agentops/examples/openai/openai_example_async.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI Async Example\n",
    "\n",
    "We are going to create a simple chatbot that creates stories based on a user provided image. The chatbot will use the gpt-4o-mini LLM to generate the story using a user prompt and its vision model to understand the image.\n",
    "\n",
    "We will track the chatbot with AgentOps and see how it performs!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's install the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required dependencies\n",
    "%pip install agentops\n",
    "%pip install openai\n",
    "%pip install python-dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then import them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import AsyncOpenAI\n",
    "import agentops\n",
    "import os\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll grab our API keys. You can use dotenv like below or however else you like to load environment variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv()\n",
    "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\", \"your_openai_api_key_here\")\n",
    "os.environ[\"AGENTOPS_API_KEY\"] = os.getenv(\"AGENTOPS_API_KEY\", \"your_api_key_here\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we initialize the AgentOps client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agentops.init(auto_start_session=True)\n",
    "tracer = agentops.start_trace(\n",
    "    trace_name=\"OpenAI Async Example\", tags=[\"openai-async-example\", \"openai\", \"agentops-example\"]\n",
    ")\n",
    "client = AsyncOpenAI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we are all set! Note the seesion url above. We will use it to track the chatbot.\n",
    "\n",
    "Let's create a simple chatbot that generates stories given an image and a user prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are a master storyteller, with the ability to create vivid and engaging stories.\n",
    "You have experience in writing for children and adults alike.\n",
    "You are given a prompt and you need to generate a story based on the prompt.\n",
    "\"\"\"\n",
    "\n",
    "user_prompt = [\n",
    "    {\"type\": \"text\", \"text\": \"Write a mystery thriller story based on your understanding of the provided image.\"},\n",
    "    {\n",
    "        \"type\": \"image_url\",\n",
    "        \"image_url\": {\"url\": \"https://www.cosy.sbg.ac.at/~pmeerw/Watermarking/lena_color.gif\"},\n",
    "    },\n",
    "]\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": system_prompt},\n",
    "    {\"role\": \"user\", \"content\": user_prompt},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def main():\n",
    "    response = await client.chat.completions.create(\n",
    "        model=\"gpt-4o-mini\",\n",
    "        messages=messages,\n",
    "    )\n",
    "\n",
    "    print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The response is a string that contains the story. We can track this with AgentOps by navigating to the trace url and viewing the run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Version\n",
    "We will demonstrate the streaming version of the API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def main_stream():\n",
    "    stream = await client.chat.completions.create(\n",
    "        model=\"gpt-4o-mini\",\n",
    "        messages=messages,\n",
    "        stream=True,\n",
    "    )\n",
    "\n",
    "    async for chunk in stream:\n",
    "        print(chunk.choices[0].delta.content or \"\", end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await main_stream()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agentops.end_trace(tracer, end_state=\"Success\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the response is a generator that yields chunks of the story. We can track this with AgentOps by navigating to the trace url and viewing the run.\n",
    "All done!"
   ]
  }
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