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41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
from __future__ import annotations
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import json
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from typing import Union
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from langchain.agents import AgentOutputParser
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from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS
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from langchain.schema import AgentAction, AgentFinish, OutputParserException
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class ConvoOutputParser(AgentOutputParser):
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def get_format_instructions(self) -> str:
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return FORMAT_INSTRUCTIONS
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def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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try:
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cleaned_output = text.strip()
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if "```json" in cleaned_output:
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_, cleaned_output = cleaned_output.split("```json")
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if "```" in cleaned_output:
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cleaned_output, _ = cleaned_output.split("```")
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if cleaned_output.startswith("```json"):
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cleaned_output = cleaned_output[len("```json") :]
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if cleaned_output.startswith("```"):
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cleaned_output = cleaned_output[len("```") :]
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if cleaned_output.endswith("```"):
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cleaned_output = cleaned_output[: -len("```")]
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cleaned_output = cleaned_output.strip()
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response = json.loads(cleaned_output)
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action, action_input = response["action"], response["action_input"]
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if action == "Final Answer":
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return AgentFinish({"output": action_input}, text)
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else:
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return AgentAction(action, action_input, text)
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except Exception as e:
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raise OutputParserException(f"Could not parse LLM output: {text}") from e
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@property
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def _type(self) -> str:
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return "conversational_chat"
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