Spaces:
Paused
Paused
File size: 3,944 Bytes
dbd2ac6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
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"
|