src/output_parser.py

import re
from typing import Union

from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.output_parser import MRKLOutputParser
#from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException

FORMAT_INSTRUCTIONS0 = """Use the following format and be sure to use new lines after each task.

Question: the input question you must answer

Thought: you should always think about what to do

Action: Exactly only one word out of: {tool_names}

Action Input: the input to the action

Observation: the result of the action

... (this Thought/Action/Action Input/Observation can repeat N times)

Thought: I now know the final answer

Final Answer: the final answer to the original input question"""

FORMAT_INSTRUCTIONS = """List of tools, use exactly one word when choosing Action: {tool_names}

Here is an example sequence to follow:
Question: What is the latest news?
Thought: I should search online for the latest news.
Action: Search
Action Input: What is the latest news?
Observation: X is going away.  Z is again happening.
Thought: That is interesting, I should search for more information about X and Z and also search about Q.
Action: Search
Action Input: How is X impacting things.  Why is Z happening again, and what are the consequences?
Observation: X is causing Y.  Z may be caused by P and will lead to H.
Thought: I now know the final answer
Final Answer: The latest news is:
* X is going away, and this is caused by Y.
* Z is happening again, and the cause is P and will lead to H.
Overall, X and Z are important problems.
"""

FINAL_ANSWER_ACTION = "Final Answer:"
MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = (
    "Invalid Format: Missing 'Action:' after 'Thought:"
)
MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = (
    "Invalid Format: Missing 'Action Input:' after 'Action:'"
)
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
    "Parsing LLM output produced both a final answer and a parse-able action:"
)


class H2OMRKLOutputParser(MRKLOutputParser):
    """MRKL Output parser for the chat agent."""

    def get_format_instructions(self) -> str:
        return FORMAT_INSTRUCTIONS

    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 = re.search(regex, text, re.DOTALL)
        if includes_answer:
            return AgentFinish(
                {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
            )
        elif action_match:
            action = action_match.group(1).strip()
            action_input = action_match.group(2)
            tool_input = action_input.strip(" ")
            # ensure if its a well formed SQL query we don't remove any trailing " chars
            if tool_input.startswith("SELECT ") is False:
                tool_input = tool_input.strip('"')

            return AgentAction(action, tool_input, text)

        if not re.search(r"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 re.search(
            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 "mrkl"