--- title: PII Masking | LlamaIndex OSS Documentation --- If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙. ``` %pip install llama-index-llms-openai %pip install llama-index-llms-huggingface ``` ``` !pip install llama-index ``` ``` import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) from llama_index.core.postprocessor import ( PIINodePostprocessor, NERPIINodePostprocessor, ) from llama_index.llms.huggingface import HuggingFaceLLM from llama_index.core import Document, VectorStoreIndex from llama_index.core.schema import TextNode ``` ``` INFO:numexpr.utils:Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. Note: NumExpr detected 16 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. INFO:numexpr.utils:NumExpr defaulting to 8 threads. NumExpr defaulting to 8 threads. /home/loganm/miniconda3/envs/llama-index/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm ``` ``` # load documents text = """ Hello Paulo Santos. The latest statement for your credit card account \ 1111-0000-1111-0000 was mailed to 123 Any Street, Seattle, WA 98109. """ node = TextNode(text=text) ``` ### Option 1: Use NER Model for PII Masking Use a Hugging Face NER model for PII Masking ``` processor = NERPIINodePostprocessor() ``` ``` from llama_index.core.schema import NodeWithScore new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)]) ``` ``` No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english). Using a pipeline without specifying a model name and revision in production is not recommended. /home/loganm/miniconda3/envs/llama-index/lib/python3.11/site-packages/transformers/pipelines/token_classification.py:169: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy="AggregationStrategy.SIMPLE"` instead. warnings.warn( ``` ``` # view redacted text new_nodes[0].node.get_text() ``` ``` 'Hello [ORG_6]. The latest statement for your credit card account 1111-0000-1111-0000 was mailed to 123 [ORG_108] [LOC_112], [LOC_120], [LOC_129] 98109.' ``` ``` # get mapping in metadata # NOTE: this is not sent to the LLM! new_nodes[0].node.metadata["__pii_node_info__"] ``` ``` {'[ORG_6]': 'Paulo Santos', '[ORG_108]': 'Any', '[LOC_112]': 'Street', '[LOC_120]': 'Seattle', '[LOC_129]': 'WA'} ``` ### Option 2: Use LLM for PII Masking NOTE: You should be using a *local* LLM model for PII masking. The example shown is using OpenAI, but normally you’d use an LLM running locally, possibly from huggingface. Examples for local LLMs are [here](https://gpt-index.readthedocs.io/en/latest/how_to/customization/custom_llms.html#example-using-a-huggingface-llm). ``` from llama_index.llms.openai import OpenAI processor = PIINodePostprocessor(llm=OpenAI()) ``` ``` from llama_index.core.schema import NodeWithScore new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)]) ``` ``` # view redacted text new_nodes[0].node.get_text() ``` ``` 'Hello [NAME]. The latest statement for your credit card account [CREDIT_CARD_NUMBER] was mailed to [ADDRESS].' ``` ``` # get mapping in metadata # NOTE: this is not sent to the LLM! new_nodes[0].node.metadata["__pii_node_info__"] ``` ``` {'NAME': 'Paulo Santos', 'CREDIT_CARD_NUMBER': '1111-0000-1111-0000', 'ADDRESS': '123 Any Street, Seattle, WA 98109'} ``` ### Option 3: Use Presidio for PII Masking Use presidio to identify and anonymize PII ``` # load documents text = """ Hello Paulo Santos. The latest statement for your credit card account \ 4095-2609-9393-4932 was mailed to Seattle, WA 98109. \ IBAN GB90YNTU67299444055881 and social security number is 474-49-7577 were verified on the system. \ Further communications will be sent to paulo@presidio.site """ presidio_node = TextNode(text=text) ``` ``` from llama_index.postprocessor.presidio import PresidioPIINodePostprocessor processor = PresidioPIINodePostprocessor() ``` ``` from llama_index.core.schema import NodeWithScore presidio_new_nodes = processor.postprocess_nodes( [NodeWithScore(node=presidio_node)] ) ``` ``` # view redacted text presidio_new_nodes[0].node.get_text() ``` ``` '\nHello . The latest statement for your credit card account was mailed to , . IBAN and social security number is were verified on the system. Further communications will be sent to \n' ``` ``` # get mapping in metadata # NOTE: this is not sent to the LLM! presidio_new_nodes[0].node.metadata["__pii_node_info__"] ``` ``` {'': 'paulo@presidio.site', '': '474-49-7577', '': 'GB90YNTU67299444055881', '': 'WA 98109', '': 'Seattle', '': '4095-2609-9393-4932', '': 'Paulo Santos'} ``` ### Feed Nodes to Index ``` # feed into index index = VectorStoreIndex([n.node for n in new_nodes]) ``` ``` INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens > [build_index_from_nodes] Total LLM token usage: 0 tokens INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 30 tokens > [build_index_from_nodes] Total embedding token usage: 30 tokens ``` ``` response = index.as_query_engine().query( "What address was the statement mailed to?" ) print(str(response)) ``` ``` INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens > [retrieve] Total LLM token usage: 0 tokens INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens > [retrieve] Total embedding token usage: 8 tokens INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 71 tokens > [get_response] Total LLM token usage: 71 tokens INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens > [get_response] Total embedding token usage: 0 tokens [ADDRESS] ```