Small fixes to docs

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -16,7 +16,7 @@
    "source": [
     "# QA with private data protection\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/use_cases/question_answering/qa_privacy_protection.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb)\n",
     "\n",
     "\n",
     "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",
@@ -643,6 +643,8 @@
     "from langchain.vectorstores import FAISS\n",
     "\n",
     "# 2. Load the data: In our case data's already loaded\n",
+    "documents = [Document(page_content=document_content)]\n",
+    "\n",
     "# 3. Anonymize the data before indexing\n",
     "for doc in documents:\n",
     "    doc.page_content = anonymizer.anonymize(doc.page_content)\n",
@@ -839,6 +841,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "documents = [Document(page_content=document_content)]\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
     "chunks = text_splitter.split_documents(documents)\n",
     "\n",