community[minor]: [Pebblo] Enhance PebbloSafeLoader to take anonymize flag (#26812)

- **Description:** The flag is named `anonymize_snippets`. When set to
true, the Pebblo server will anonymize snippets by redacting all
personally identifiable information (PII) from the snippets going into
VectorDB and the generated reports
- **Issue:** NA
- **Dependencies:** NA
- **docs**: Updated

docs/docs/integrations/document_loaders/pebblo.ipynb

@@ -124,6 +124,39 @@
   {
    "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "### Anonymize the snippets to redact all PII details\n",
+    "\n",
+    "Set `anonymize_snippets` to `True` to anonymize all personally identifiable information (PII) from the snippets going into VectorDB and the generated reports.\n",
+    "\n",
+    "> Note: The _Pebblo Entity Classifier_ effectively identifies personally identifiable information (PII) and is continuously evolving. While its recall is not yet 100%, it is steadily improving.\n",
+    "> For more details, please refer to the [_Pebblo Entity Classifier docs_](https://daxa-ai.github.io/pebblo/entityclassifier/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import CSVLoader, PebbloSafeLoader\n",
+    "\n",
+    "loader = PebbloSafeLoader(\n",
+    "    CSVLoader(\"data/corp_sens_data.csv\"),\n",
+    "    name=\"acme-corp-rag-1\",  # App name (Mandatory)\n",
+    "    owner=\"Joe Smith\",  # Owner (Optional)\n",
+    "    description=\"Support productivity RAG application\",  # Description (Optional)\n",
+    "    anonymize_snippets=True,  # Whether to anonymize entities in the PDF Report (Optional, default=False)\n",
+    ")\n",
+    "documents = loader.load()\n",
+    "print(documents[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],