initial commit

venv/Lib/site-packages/langchain_community/utilities/opaqueprompts.py

@@ -0,0 +1,102 @@
+from typing import Dict, Union
+
+
+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)}")
+
+
+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