Source code for langchain_community.utilities.opaqueprompts

from typing import Dict, Union


[docs]def sanitize( input: Union[str, Dict[str, str]], ) -> Dict[str, Union[str, Dict[str, str]]]: """ Sanitize input string or dict of strings by replacing sensitive data with placeholders. It returns the sanitized input string or dict of strings and the secure context as a dict following the format: { "sanitized_input": <sanitized input string or dict of strings>, "secure_context": <secure context> } The secure context is a bytes object that is needed to de-sanitize the response from the LLM. Args: input: Input string or dict of strings. Returns: Sanitized input string or dict of strings and the secure context as a dict following the format: { "sanitized_input": <sanitized input string or dict of strings>, "secure_context": <secure context> } The `secure_context` needs to be passed to the `desanitize` function. Raises: ValueError: If the input is not a string or dict of strings. ImportError: If the `opaqueprompts` Python package is not installed. """ try: import opaqueprompts as op except ImportError: raise ImportError( "Could not import the `opaqueprompts` Python package, " "please install it with `pip install opaqueprompts`." ) if isinstance(input, str): # the input could be a string, so we sanitize the string sanitize_response: op.SanitizeResponse = op.sanitize([input]) return { "sanitized_input": sanitize_response.sanitized_texts[0], "secure_context": sanitize_response.secure_context, } if isinstance(input, dict): # the input could be a dict[string, string], so we sanitize the values values = list() # get the values from the dict for key in input: values.append(input[key]) # sanitize the values sanitize_values_response: op.SanitizeResponse = op.sanitize(values) # reconstruct the dict with the sanitized values sanitized_input_values = sanitize_values_response.sanitized_texts idx = 0 sanitized_input = dict() for key in input: sanitized_input[key] = sanitized_input_values[idx] idx += 1 return { "sanitized_input": sanitized_input, "secure_context": sanitize_values_response.secure_context, } raise ValueError(f"Unexpected input type {type(input)}")
[docs]def desanitize(sanitized_text: str, secure_context: bytes) -> str: """ Restore the original sensitive data from the sanitized text. Args: sanitized_text: Sanitized text. secure_context: Secure context returned by the `sanitize` function. Returns: De-sanitized text. """ try: import opaqueprompts as op except ImportError: raise ImportError( "Could not import the `opaqueprompts` Python package, " "please install it with `pip install opaqueprompts`." ) desanitize_response: op.DesanitizeResponse = op.desanitize( sanitized_text, secure_context ) return desanitize_response.desanitized_text