L2_Sequential_Chats_and_Customer_Onboarding.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "8140b161",
   "metadata": {},
   "source": [
    "# Lesson 2: Sequential Chats and Customer Onboarding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d4d307",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24b75995-4ee4-4ff0-9c44-3943caae37e7",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "llm_config={\"model\": \"gpt-3.5-turbo\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20ce6700-8a33-424f-aefe-8852fd1e6d07",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from autogen import ConversableAgent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f979f9",
   "metadata": {},
   "source": [
    "## Creating the needed agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a527bb1e-dd4e-47b0-a1b7-a9cbcd87cbdb",
   "metadata": {
    "height": 212
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   "source": [
    "onboarding_personal_information_agent = ConversableAgent(\n",
    "    name=\"Onboarding Personal Information Agent\",\n",
    "    system_message='''You are a helpful customer onboarding agent,\n",
    "    you are here to help new customers get started with our product.\n",
    "    Your job is to gather customer's name and location.\n",
    "    Do not ask for other information. Return 'TERMINATE' \n",
    "    when you have gathered all the information.''',\n",
    "    llm_config=llm_config,\n",
    "    code_execution_config=False,\n",
    "    human_input_mode=\"NEVER\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51bc9a24-a680-444d-943b-b740bce0189d",
   "metadata": {
    "height": 212
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   "outputs": [],
   "source": [
    "onboarding_topic_preference_agent = ConversableAgent(\n",
    "    name=\"Onboarding Topic preference Agent\",\n",
    "    system_message='''You are a helpful customer onboarding agent,\n",
    "    you are here to help new customers get started with our product.\n",
    "    Your job is to gather customer's preferences on news topics.\n",
    "    Do not ask for other information.\n",
    "    Return 'TERMINATE' when you have gathered all the information.''',\n",
    "    llm_config=llm_config,\n",
    "    code_execution_config=False,\n",
    "    human_input_mode=\"NEVER\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6755a7fc-cb17-4d62-a03f-48e260f39010",
   "metadata": {
    "height": 246
   },
   "outputs": [],
   "source": [
    "customer_engagement_agent = ConversableAgent(\n",
    "    name=\"Customer Engagement Agent\",\n",
    "    system_message='''You are a helpful customer service agent\n",
    "    here to provide fun for the customer based on the user's\n",
    "    personal information and topic preferences.\n",
    "    This could include fun facts, jokes, or interesting stories.\n",
    "    Make sure to make it engaging and fun!\n",
    "    Return 'TERMINATE' when you are done.''',\n",
    "    llm_config=llm_config,\n",
    "    code_execution_config=False,\n",
    "    human_input_mode=\"NEVER\",\n",
    "    is_termination_msg=lambda msg: \"terminate\" in msg.get(\"content\").lower(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64267c0b-f7f2-46e6-ab44-6f7b5fbd9db7",
   "metadata": {
    "height": 144
   },
   "outputs": [],
   "source": [
    "customer_proxy_agent = ConversableAgent(\n",
    "    name=\"customer_proxy_agent\",\n",
    "    llm_config=False,\n",
    "    code_execution_config=False,\n",
    "    human_input_mode=\"ALWAYS\",\n",
    "    is_termination_msg=lambda msg: \"terminate\" in msg.get(\"content\").lower(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f240408",
   "metadata": {},
   "source": [
    "## Creating tasks\n",
    "\n",
    "Now, you can craft a series of tasks to facilitate the onboarding process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b15af1d-7042-4569-a936-7966be203f05",
   "metadata": {
    "height": 603
   },
   "outputs": [],
   "source": [
    "chats = [\n",
    "    {\n",
    "        \"sender\": onboarding_personal_information_agent,\n",
    "        \"recipient\": customer_proxy_agent,\n",
    "        \"message\": \n",
    "            \"Hello, I'm here to help you get started with our product.\"\n",
    "            \"Could you tell me your name and location?\",\n",
    "        \"summary_method\": \"reflection_with_llm\",\n",
    "        \"summary_args\": {\n",
    "            \"summary_prompt\" : \"Return the customer information \"\n",
    "                             \"into as JSON object only: \"\n",
    "                             \"{'name': '', 'location': ''}\",\n",
    "        },\n",
    "        \"max_turns\": 2,\n",
    "        \"clear_history\" : True\n",
    "    },\n",
    "    {\n",
    "        \"sender\": onboarding_topic_preference_agent,\n",
    "        \"recipient\": customer_proxy_agent,\n",
    "        \"message\": \n",
    "                \"Great! Could you tell me what topics you are \"\n",
    "                \"interested in reading about?\",\n",
    "        \"summary_method\": \"reflection_with_llm\",\n",
    "        \"max_turns\": 1,\n",
    "        \"clear_history\" : False\n",
    "    },\n",
    "    {\n",
    "        \"sender\": customer_proxy_agent,\n",
    "        \"recipient\": customer_engagement_agent,\n",
    "        \"message\": \"Let's find something fun to read.\",\n",
    "        \"max_turns\": 1,\n",
    "        \"summary_method\": \"reflection_with_llm\",\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862a066b",
   "metadata": {},
   "source": [
    "## Start the onboarding process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0fa8f99",
   "metadata": {},
   "source": [
    "**Note**: You might get a slightly different response than what's shown in the video. Feel free to try different inputs, such as name, location, and preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d6d1d4a-0b50-41a5-a1f0-3ff208398bc6",
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\n",
      "********************************************************************************\u001b[0m\n",
      "\u001b[34mStarting a new chat....\u001b[0m\n",
      "\u001b[34m\n",
      "********************************************************************************\u001b[0m\n",
      "\u001b[33mOnboarding Personal Information Agent\u001b[0m (to customer_proxy_agent):\n",
      "\n",
      "Hello, I'm here to help you get started with our product.Could you tell me your name and location?\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/autogen/agentchat/chat.py:47: UserWarning: Repetitive recipients detected: The chat history will be cleared by default if a recipient appears more than once. To retain the chat history, please set 'clear_history=False' in the configuration of the repeating agent.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Provide feedback to Onboarding Personal Information Agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: good !\n",
      "\u001b[33mcustomer_proxy_agent\u001b[0m (to Onboarding Personal Information Agent):\n",
      "\n",
      "good !\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mOnboarding Personal Information Agent\u001b[0m (to customer_proxy_agent):\n",
      "\n",
      "I'm glad to hear that! Could you please provide me with your name and location?\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Provide feedback to Onboarding Personal Information Agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: exit\n",
      "\u001b[34m\n",
      "********************************************************************************\u001b[0m\n",
      "\u001b[34mStarting a new chat....\u001b[0m\n",
      "\u001b[34m\n",
      "********************************************************************************\u001b[0m\n",
      "\u001b[33mOnboarding Topic preference Agent\u001b[0m (to customer_proxy_agent):\n",
      "\n",
      "Great! Could you tell me what topics you are interested in reading about?\n",
      "Context: \n",
      "{\n",
      "  \"name\": \"John\",\n",
      "  \"location\": \"New York\"\n",
      "}\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Provide feedback to Onboarding Topic preference Agent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: exit\n",
      "\u001b[34m\n",
      "********************************************************************************\u001b[0m\n",
      "\u001b[34mStarting a new chat....\u001b[0m\n",
      "\u001b[34m\n",
      "********************************************************************************\u001b[0m\n",
      "\u001b[33mcustomer_proxy_agent\u001b[0m (to Customer Engagement Agent):\n",
      "\n",
      "Let's find something fun to read.\n",
      "Context: \n",
      "{\n",
      "  \"name\": \"John\",\n",
      "  \"location\": \"New York\"\n",
      "}\n",
      "The user asked what topics the individual is interested in reading about, but did not specify any specific preferences.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mCustomer Engagement Agent\u001b[0m (to customer_proxy_agent):\n",
      "\n",
      "Hey John from New York! 🗽 How about we explore some fun facts about the Big Apple? Did you know that New York City has a hidden park that sits 30 feet below street level? It's called the Lowline Lab, and it's an underground oasis of plants and greenery right in the heart of the city! If you're interested in urban exploration and unique hidden gems, this could be a fascinating read. What do you think? 🌳🌆\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from autogen import initiate_chats\n",
    "\n",
    "chat_results = initiate_chats(chats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9e2713",
   "metadata": {},
   "source": [
    "## Print out the summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e122f8a-1ceb-4635-9672-662114b0552a",
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"name\": \"John\",\n",
      "  \"location\": \"New York\"\n",
      "}\n",
      "\n",
      "\n",
      "The user asked what topics the individual is interested in reading about, but did not specify any specific preferences.\n",
      "\n",
      "\n",
      "Explore fun facts about the Big Apple, such as the hidden Lowline Lab park in New York City.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for chat_result in chat_results:\n",
    "    print(chat_result.summary)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a674c4eb",
   "metadata": {},
   "source": [
    "## Print out the cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b82a10a-afe5-4ba3-97b4-41c8c14b739f",
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'usage_including_cached_inference': {'total_cost': 0.0001425, 'gpt-3.5-turbo-0125': {'cost': 0.0001425, 'prompt_tokens': 180, 'completion_tokens': 35, 'total_tokens': 215}}, 'usage_excluding_cached_inference': {'total_cost': 0.0001425, 'gpt-3.5-turbo-0125': {'cost': 0.0001425, 'prompt_tokens': 180, 'completion_tokens': 35, 'total_tokens': 215}}}\n",
      "\n",
      "\n",
      "{'usage_including_cached_inference': {'total_cost': 6.25e-05, 'gpt-3.5-turbo-0125': {'cost': 6.25e-05, 'prompt_tokens': 62, 'completion_tokens': 21, 'total_tokens': 83}}, 'usage_excluding_cached_inference': {'total_cost': 6.25e-05, 'gpt-3.5-turbo-0125': {'cost': 6.25e-05, 'prompt_tokens': 62, 'completion_tokens': 21, 'total_tokens': 83}}}\n",
      "\n",
      "\n",
      "{'usage_including_cached_inference': {'total_cost': 0.0003295, 'gpt-3.5-turbo-0125': {'cost': 0.0003295, 'prompt_tokens': 302, 'completion_tokens': 119, 'total_tokens': 421}}, 'usage_excluding_cached_inference': {'total_cost': 0.0003295, 'gpt-3.5-turbo-0125': {'cost': 0.0003295, 'prompt_tokens': 302, 'completion_tokens': 119, 'total_tokens': 421}}}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for chat_result in chat_results:\n",
    "    print(chat_result.cost)\n",
    "    print(\"\\n\")"
   ]
  }
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