actions.py

# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
from typing import Optional

from langchain.llms.base import BaseLLM

from nemoguardrails.actions.actions import ActionResult, action
from nemoguardrails.actions.llm.utils import llm_call
from nemoguardrails.context import llm_call_info_var
from nemoguardrails.llm.params import llm_params
from nemoguardrails.llm.taskmanager import LLMTaskManager
from nemoguardrails.llm.types import Task
from nemoguardrails.logging.explain import LLMCallInfo
from nemoguardrails.utils import new_event_dict

log = logging.getLogger(__name__)


@action(is_system_action=True)
async def self_check_input(
    llm_task_manager: LLMTaskManager,
    context: Optional[dict] = None,
    llm: Optional[BaseLLM] = None,
):
    """Checks the input from the user.

    Prompt the LLM, using the `check_input` task prompt, to determine if the input
    from the user should be allowed or not.

    Returns:
        True if the input should be allowed, False otherwise.
    """

    user_input = context.get("user_message")

    if user_input:
        prompt = llm_task_manager.render_task_prompt(
            task=Task.SELF_CHECK_INPUT,
            context={
                "user_input": user_input,
            },
        )

        # Initialize the LLMCallInfo object
        llm_call_info_var.set(LLMCallInfo(task=Task.SELF_CHECK_INPUT.value))

        with llm_params(llm, temperature=0.0):
            check = await llm_call(llm, prompt)

        check = check.lower().strip()
        log.info(f"Input self-checking result is: `{check}`.")

        if "yes" in check:
            return ActionResult(
                return_value=False,
                events=[
                    new_event_dict(
                        "mask_prev_user_message", intent="unanswerable message"
                    )
                ],
            )

    return True