from __future__ import annotations
import json
from pathlib import Path
from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
import yaml
from langchain_experimental.data_anonymizer.base import (
DEFAULT_DEANONYMIZER_MATCHING_STRATEGY,
AnonymizerBase,
ReversibleAnonymizerBase,
)
from langchain_experimental.data_anonymizer.deanonymizer_mapping import (
DeanonymizerMapping,
MappingDataType,
create_anonymizer_mapping,
)
from langchain_experimental.data_anonymizer.deanonymizer_matching_strategies import (
exact_matching_strategy,
)
from langchain_experimental.data_anonymizer.faker_presidio_mapping import (
get_pseudoanonymizer_mapping,
)
if TYPE_CHECKING:
from presidio_analyzer import AnalyzerEngine, EntityRecognizer
from presidio_analyzer.nlp_engine import NlpEngineProvider
from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig
def _import_analyzer_engine() -> "AnalyzerEngine":
try:
from presidio_analyzer import AnalyzerEngine
except ImportError as e:
raise ImportError(
"Could not import presidio_analyzer, please install with "
"`pip install presidio-analyzer`. You will also need to download a "
"spaCy model to use the analyzer, e.g. "
"`python -m spacy download en_core_web_lg`."
) from e
return AnalyzerEngine
def _import_nlp_engine_provider() -> "NlpEngineProvider":
try:
from presidio_analyzer.nlp_engine import NlpEngineProvider
except ImportError as e:
raise ImportError(
"Could not import presidio_analyzer, please install with "
"`pip install presidio-analyzer`. You will also need to download a "
"spaCy model to use the analyzer, e.g. "
"`python -m spacy download en_core_web_lg`."
) from e
return NlpEngineProvider
def _import_anonymizer_engine() -> "AnonymizerEngine":
try:
from presidio_anonymizer import AnonymizerEngine
except ImportError as e:
raise ImportError(
"Could not import presidio_anonymizer, please install with "
"`pip install presidio-anonymizer`."
) from e
return AnonymizerEngine
def _import_operator_config() -> "OperatorConfig":
try:
from presidio_anonymizer.entities import OperatorConfig
except ImportError as e:
raise ImportError(
"Could not import presidio_anonymizer, please install with "
"`pip install presidio-anonymizer`."
) from e
return OperatorConfig
# Configuring Anonymizer for multiple languages
# Detailed description and examples can be found here:
# langchain/docs/extras/guides/privacy/multi_language_anonymization.ipynb
DEFAULT_LANGUAGES_CONFIG = {
# You can also use Stanza or transformers library.
# See https://microsoft.github.io/presidio/analyzer/customizing_nlp_models/
"nlp_engine_name": "spacy",
"models": [
{"lang_code": "en", "model_name": "en_core_web_lg"},
# {"lang_code": "de", "model_name": "de_core_news_md"},
# {"lang_code": "es", "model_name": "es_core_news_md"},
# ...
# List of available models: https://spacy.io/usage/models
],
}
[docs]class PresidioAnonymizerBase(AnonymizerBase):
[docs] def __init__(
self,
analyzed_fields: Optional[List[str]] = None,
operators: Optional[Dict[str, OperatorConfig]] = None,
languages_config: Dict = DEFAULT_LANGUAGES_CONFIG,
add_default_faker_operators: bool = True,
faker_seed: Optional[int] = None,
):
"""
Args:
analyzed_fields: List of fields to detect and then anonymize.
Defaults to all entities supported by Microsoft Presidio.
operators: Operators to use for anonymization.
Operators allow for custom anonymization of detected PII.
Learn more:
https://microsoft.github.io/presidio/tutorial/10_simple_anonymization/
languages_config: Configuration for the NLP engine.
First language in the list will be used as the main language
in self.anonymize(...) when no language is specified.
Learn more:
https://microsoft.github.io/presidio/analyzer/customizing_nlp_models/
faker_seed: Seed used to initialize faker.
Defaults to None, in which case faker will be seeded randomly
and provide random values.
"""
OperatorConfig = _import_operator_config()
AnalyzerEngine = _import_analyzer_engine()
NlpEngineProvider = _import_nlp_engine_provider()
AnonymizerEngine = _import_anonymizer_engine()
self.analyzed_fields = (
analyzed_fields
if analyzed_fields is not None
else list(get_pseudoanonymizer_mapping().keys())
)
if add_default_faker_operators:
self.operators = {
field: OperatorConfig(
operator_name="custom", params={"lambda": faker_function}
)
for field, faker_function in get_pseudoanonymizer_mapping(
faker_seed
).items()
}
else:
self.operators = {}
if operators:
self.add_operators(operators)
provider = NlpEngineProvider(nlp_configuration=languages_config)
nlp_engine = provider.create_engine()
self.supported_languages = list(nlp_engine.nlp.keys())
self._analyzer = AnalyzerEngine(
supported_languages=self.supported_languages, nlp_engine=nlp_engine
)
self._anonymizer = AnonymizerEngine()
[docs] def add_recognizer(self, recognizer: EntityRecognizer) -> None:
"""Add a recognizer to the analyzer
Args:
recognizer: Recognizer to add to the analyzer.
"""
self._analyzer.registry.add_recognizer(recognizer)
self.analyzed_fields.extend(recognizer.supported_entities)
[docs] def add_operators(self, operators: Dict[str, OperatorConfig]) -> None:
"""Add operators to the anonymizer
Args:
operators: Operators to add to the anonymizer.
"""
self.operators.update(operators)
[docs]class PresidioAnonymizer(PresidioAnonymizerBase):
def _anonymize(
self,
text: str,
language: Optional[str] = None,
allow_list: Optional[List[str]] = None,
) -> str:
"""Anonymize text.
Each PII entity is replaced with a fake value.
Each time fake values will be different, as they are generated randomly.
PresidioAnonymizer has no built-in memory -
so it will not remember the effects of anonymizing previous texts.
>>> anonymizer = PresidioAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Brett Russell. Hi Brett Russell!'
Args:
text: text to anonymize
language: language to use for analysis of PII
If None, the first (main) language in the list
of languages specified in the configuration will be used.
"""
if language is None:
language = self.supported_languages[0]
if language not in self.supported_languages:
raise ValueError(
f"Language '{language}' is not supported. "
f"Supported languages are: {self.supported_languages}. "
"Change your language configuration file to add more languages."
)
# Check supported entities for given language
# e.g. IT_FISCAL_CODE is not supported for English in Presidio by default
# If you want to use it, you need to add a recognizer manually
supported_entities = []
for recognizer in self._analyzer.get_recognizers(language):
recognizer_dict = recognizer.to_dict()
supported_entities.extend(
[recognizer_dict["supported_entity"]]
if "supported_entity" in recognizer_dict
else recognizer_dict["supported_entities"]
)
entities_to_analyze = list(
set(supported_entities).intersection(set(self.analyzed_fields))
)
analyzer_results = self._analyzer.analyze(
text,
entities=entities_to_analyze,
language=language,
allow_list=allow_list,
)
filtered_analyzer_results = (
self._anonymizer._remove_conflicts_and_get_text_manipulation_data(
analyzer_results
)
)
anonymizer_results = self._anonymizer.anonymize(
text,
analyzer_results=analyzer_results,
operators=self.operators,
)
anonymizer_mapping = create_anonymizer_mapping(
text,
filtered_analyzer_results,
anonymizer_results,
)
return exact_matching_strategy(text, anonymizer_mapping)
[docs]class PresidioReversibleAnonymizer(PresidioAnonymizerBase, ReversibleAnonymizerBase):
[docs] def __init__(
self,
analyzed_fields: Optional[List[str]] = None,
operators: Optional[Dict[str, OperatorConfig]] = None,
languages_config: Dict = DEFAULT_LANGUAGES_CONFIG,
add_default_faker_operators: bool = True,
faker_seed: Optional[int] = None,
):
super().__init__(
analyzed_fields,
operators,
languages_config,
add_default_faker_operators,
faker_seed,
)
self._deanonymizer_mapping = DeanonymizerMapping()
@property
def deanonymizer_mapping(self) -> MappingDataType:
"""Return the deanonymizer mapping"""
return self._deanonymizer_mapping.data
@property
def anonymizer_mapping(self) -> MappingDataType:
"""Return the anonymizer mapping
This is just the reverse version of the deanonymizer mapping."""
return {
key: {v: k for k, v in inner_dict.items()}
for key, inner_dict in self.deanonymizer_mapping.items()
}
def _anonymize(
self,
text: str,
language: Optional[str] = None,
allow_list: Optional[List[str]] = None,
) -> str:
"""Anonymize text.
Each PII entity is replaced with a fake value.
Each time fake values will be different, as they are generated randomly.
At the same time, we will create a mapping from each anonymized entity
back to its original text value.
Thanks to the built-in memory, all previously anonymised entities
will be remembered and replaced by the same fake values:
>>> anonymizer = PresidioReversibleAnonymizer()
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
>>> anonymizer.anonymize("My name is John Doe. Hi John Doe!")
'My name is Noah Rhodes. Hi Noah Rhodes!'
Args:
text: text to anonymize
language: language to use for analysis of PII
If None, the first (main) language in the list
of languages specified in the configuration will be used.
"""
if language is None:
language = self.supported_languages[0]
if language not in self.supported_languages:
raise ValueError(
f"Language '{language}' is not supported. "
f"Supported languages are: {self.supported_languages}. "
"Change your language configuration file to add more languages."
)
# Check supported entities for given language
# e.g. IT_FISCAL_CODE is not supported for English in Presidio by default
# If you want to use it, you need to add a recognizer manually
supported_entities = []
for recognizer in self._analyzer.get_recognizers(language):
recognizer_dict = recognizer.to_dict()
supported_entities.extend(
[recognizer_dict["supported_entity"]]
if "supported_entity" in recognizer_dict
else recognizer_dict["supported_entities"]
)
entities_to_analyze = list(
set(supported_entities).intersection(set(self.analyzed_fields))
)
analyzer_results = self._analyzer.analyze(
text,
entities=entities_to_analyze,
language=language,
allow_list=allow_list,
)
filtered_analyzer_results = (
self._anonymizer._remove_conflicts_and_get_text_manipulation_data(
analyzer_results
)
)
anonymizer_results = self._anonymizer.anonymize(
text,
analyzer_results=analyzer_results,
operators=self.operators,
)
new_deanonymizer_mapping = create_anonymizer_mapping(
text,
filtered_analyzer_results,
anonymizer_results,
is_reversed=True,
)
self._deanonymizer_mapping.update(new_deanonymizer_mapping)
return exact_matching_strategy(text, self.anonymizer_mapping)
def _deanonymize(
self,
text_to_deanonymize: str,
deanonymizer_matching_strategy: Callable[
[str, MappingDataType], str
] = DEFAULT_DEANONYMIZER_MATCHING_STRATEGY,
) -> str:
"""Deanonymize text.
Each anonymized entity is replaced with its original value.
This method exploits the mapping created during the anonymization process.
Args:
text_to_deanonymize: text to deanonymize
deanonymizer_matching_strategy: function to use to match
anonymized entities with their original values and replace them.
"""
if not self._deanonymizer_mapping:
raise ValueError(
"Deanonymizer mapping is empty.",
"Please call anonymize() and anonymize some text first.",
)
text_to_deanonymize = deanonymizer_matching_strategy(
text_to_deanonymize, self.deanonymizer_mapping
)
return text_to_deanonymize
[docs] def reset_deanonymizer_mapping(self) -> None:
"""Reset the deanonymizer mapping"""
self._deanonymizer_mapping = DeanonymizerMapping()
[docs] def save_deanonymizer_mapping(self, file_path: Union[Path, str]) -> None:
"""Save the deanonymizer mapping to a JSON or YAML file.
Args:
file_path: Path to file to save the mapping to.
Example:
.. code-block:: python
anonymizer.save_deanonymizer_mapping(file_path="path/mapping.json")
"""
save_path = Path(file_path)
if save_path.suffix not in [".json", ".yaml"]:
raise ValueError(f"{save_path} must have an extension of .json or .yaml")
# Make sure parent directories exist
save_path.parent.mkdir(parents=True, exist_ok=True)
if save_path.suffix == ".json":
with open(save_path, "w") as f:
json.dump(self.deanonymizer_mapping, f, indent=2)
elif save_path.suffix == ".yaml":
with open(save_path, "w") as f:
yaml.dump(self.deanonymizer_mapping, f, default_flow_style=False)
[docs] def load_deanonymizer_mapping(self, file_path: Union[Path, str]) -> None:
"""Load the deanonymizer mapping from a JSON or YAML file.
Args:
file_path: Path to file to load the mapping from.
Example:
.. code-block:: python
anonymizer.load_deanonymizer_mapping(file_path="path/mapping.json")
"""
load_path = Path(file_path)
if load_path.suffix not in [".json", ".yaml"]:
raise ValueError(f"{load_path} must have an extension of .json or .yaml")
if load_path.suffix == ".json":
with open(load_path, "r") as f:
loaded_mapping = json.load(f)
elif load_path.suffix == ".yaml":
with open(load_path, "r") as f:
loaded_mapping = yaml.load(f, Loader=yaml.FullLoader)
self._deanonymizer_mapping.update(loaded_mapping)