test_openai_functions.py — langchain Source File

Source Code

import json
from typing import Any

import pytest
from pydantic import BaseModel

from langchain_core.exceptions import OutputParserException
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.output_parsers.openai_functions import (
    JsonOutputFunctionsParser,
    PydanticOutputFunctionsParser,
)
from langchain_core.outputs import ChatGeneration


def test_json_output_function_parser() -> None:
    """Test the JSON output function parser is configured with robust defaults."""
    message = AIMessage(
        content="This is a test message",
        additional_kwargs={
            "function_call": {
                "name": "function_name",
                "arguments": '{"arg1": "code\ncode"}',
            }
        },
    )
    chat_generation = ChatGeneration(message=message)

    # Full output
    # Test that the parsers defaults are configured to parse in non-strict mode
    parser = JsonOutputFunctionsParser(args_only=False)
    result = parser.parse_result([chat_generation])
    assert result == {"arguments": {"arg1": "code\ncode"}, "name": "function_name"}

    # Args only
    parser = JsonOutputFunctionsParser(args_only=True)
    result = parser.parse_result([chat_generation])
    assert result == {"arg1": "code\ncode"}

    # Verify that the original message is not modified
    assert message.additional_kwargs == {
        "function_call": {
            "name": "function_name",
            "arguments": '{"arg1": "code\ncode"}',
        }
    }


@pytest.mark.parametrize(
    "config",
    [
        {
            "args_only": False,
            "strict": False,
            "args": '{"arg1": "value1"}',
            "result": {"arguments": {"arg1": "value1"}, "name": "function_name"},
            "exception": None,
        },
        {
            "args_only": True,
// ... (137 more lines)