Source code for langchain.agents.conversational_chat.output_parser

from __future__ import annotations

from typing import Union

from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.utils.json import parse_json_markdown

from langchain.agents import AgentOutputParser
from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS


# Define a class that parses output for conversational agents
class ConvoOutputParser(AgentOutputParser):
    """对话代理的输出解析器。"""

    format_instructions: str = FORMAT_INSTRUCTIONS
    """默认格式指令"""

    def get_format_instructions(self) -> str:
        """返回给定输出解析器的格式化指令。"""
        return self.format_instructions

    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
        """尝试将给定文本解析为AgentAction或AgentFinish。

如果解析失败，则引发OutputParserException。
"""
        try:
            # Attempt to parse the text into a structured format (assumed to be JSON
            # stored as markdown)
            response = parse_json_markdown(text)

            # If the response contains an 'action' and 'action_input'
            if "action" in response and "action_input" in response:
                action, action_input = response["action"], response["action_input"]

                # If the action indicates a final answer, return an AgentFinish
                if action == "Final Answer":
                    return AgentFinish({"output": action_input}, text)
                else:
                    # Otherwise, return an AgentAction with the specified action and
                    # input
                    return AgentAction(action, action_input, text)
            else:
                # If the necessary keys aren't present in the response, raise an
                # exception
                raise OutputParserException(
                    f"Missing 'action' or 'action_input' in LLM output: {text}"
                )
        except Exception as e:
            # If any other exception is raised during parsing, also raise an
            # OutputParserException
            raise OutputParserException(f"Could not parse LLM output: {text}") from e

    @property
    def _type(self) -> str:
        return "conversational_chat"