Source code for langchain.agents.chat.output_parser
import json
import re
from typing import Pattern, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_ACTION = "Final Answer:"
[docs]class ChatOutputParser(AgentOutputParser):
"""Output parser for the chat agent."""
format_instructions: str = FORMAT_INSTRUCTIONS
"""Default formatting instructions"""
pattern: Pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL)
"""Regex pattern to parse the output."""
[docs] def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse the output from the agent into
an AgentAction or AgentFinish object.
Args:
text: The text to parse.
Returns:
An AgentAction or AgentFinish object.
Raises:
OutputParserException: If the output could not be parsed.
ValueError: If the action could not be found.
"""
includes_answer = FINAL_ANSWER_ACTION in text
try:
found = self.pattern.search(text)
if not found:
# Fast fail to parse Final Answer.
raise ValueError("action not found")
action = found.group(1)
response = json.loads(action.strip())
includes_action = "action" in response
if includes_answer and includes_action:
raise OutputParserException(
"Parsing LLM output produced a final answer "
f"and a parse-able action: {text}"
)
return AgentAction(
response["action"], response.get("action_input", {}), text
)
except Exception as exc:
if not includes_answer:
raise OutputParserException(
f"Could not parse LLM output: {text}"
) from exc
output = text.split(FINAL_ANSWER_ACTION)[-1].strip()
return AgentFinish({"output": output}, text)
@property
def _type(self) -> str:
return "chat"