diff --git a/docs/extras/guides/privacy/presidio_data_anonymization.ipynb b/docs/extras/guides/privacy/presidio_data_anonymization.ipynb
index 7bb0b15934..faa9929259 100644
--- a/docs/extras/guides/privacy/presidio_data_anonymization.ipynb
+++ b/docs/extras/guides/privacy/presidio_data_anonymization.ipynb
@@ -28,12 +28,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Install necessary packages\n",
-    "# ! pip install langchain langchain-experimental openai\n",
+    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
     "# ! python -m spacy download en_core_web_lg"
    ]
   },
@@ -47,16 +47,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'My name is Marie Santos, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
+       "'My name is Mrs. Rachel Chen DDS, call me at 849-829-7628x073 or email me at christopherfrey@example.org'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -64,6 +64,92 @@
    "source": [
     "from langchain_experimental.data_anonymizer import PresidioAnonymizer\n",
     "\n",
+    "anonymizer = PresidioAnonymizer()\n",
+    "\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using with LangChain Expression Language\n",
+    "\n",
+    "With LCEL we can easily chain together anonymization with the rest of our application."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
+    "# import dotenv\n",
+    "\n",
+    "# dotenv.load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='You can find our super secret data at https://www.ross.com/', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "\n",
+    "template = \"\"\"According to this text, where can you find our super secret data?\n",
+    "\n",
+    "{anonymized_text}\n",
+    "\n",
+    "Answer:\"\"\"\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "llm = ChatOpenAI()\n",
+    "\n",
+    "chain = {\"anonymized_text\": anonymizer.anonymize} | prompt | llm\n",
+    "chain.invoke(\"You can find our super secret data at https://supersecretdata.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customization\n",
+    "We can specify ``analyzed_fields`` to only anonymize particular types of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Gabrielle Edwards, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "anonymizer = PresidioAnonymizer(analyzed_fields=[\"PERSON\"])\n",
     "\n",
     "anonymizer.anonymize(\n",
@@ -75,7 +161,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\\\n",
     "As can be observed, the name was correctly identified and replaced with another. The `analyzed_fields` attribute is responsible for what values are to be detected and substituted. We can add *PHONE_NUMBER* to the list:"
    ]
   },
@@ -331,125 +416,6 @@
     "anonymizer.anonymize(\"My polish phone number is 666555444\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\\\n",
-    "Finally, it is worth showing how to implement anonymizer as a chain. Since anonymization is based on string operations, we can use `TransformChain` for this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': 'You can find our super secret data at https://supersecretdata.com',\n",
-       " 'anonymized_text': 'You can find our super secret data at https://www.fox.org/'}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains.transform import TransformChain\n",
-    "\n",
-    "anonymizer = PresidioAnonymizer()\n",
-    "\n",
-    "\n",
-    "def anonymize_func(inputs: dict) -> dict:\n",
-    "    text = inputs[\"text\"]\n",
-    "    return {\"anonymized_text\": anonymizer.anonymize(text)}\n",
-    "\n",
-    "\n",
-    "anonymize_chain = TransformChain(\n",
-    "    input_variables=[\"text\"],\n",
-    "    output_variables=[\"anonymized_text\"],\n",
-    "    transform=anonymize_func,\n",
-    ")\n",
-    "\n",
-    "anonymize_chain(\"You can find our super secret data at https://supersecretdata.com\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\\\n",
-    "Later, you can, for example, use such anonymization as part of chain sequence. We will use `LangChain Expression Language` ([learn more here](https://python.langchain.com/docs/guides/expression_language/)) for composing these chains together, as shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# ! pip install openai\n",
-    "\n",
-    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
-    "import dotenv\n",
-    "\n",
-    "dotenv.load_dotenv()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'anonymized_text': StringPromptValue(text='According to this text, where can you find our super secret data?\\n\\nYou can find our super secret data at https://evans-summers.info/\\n\\nAnswer:'),\n",
-       " 'text': ' https://evans-summers.info/'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from operator import itemgetter\n",
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "from langchain.chains.llm import LLMChain\n",
-    "from langchain.llms.openai import OpenAI\n",
-    "\n",
-    "template = \"\"\"According to this text, where can you find our super secret data?\n",
-    "\n",
-    "{anonymized_text}\n",
-    "\n",
-    "Answer:\"\"\"\n",
-    "prompt = PromptTemplate(input_variables=[\"anonymized_text\"], template=template)\n",
-    "llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)\n",
-    "\n",
-    "\n",
-    "chain = (\n",
-    "    anonymize_chain\n",
-    "    | {\"anonymized_text\": itemgetter(\"anonymized_text\")}\n",
-    "    | prompt\n",
-    "    | llm_chain\n",
-    ")\n",
-    "chain.invoke(\"You can find our super secret data at https://supersecretdata.com\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},