Source code for langchain.agents.output_parsers.openai_functions
import json
from json import JSONDecodeError
from typing import Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import ChatGeneration, Generation
from langchain.agents.agent import AgentOutputParser
[docs]
class OpenAIFunctionsAgentOutputParser(AgentOutputParser):
"""Parses a message into agent action/finish.
Is meant to be used with OpenAI models, as it relies on the specific
function_call parameter from OpenAI to convey what tools to use.
If a function_call parameter is passed, then that is used to get
the tool and tool input.
If one is not passed, then the AIMessage is assumed to be the final output.
"""
@property
def _type(self) -> str:
return "openai-functions-agent"
@staticmethod
def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
"""Parse an AI message."""
if not isinstance(message, AIMessage):
msg = f"Expected an AI message got {type(message)}"
raise TypeError(msg)
function_call = message.additional_kwargs.get("function_call", {})
if function_call:
function_name = function_call["name"]
try:
if len(function_call["arguments"].strip()) == 0:
# OpenAI returns an empty string for functions containing no args
_tool_input = {}
else:
# otherwise it returns a json object
_tool_input = json.loads(function_call["arguments"], strict=False)
except JSONDecodeError as e:
msg = (
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
)
raise OutputParserException(msg) from e
# HACK HACK HACK:
# The code that encodes tool input into Open AI uses a special variable
# name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input.
# We unpack the argument here if it exists.
# Open AI does not support passing in a JSON array as an argument.
if "__arg1" in _tool_input:
tool_input = _tool_input["__arg1"]
else:
tool_input = _tool_input
content_msg = f"responded: {message.content}\n" if message.content else "\n"
log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n"
return AgentActionMessageLog(
tool=function_name,
tool_input=tool_input,
log=log,
message_log=[message],
)
return AgentFinish(
return_values={"output": message.content},
log=str(message.content),
)
[docs]
def parse_result(
self,
result: list[Generation],
*,
partial: bool = False,
) -> Union[AgentAction, AgentFinish]:
if not isinstance(result[0], ChatGeneration):
msg = "This output parser only works on ChatGeneration output"
raise ValueError(msg)
message = result[0].message
return self._parse_ai_message(message)
[docs]
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
msg = "Can only parse messages"
raise ValueError(msg)