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from __future__ import annotations
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents import AgentOutputParser
from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_json_markdown
# Define a class that parses output for conversational agents
class ConvoOutputParser(AgentOutputParser):
"""Output parser for the conversational agent."""
def get_format_instructions(self) -> str:
"""Returns formatting instructions for the given output parser."""
return FORMAT_INSTRUCTIONS
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Attempts to parse the given text into an AgentAction or AgentFinish.
Raises:
OutputParserException if parsing fails.
"""
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"
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