'UK_NHS': {'3361388464': '987-654-3210'},     'US_DRIVER_LICENSE': {'532311310': '999000680'},     'US_SSN': {'690-84-1613': '602-76-4532'}}Voil√†! Now all values are replaced with synthetic ones. Note that the deanonymizer mapping has been updated accordingly.Question-answering system with PII anonymization‚ÄãNow, let's wrap it up together and create full question-answering system, based on PresidioReversibleAnonymizer and LangChain Expression Language (LCEL).# 1. Initialize anonymizeranonymizer = PresidioReversibleAnonymizer(    # Faker seed is used here to make sure the same fake data is generated for the test purposes    # In production, it is recommended to remove the faker_seed parameter (it will default to None)    faker_seed=42,)anonymizer.add_recognizer(polish_id_recognizer)anonymizer.add_recognizer(time_recognizer)anonymizer.add_operators(new_operators)from langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import FAISS# 2. Load the data: In our case data's already loaded# 3. Anonymize the data before indexingfor doc in documents:    doc.page_content = anonymizer.anonymize(doc.page_content)# 4. Split the documents into chunkstext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)chunks = text_splitter.split_documents(documents)# 5. Index the chunks (using OpenAI embeddings, because the data is already anonymized)embeddings = OpenAIEmbeddings()docsearch = FAISS.from_documents(chunks, embeddings)retriever = docsearch.as_retriever()from operator import itemgetterfrom langchain.chat_models.openai import ChatOpenAIfrom langchain.schema.runnable import RunnableMapfrom langchain.prompts import ChatPromptTemplatefrom langchain.schema.output_parser import StrOutputParserfrom langchain.schema.runnable import RunnablePassthroughfrom langchain.schema.runnable import RunnableLambda# 6. Create anonymizer chaintemplate = """Answer the question
Open In Colab
Open In Colab ->: 'UK_NHS': {'3361388464': '987-654-3210'},     'US_DRIVER_LICENSE': {'532311310': '999000680'},     'US_SSN': {'690-84-1613': '602-76-4532'}}Voil√†! Now all values are replaced with synthetic ones. Note that the deanonymizer mapping has been updated accordingly.Question-answering system with PII anonymization‚ÄãNow, let's wrap it up together and create full question-answering system, based on PresidioReversibleAnonymizer and LangChain Expression Language (LCEL).# 1. Initialize anonymizeranonymizer = PresidioReversibleAnonymizer(    # Faker seed is used here to make sure the same fake data is generated for the test purposes    # In production, it is recommended to remove the faker_seed parameter (it will default to None)    faker_seed=42,)anonymizer.add_recognizer(polish_id_recognizer)anonymizer.add_recognizer(time_recognizer)anonymizer.add_operators(new_operators)from langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import FAISS# 2. Load the data: In our case data's already loaded# 3. Anonymize the data before indexingfor doc in documents:    doc.page_content = anonymizer.anonymize(doc.page_content)# 4. Split the documents into chunkstext_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)chunks = text_splitter.split_documents(documents)# 5. Index the chunks (using OpenAI embeddings, because the data is already anonymized)embeddings = OpenAIEmbeddings()docsearch = FAISS.from_documents(chunks, embeddings)retriever = docsearch.as_retriever()from operator import itemgetterfrom langchain.chat_models.openai import ChatOpenAIfrom langchain.schema.runnable import RunnableMapfrom langchain.prompts import ChatPromptTemplatefrom langchain.schema.output_parser import StrOutputParserfrom langchain.schema.runnable import RunnablePassthroughfrom langchain.schema.runnable import RunnableLambda# 6. Create anonymizer chaintemplate = """Answer the question