diff --git a/docs/docs/integrations/chat/perplexity.ipynb b/docs/docs/integrations/chat/perplexity.ipynb
index b2545aad14e..2a9c0efce0b 100644
--- a/docs/docs/integrations/chat/perplexity.ipynb
+++ b/docs/docs/integrations/chat/perplexity.ipynb
@@ -173,6 +173,41 @@
     "response.content"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a7f8f61b",
+   "metadata": {},
+   "source": [
+    "## Using Perplexity-specific parameters through `ChatPerplexity`\n",
+    "\n",
+    "You can also use Perplexity-specific parameters through the ChatPerplexity class. For example, parameters like search_domain_filter, return_images, return_related_questions or search_recency_filter using the extra_body parameter as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73960f51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Sure, here's a cat joke for you:\\n\\nWhy are cats bad storytellers?\\n\\nBecause they only have one tale. (Pun alert!)\\n\\nSource: OneLineFun.com [4]\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = ChatPerplexity(temperature=0.7, model=\"llama-3.1-sonar-small-128k-online\")\n",
+    "response = chat.invoke(\n",
+    "    \"Tell me a joke about cats\", extra_body={\"search_recency_filter\": \"week\"}\n",
+    ")\n",
+    "response.content"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "13d93dc4",
@@ -216,13 +251,56 @@
    ],
    "source": [
     "chat = ChatPerplexity(temperature=0.7, model=\"llama-3.1-sonar-small-128k-online\")\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"human\", \"Give me a list of famous tourist attractions in Pakistan\")]\n",
-    ")\n",
-    "chain = prompt | chat\n",
-    "for chunk in chain.stream({}):\n",
+    "\n",
+    "for chunk in chat.stream(\"Give me a list of famous tourist attractions in Pakistan\"):\n",
     "    print(chunk.content, end=\"\", flush=True)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397c43de",
+   "metadata": {},
+   "source": [
+    "## `ChatPerplexity` Supports Structured Outputs for Tier 3+ Users"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1bae9b80-394a-4340-9c30-612c136b742a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnswerFormat(first_name='Michael', last_name='Jordan', year_of_birth=1963, num_seasons_in_nba=15)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class AnswerFormat(BaseModel):\n",
+    "    first_name: str\n",
+    "    last_name: str\n",
+    "    year_of_birth: int\n",
+    "    num_seasons_in_nba: int\n",
+    "\n",
+    "\n",
+    "chat = ChatPerplexity(temperature=0.7, model=\"sonar-pro\")\n",
+    "structured_chat = chat.with_structured_output(AnswerFormat)\n",
+    "response = structured_chat.invoke(\n",
+    "    \"Tell me about Michael Jordan. Return your answer \"\n",
+    "    \"as JSON with keys first_name (str), last_name (str), \"\n",
+    "    \"year_of_birth (int), and num_seasons_in_nba (int).\"\n",
+    ")\n",
+    "response"
+   ]
   }
  ],
  "metadata": {
@@ -241,7 +319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,