return AgentFinish( {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text ) if not"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL): raise OutputParserException( f"Could not parse LLM output: `{text}`", observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE, llm_output=text, send_to_llm=True, ) elif not r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL ): raise OutputParserException( f"Could not parse LLM output: `{text}`", observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE, llm_output=text, send_to_llm=True, ) else: raise OutputParserException(f"Could not parse LLM output: `{text}`")
@property def _type(self) -> str: return "react-single-input"