diff --git a/examples/Structured_output_with_function_calling.ipynb b/examples/Structured_output_with_function_calling.ipynb
new file mode 100644
index 00000000..44458c30
--- /dev/null
+++ b/examples/Structured_output_with_function_calling.ipynb
@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Structured Output with Function Calling\n",
+    "\n",
+    "As developers, we often want our models to return structured output (as opposed to raw text), so they can interface with other systems. There's a range of [interesting](https://x.com/goodside/status/1657396491676164096?s=20) ways of doing this. This notebook will specifically look at the **function calling** approach, but we'll briefly go over alternatives at the end."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "**First off, why use function calling for JSON? What do function calls have to do with JSON output?**\n",
+    "\n",
+    "\n",
+    "### Motivation\n",
+    "\n",
+    "What does calling functions have to do with JSON output?\n",
+    "\n",
+    "Strictly speaking, it doesn't! The key idea is that both involve _structure_ (keys, values, and types), so we can leverage the **inherent structure in the typed arguments of a function** to model the **JSON we want to output**."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at an example:\n",
+    "\n",
+    "Say we want to extract the `name`, `date`, `event_type`, and `attendees` of an event given some natural language input.\n",
+    "\n",
+    "Input:\n",
+    "```\n",
+    "Next tuesday Jennifer is hosting a house warming at her place, and so far she's invited Steven and Julian. Alex told me he's going with Jessica, but Samantha can't make it.\n",
+    "```\n",
+    "\n",
+    "Desired Output:\n",
+    "```javascript\n",
+    "{\n",
+    "    \"name\": \"Jennifer's Housewarming\",\n",
+    "    \"date\": \"Next Tuesday\", // (to be computed later)\n",
+    "    \"event_type\": \"SOCIAL\",\n",
+    "    \"attendees\": [\"Steven\", \"Julian\", \"Jessica\"]\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "We could hypothetically caputre this structre in a function's argument by defining it like so:\n",
+    "\n",
+    "```python\n",
+    "def define_event(name: str, date: str, attendees: List[str]):\n",
+    "    pass\n",
+    "```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We don't actually have to define this function in python – just define it's interface to pass to our model in the `functions` param! We can optionally use descriptions to help the model understand what an argument is meant to represent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions = [\n",
+    "    {\n",
+    "        \"name\": \"define_event\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"name\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                },\n",
+    "                \"date\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"Can be either absolute or relative e.g. Next Week.\",\n",
+    "                },\n",
+    "                \"event_type\": {\"type\": \"string\", \"enum\": [\"SOCIAL\", \"WORK\", \"OTHER\"]},\n",
+    "                \"attendees\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n",
+    "            },\n",
+    "        },\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can make the call to OpenAI by providing the function in the `functions` param. We'll also set the `function_call` param to `{\"name\":\"define_event\"}` to force the model to call that function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<OpenAIObject chat.completion id=chatcmpl-8HMQCDtgR9NDjRL2G1FRFAV8CLrfe at 0x13a6facc0> JSON: {\n",
+       "  \"id\": \"chatcmpl-8HMQCDtgR9NDjRL2G1FRFAV8CLrfe\",\n",
+       "  \"object\": \"chat.completion\",\n",
+       "  \"created\": 1699148456,\n",
+       "  \"model\": \"gpt-4-0613\",\n",
+       "  \"choices\": [\n",
+       "    {\n",
+       "      \"index\": 0,\n",
+       "      \"message\": {\n",
+       "        \"role\": \"assistant\",\n",
+       "        \"content\": null,\n",
+       "        \"function_call\": {\n",
+       "          \"name\": \"define_event\",\n",
+       "          \"arguments\": \"{\\n  \\\"name\\\": \\\"Jennifer's House Warming\\\",\\n  \\\"date\\\": \\\"Next Tuesday\\\",\\n  \\\"event_type\\\": \\\"SOCIAL\\\",\\n  \\\"attendees\\\": [\\\"Steven\\\", \\\"Julian\\\", \\\"Alex\\\", \\\"Jessica\\\"]\\n}\"\n",
+       "        }\n",
+       "      },\n",
+       "      \"finish_reason\": \"stop\",\n",
+       "      \"internal_metrics\": [\n",
+       "        {\n",
+       "          \"cached_prompt_tokens\": 0,\n",
+       "          \"total_accepted_tokens\": 0,\n",
+       "          \"total_batched_tokens\": 173,\n",
+       "          \"total_predicted_tokens\": 0,\n",
+       "          \"total_rejected_tokens\": 0,\n",
+       "          \"total_tokens_in_completion\": 174,\n",
+       "          \"cached_embeddings_bytes\": 0,\n",
+       "          \"cached_embeddings_n\": 0,\n",
+       "          \"uncached_embeddings_bytes\": 0,\n",
+       "          \"uncached_embeddings_n\": 0,\n",
+       "          \"fetched_embeddings_bytes\": 0,\n",
+       "          \"fetched_embeddings_n\": 0,\n",
+       "          \"n_evictions\": 0,\n",
+       "          \"batcher_ttft\": 0.06491327285766602,\n",
+       "          \"batcher_initial_queue_time\": 0.0002872943878173828\n",
+       "        }\n",
+       "      ]\n",
+       "    }\n",
+       "  ],\n",
+       "  \"usage\": {\n",
+       "    \"prompt_tokens\": 125,\n",
+       "    \"completion_tokens\": 48,\n",
+       "    \"total_tokens\": 173\n",
+       "  }\n",
+       "}"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import openai\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"system\",\n",
+    "        \"content\": \"Extract the event details.\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": \"Next tuesday Jennifer is hosting a house warming at her place, and so far she's invited Steven and Julian. Alex told me he's going with Jessica, but Samantha can't make it.\",\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "response = openai.ChatCompletion.create(\n",
+    "    model=\"gpt-4\",\n",
+    "    messages=messages,\n",
+    "    functions=functions,\n",
+    "    function_call={\"name\": \"define_event\"},\n",
+    ")\n",
+    "response"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we extract the function call provided by the model. Note the arguments are JSON encoded, so we'll need to decode those as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': \"Jennifer's House Warming\",\n",
+       " 'date': 'Next Tuesday',\n",
+       " 'event_type': 'SOCIAL',\n",
+       " 'attendees': ['Steven', 'Julian', 'Alex', 'Jessica']}"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "event_details = json.loads(response.choices[0].message.function_call.arguments)\n",
+    "event_details"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Awesome! Now all together wrapped ina  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "\n",
+    "def extract_event_details(user_input):\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": \"Extract the event details.\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": user_input,\n",
+    "        },\n",
+    "    ]\n",
+    "    response = openai.ChatCompletion.create(\n",
+    "        model=\"gpt-4\",\n",
+    "        messages=messages,\n",
+    "        functions=functions,\n",
+    "        function_call={\"name\":\"define_event\"},\n",
+    "    )\n",
+    "    return response\n",
+    "\n",
+    "response = extract_event_details(\"Next tuesday Jennifer is hosting a house warming at her place, and so far she's invited Steven and Julian. Alex told me he's going with Jessica, but Samantha can't make it.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "event_details = json.loads(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai, json\n",
+    "\n",
+    "SAMPLE_JSON = {\n",
+    "    \"date\": \"2021-10-10\",\n",
+    "    \"event_type\": \"social\",\n",
+    "}\n",
+    "\n",
+    "JSON_SCHEMA = {\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "        \"date\": {\n",
+    "            \"type\": \"string\",\n",
+    "        },\n",
+    "        \"event_type\": {\"type\": \"string\", \"enum\": [\"social\", \"work\"]},\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def extract_json(input_text, json_schema):\n",
+    "    extract_function_name = \"extract_json\"\n",
+    "    functions = [{\"name\": extract_function_name, \"parameters\": json_schema}]\n",
+    "\n",
+    "    messages = [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": \"Extract the relevant fields by using the return_json function.\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": input_text,\n",
+    "        },\n",
+    "    ]\n",
+    "\n",
+    "    response = openai.ChatCompletion.create(\n",
+    "        model=\"gpt-4\",\n",
+    "        messages=messages,\n",
+    "        functions=functions,\n",
+    "        function_call={\"name\":extract_function_name},\n",
+    "    )\n",
+    "    return json.loads(response.choices[0].message.function_call.arguments)\n",
+    "\n",
+    "\n",
+    "print(extract_json(\"The date is 2021-10-10 and the event type is social.\", JSON_SCHEMA))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import os\n",
+    "\n",
+    "def maybe_json():\n",
+    "    headers = {\n",
+    "        \"Content-Type\": \"application/json\",\n",
+    "        \"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\",\n",
+    "    }\n",
+    "    data = {\n",
+    "        \"model\": \"gpt-3.5-turbo-1106\",\n",
+    "        # \"response_format\": { \"type\": \"json_object\" },\n",
+    "        \"max_tokens\": 200,\n",
+    "        \"messages\": [\n",
+    "            {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": \"Tell me a two sentence story about JSON.\"\n",
+    "            }\n",
+    "        ]\n",
+    "    }\n",
+    "    response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=data)\n",
+    "    return response.json()\n",
+    "\n",
+    "print(json.dumps(maybe_json(), indent=4))\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openai",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}