2_chain_of_thought.ipynb

{
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   "metadata": {},
   "source": [
    "## Chain-of-thought-prompting\n",
    "\n",
    "In this notebook, we will explore how we can ask the chat model to show us its conclusions in a multi-step process. Such operations would typically be masked from the user and serve to help developers test the chat application. \n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rastringer/building_apps_with_genai_studio/blob/main/2_chain_of_thought.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69d259b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install \"shapely<2.0.0\" \n",
    "# !pip install google-cloud-aiplatform "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380e4532",
   "metadata": {},
   "source": [
    "If you're on Colab, run the following cell to authenticate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff6bde07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from google.colab import auth\n",
    "# auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e34aa2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import aiplatform as vertexai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea097a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import vertexai\n",
    "from vertexai.preview.language_models import ChatModel, InputOutputTextPair\n",
    "\n",
    "# Replace the project and location placeholder values below\n",
    "vertexai.init(project=\"<your-project-id>\", location=\"<location>\")\n",
    "chat_model = ChatModel.from_pretrained(\"chat-bison@001\")\n",
    "parameters = {\n",
    "    \"temperature\": 0.2,\n",
    "    \"max_output_tokens\": 256,\n",
    "    \"top_p\": 0.8,\n",
    "    \"top_k\": 40\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dc72204",
   "metadata": {},
   "outputs": [],
   "source": [
    "products = \"\"\"\n",
    "name: Caffeino Classic\n",
    "category: Espresso Machines\n",
    "brand: EliteBrew\n",
    "model_number: EB-1001\n",
    "warranty: 2 years\n",
    "rating: 4.6/5 stars\n",
    "features:\n",
    "  15-bar pump for authentic espresso extraction. \n",
    "  Milk frother for creating creamy cappuccinos and lattes.\n",
    "  Removable water reservoir for easy refilling.\n",
    "description: The Caffeino Classic by EliteBrew is a powerful espresso machine that delivers rich and flavorful shots of espresso with the convenience of a built-in milk frother, perfect for indulging in your favorite cafe-style beverages at home.\n",
    "price: £179.99\n",
    "\n",
    "name: BeanPresso\n",
    "category: Single Serve Coffee Makers\n",
    "brand: FreshBrew\n",
    "model_number: FB-500\n",
    "warranty: 1 year\n",
    "rating: 4.3/5 stars\n",
    "features:\n",
    "  Compact design ideal for small spaces or travel.\n",
    "  Compatible with various coffee pods for quick and easy brewing.\n",
    "  Auto-off feature for energy efficiency and safety.\n",
    "description: The BeanPresso by FreshBrew is a compact single-serve coffee maker that allows you to enjoy a fresh cup of coffee effortlessly using your favorite coffee pods, making it the perfect companion for those with limited space or always on the go.\n",
    "price: £49.99\n",
    "\n",
    "name: BrewBlend Pro\n",
    "category: Drip Coffee Makers\n",
    "brand: MasterRoast\n",
    "model_number: MR-800\n",
    "warranty: 3 years\n",
    "rating: 4.7/5 stars\n",
    "features:\n",
    "  Adjustable brew strength for customized coffee flavor.\n",
    "  Large LCD display with programmable timer for convenient brewing.\n",
    "  Anti-drip system to prevent messes on the warming plate.\n",
    "description: The BrewBlend Pro by MasterRoast offers a superior brewing experience with adjustable brew strength, programmable timer, and anti-drip system, ensuring a perfectly brewed cup of coffee every time, making mornings more enjoyable.\n",
    "price: £89.99\n",
    "\n",
    "name: SteamGenie\n",
    "category: Stovetop Coffee Makers\n",
    "brand: KitchenWiz\n",
    "model_number: KW-200\n",
    "warranty: 2 years\n",
    "rating: 4.4/5 stars\n",
    "features:\n",
    "  Classic Italian stovetop design for rich and aromatic coffee.\n",
    "  Durable stainless steel construction for long-lasting performance.\n",
    "  Available in multiple sizes to suit different brewing needs.\n",
    "description: The SteamGenie by KitchenWiz is a traditional stovetop coffee maker that harnesses the essence of Italian coffee culture, crafted with durable stainless steel and delivering a rich, authentic coffee experience with every brew.\n",
    "price: £39.99\n",
    "\n",
    "name: AeroBlend Max\n",
    "category: Coffee and Espresso Combo Machines\n",
    "brand: AeroGen\n",
    "model_number: AG-1200\n",
    "warranty: 2 years\n",
    "rating: 4.9/5 stars\n",
    "features:\n",
    "  Dual-functionality for brewing coffee and espresso.\n",
    "  Built-in burr grinder for fresh coffee grounds.\n",
    "  Adjustable temperature and brew strength settings for personalized beverages.\n",
    "description: The AeroBlend Max by AeroGen is a versatile coffee and espresso combo machine that combines the convenience of brewing both coffee and espresso with a built-in grinder, \n",
    "allowing you to enjoy the perfect cup of your preferred caffeinated delight with ease.\n",
    "price: £299.99\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af53a416",
   "metadata": {},
   "outputs": [],
   "source": [
    "delimiter = \"####\"\n",
    "context = f\"\"\"\n",
    "Follow these steps to answer the customer queries.\n",
    "The customer query will be delimited with four hashtags,\\\n",
    "i.e. {delimiter}. \n",
    "\n",
    "Step 1:{delimiter} First decide whether the user is \\\n",
    "asking a question about a specific product or products. \\\n",
    "Product cateogry doesn't count. \n",
    "\n",
    "Step 2:{delimiter} If the user is asking about \\\n",
    "specific products, identify whether \\\n",
    "the products are in the following list.\n",
    "All available products: \n",
    "{products}\n",
    "\n",
    "Use the following format:\n",
    "Step 1:{delimiter} <step 1 reasoning>\n",
    "Step 2:{delimiter} <step 2 reasoning>\n",
    "Step 3:{delimiter} <step 3 reasoning>\n",
    "Step 4:{delimiter} <step 4 reasoning>\n",
    "Response to user:{delimiter} <response to customer>\n",
    "\n",
    "Make sure to include {delimiter} to separate every step.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18601be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat = chat_model.start_chat(\n",
    "    context=context,\n",
    "    examples=[]\n",
    ")\n",
    "\n",
    "user_message = f\"\"\"\n",
    "How much more expensive is the BrewBlend Pro vs the Caffeino Classic?\n",
    "\"\"\" \n",
    "response = chat.send_message(user_message, **parameters)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06861c5e",
   "metadata": {},
   "source": [
    "The delimiters can help select different parts of the responses. We first, however, have to convert the object returned by the chat into a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95cf814b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vertex returns a TextGenerationResponse\n",
    "type(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3747c6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_response = str(response)\n",
    "print(final_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11259d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    final_response = str(response).split(delimiter)[-1].strip()\n",
    "except Exception as e:\n",
    "    final_response = \"Sorry, I'm unsure of the answer, please try asking another.\"\n",
    "    \n",
    "print(final_response)"
   ]
  }
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