Source code for langchain_community.llms.opaqueprompts

import logging
from typing import Any, Dict, List, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.language_models.llms import LLM
from langchain_core.messages import AIMessage
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

logger = logging.getLogger(__name__)


[docs]class OpaquePrompts(LLM): """LLM that uses OpaquePrompts to sanitize prompts. Wraps another LLM and sanitizes prompts before passing it to the LLM, then de-sanitizes the response. To use, you should have the ``opaqueprompts`` python package installed, and the environment variable ``OPAQUEPROMPTS_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.llms import OpaquePrompts from langchain_community.chat_models import ChatOpenAI op_llm = OpaquePrompts(base_llm=ChatOpenAI()) """ base_llm: BaseLanguageModel """The base LLM to use.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validates that the OpaquePrompts API key and the Python package exist.""" try: import opaqueprompts as op except ImportError: raise ImportError( "Could not import the `opaqueprompts` Python package, " "please install it with `pip install opaqueprompts`." ) if op.__package__ is None: raise ValueError( "Could not properly import `opaqueprompts`, " "opaqueprompts.__package__ is None." ) api_key = get_from_dict_or_env( values, "opaqueprompts_api_key", "OPAQUEPROMPTS_API_KEY", default="" ) if not api_key: raise ValueError( "Could not find OPAQUEPROMPTS_API_KEY in the environment. " "Please set it to your OpaquePrompts API key." "You can get it by creating an account on the OpaquePrompts website: " "https://opaqueprompts.opaque.co/ ." ) return values def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call base LLM with sanitization before and de-sanitization after. Args: prompt: The prompt to pass into the model. Returns: The string generated by the model. Example: .. code-block:: python response = op_llm.invoke("Tell me a joke.") """ import opaqueprompts as op _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager() # sanitize the prompt by replacing the sensitive information with a placeholder sanitize_response: op.SanitizeResponse = op.sanitize([prompt]) sanitized_prompt_value_str = sanitize_response.sanitized_texts[0] # TODO: Add in callbacks once child runs for LLMs are supported by LangSmith. # call the LLM with the sanitized prompt and get the response llm_response = self.base_llm.bind(stop=stop).invoke( sanitized_prompt_value_str, ) if isinstance(llm_response, AIMessage): llm_response = llm_response.content # desanitize the response by restoring the original sensitive information desanitize_response: op.DesanitizeResponse = op.desanitize( llm_response, secure_context=sanitize_response.secure_context, ) return desanitize_response.desanitized_text @property def _llm_type(self) -> str: """Return type of LLM. This is an override of the base class method. """ return "opaqueprompts"