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@ -21,7 +21,7 @@
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"\n",
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"In this notebook, we will look at building a basic system for question answering, based on private data. Before feeding the LLM with this data, we need to protect it so that it doesn't go to an external API (e.g. OpenAI, Anthropic). Then, after receiving the model output, we would like the data to be restored to its original form. Below you can observe an example flow of this QA system:\n",
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"\n",
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"<img src=\"/img/qa_privacy_protection.png\" width=\"800\"/>\n",
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"<img src=\"/img/qa_privacy_protection.png\" width=\"900\"/>\n",
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"\n",
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"\n",
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"In the following notebook, we will not go into the details of how the anonymizer works. If you are interested, please visit [this part of the documentation](https://python.langchain.com/docs/guides/privacy/presidio_data_anonymization/).\n",
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@ -839,6 +839,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"documents = [Document(page_content=document_content)]\n",
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"\n",
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"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
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"chunks = text_splitter.split_documents(documents)\n",
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"\n",
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