{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927",
   "metadata": {},
   "source": [
    "# Welcome to Week 2!\n",
    "\n",
    "## Frontier Model APIs\n",
    "\n",
    "In Week 1, we used multiple Frontier LLMs through their Chat UI, and we connected with the OpenAI's API.\n",
    "\n",
    "Today we'll connect with the APIs for Anthropic and Google, as well as OpenAI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cfe275-4705-4d30-abea-643fbddf1db0",
   "metadata": {},
   "source": [
    "## Setting up your keys\n",
    "\n",
    "If you haven't done so already, you'll need to create API keys from OpenAI, Anthropic and Google.\n",
    "\n",
    "For OpenAI, visit https://openai.com/api/  \n",
    "For Anthropic, visit https://console.anthropic.com/  \n",
    "For Google, visit https://ai.google.dev/gemini-api  \n",
    "\n",
    "When you get your API keys, you need to set them as environment variables.\n",
    "\n",
    "EITHER (recommended) create a file called `.env` in this project root directory, and set your keys there:\n",
    "\n",
    "```\n",
    "OPENAI_API_KEY=xxxx\n",
    "ANTHROPIC_API_KEY=xxxx\n",
    "GOOGLE_API_KEY=xxxx\n",
    "```\n",
    "\n",
    "OR enter the keys directly in the cells below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import google.generativeai\n",
    "import anthropic\n",
    "from IPython.display import Markdown, display, update_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables in a file called .env\n",
    "\n",
    "load_dotenv()\n",
    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
    "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
    "os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to OpenAI, Anthropic and Google\n",
    "# All 3 APIs are similar\n",
    "# Having problems with API files? You can use openai = OpenAI(api_key=\"your-key-here\") and same for claude\n",
    "# Having problems with Google Gemini setup? Then just skip Gemini; you'll get all the experience you need from GPT and Claude.\n",
    "\n",
    "openai = OpenAI()\n",
    "\n",
    "claude = anthropic.Anthropic()\n",
    "\n",
    "google.generativeai.configure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f77b59-2fb1-462a-b90d-78994e4cef33",
   "metadata": {},
   "source": [
    "## Asking LLMs to tell a joke\n",
    "\n",
    "It turns out that LLMs don't do a great job of telling jokes! Let's compare a few models.\n",
    "Later we will be putting LLMs to better use!\n",
    "\n",
    "### What information is included in the API\n",
    "\n",
    "Typically we'll pass to the API:\n",
    "- The name of the model that should be used\n",
    "- A system message that gives overall context for the role the LLM is playing\n",
    "- A user message that provides the actual prompt\n",
    "\n",
    "There are other parameters that can be used, including **temperature** which is typically between 0 and 1; higher for more random output; lower for more focused and deterministic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "378a0296-59a2-45c6-82eb-941344d3eeff",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are an assistant that is great at telling jokes\"\n",
    "user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d56a0f-2a3d-484d-9344-0efa6862aff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    {\"role\": \"system\", \"content\": system_message},\n",
    "    {\"role\": \"user\", \"content\": user_prompt}\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPT-3.5-Turbo\n",
    "\n",
    "completion = openai.chat.completions.create(model='gpt-3.5-turbo', messages=prompts)\n",
    "print(completion.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPT-4o-mini\n",
    "# Temperature setting controls creativity\n",
    "\n",
    "completion = openai.chat.completions.create(\n",
    "    model='gpt-4o-mini',\n",
    "    messages=prompts,\n",
    "    temperature=0.7\n",
    ")\n",
    "print(completion.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GPT-4o\n",
    "\n",
    "completion = openai.chat.completions.create(\n",
    "    model='gpt-4o',\n",
    "    messages=prompts,\n",
    "    temperature=0.4\n",
    ")\n",
    "print(completion.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Claude 3.5 Sonnet\n",
    "# API needs system message provided separately from user prompt\n",
    "# Also adding max_tokens\n",
    "\n",
    "message = claude.messages.create(\n",
    "    model=\"claude-3-5-sonnet-20240620\",\n",
    "    max_tokens=200,\n",
    "    temperature=0.7,\n",
    "    system=system_message,\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": user_prompt},\n",
    "    ],\n",
    ")\n",
    "\n",
    "print(message.content[0].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Claude 3.5 Sonnet again\n",
    "# Now let's add in streaming back results\n",
    "\n",
    "result = claude.messages.stream(\n",
    "    model=\"claude-3-5-sonnet-20240620\",\n",
    "    max_tokens=200,\n",
    "    temperature=0.7,\n",
    "    system=system_message,\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": user_prompt},\n",
    "    ],\n",
    ")\n",
    "\n",
    "with result as stream:\n",
    "    for text in stream.text_stream:\n",
    "            print(text, end=\"\", flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The API for Gemini has a slightly different structure\n",
    "\n",
    "gemini = google.generativeai.GenerativeModel(\n",
    "    model_name='gemini-1.5-flash',\n",
    "    system_instruction=system_message\n",
    ")\n",
    "response = gemini.generate_content(user_prompt)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83ddb483-4f57-4668-aeea-2aade3a9e573",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be serious! GPT-4o-mini with the original question\n",
    "\n",
    "prompts = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
    "    {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution?\"}\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "749f50ab-8ccd-4502-a521-895c3f0808a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Have it stream back results in markdown\n",
    "\n",
    "stream = openai.chat.completions.create(\n",
    "    model='gpt-4o',\n",
    "    messages=prompts,\n",
    "    temperature=0.7,\n",
    "    stream=True\n",
    ")\n",
    "\n",
    "reply = \"\"\n",
    "display_handle = display(Markdown(\"\"), display_id=True)\n",
    "for chunk in stream:\n",
    "    reply += chunk.choices[0].delta.content or ''\n",
    "    reply = reply.replace(\"```\",\"\").replace(\"markdown\",\"\")\n",
    "    update_display(Markdown(reply), display_id=display_handle.display_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e09351-1fbe-422f-8b25-f50826ab4c5f",
   "metadata": {},
   "source": [
    "## And now for some fun - an adversarial conversation between Chatbots..\n",
    "\n",
    "You're already familar with prompts being organized into lists like:\n",
    "\n",
    "```\n",
    "[\n",
    "    {\"role\": \"system\", \"content\": \"system message here\"},\n",
    "    {\"role\": \"user\", \"content\": \"user prompt here\"}\n",
    "]\n",
    "```\n",
    "\n",
    "In fact this structure can be used to reflect a longer conversation history:\n",
    "\n",
    "```\n",
    "[\n",
    "    {\"role\": \"system\", \"content\": \"system message here\"},\n",
    "    {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
    "    {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
    "    {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
    "]\n",
    "```\n",
    "\n",
    "And we can use this approach to engage in a longer interaction with history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
    "# We're using cheap versions of models so the costs will be minimal\n",
    "\n",
    "gpt_model = \"gpt-4o-mini\"\n",
    "claude_model = \"claude-3-haiku-20240307\"\n",
    "\n",
    "gpt_system = \"You are a chatbot who is very argumentative; \\\n",
    "you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
    "\n",
    "claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
    "everything the other person says, or find common ground. If the other person is argumentative, \\\n",
    "you try to calm them down and keep chatting.\"\n",
    "\n",
    "gpt_messages = [\"Hi there\"]\n",
    "claude_messages = [\"Hi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1df47dc7-b445-4852-b21b-59f0e6c2030f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_gpt():\n",
    "    messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
    "    for gpt, claude in zip(gpt_messages, claude_messages):\n",
    "        messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
    "        messages.append({\"role\": \"user\", \"content\": claude})\n",
    "    completion = openai.chat.completions.create(\n",
    "        model=gpt_model,\n",
    "        messages=messages\n",
    "    )\n",
    "    return completion.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606",
   "metadata": {},
   "outputs": [],
   "source": [
    "call_gpt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_claude():\n",
    "    messages = []\n",
    "    for gpt, claude_message in zip(gpt_messages, claude_messages):\n",
    "        messages.append({\"role\": \"user\", \"content\": gpt})\n",
    "        messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
    "    messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
    "    message = claude.messages.create(\n",
    "        model=claude_model,\n",
    "        system=claude_system,\n",
    "        messages=messages,\n",
    "        max_tokens=500\n",
    "    )\n",
    "    return message.content[0].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01395200-8ae9-41f8-9a04-701624d3fd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "call_claude()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08c2279e-62b0-4671-9590-c82eb8d1e1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "call_gpt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpt_messages = [\"Hi there\"]\n",
    "claude_messages = [\"Hi\"]\n",
    "\n",
    "print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
    "print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
    "\n",
    "for i in range(5):\n",
    "    gpt_next = call_gpt()\n",
    "    print(f\"GPT:\\n{gpt_next}\\n\")\n",
    "    gpt_messages.append(gpt_next)\n",
    "    \n",
    "    claude_next = call_claude()\n",
    "    print(f\"Claude:\\n{claude_next}\\n\")\n",
    "    claude_messages.append(claude_next)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392d2072-4e5d-424c-b4b4-098d7e3ead2d",
   "metadata": {},
   "source": [
    "# And now for a 3 way convo including Gemini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb0c319c-0226-4c6d-99bc-a9167fa86005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
    "# We're using cheap versions of models so the costs will be minimal\n",
    "\n",
    "gpt_model = \"gpt-4o-mini\"\n",
    "claude_model = \"claude-3-haiku-20240307\"\n",
    "\n",
    "gpt_system = \"You are a chatbot who is very argumentative; \\\n",
    "you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
    "\n",
    "claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
    "everything the other people in the conversation say, or find common ground. If another person is argumentative, \\\n",
    "you try to calm them down and keep chatting.\"\n",
    "\n",
    "gemini_system = \"You are an extremely knowledgeable and know-it-all counselor chatbot.  You try to help resolve disagreements, \\\n",
    "and if a person is either too argumentative or too polite, you cannot help but to use quotes from famous psychologists to teach \\\n",
    "your students to be kind yet maintain boundaries.\"\n",
    "\n",
    "gemini_instance = google.generativeai.GenerativeModel(\n",
    "    model_name='gemini-1.5-flash',\n",
    "    system_instruction=gemini_system\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "041b1596-a646-4321-a89a-9a51046d9b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpt_messages = [\"Hi there\"]\n",
    "claude_messages = [\"Hi\"]\n",
    "gemini_messages = [\"How is everyone?\"]\n",
    "gpt_name = \"Bob\"\n",
    "claude_name = \"Larry\"\n",
    "gemini_name = \"Frank\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcd0a633-d506-4b68-8411-46f3fbe34752",
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_joined_user_msg(msg1, msg1_name, msg2, msg2_name):\n",
    "    return msg1_name + ' said: ' + msg1 + '. \\n\\nThen ' + msg2_name + ' said: ' + msg2 + '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aef8ba5-1d93-4473-8c5f-707a12d8a1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_gpt(return_msgs=False):\n",
    "    messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
    "    for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
    "        messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
    "        messages.append({\"role\": \"user\", \"content\": construct_joined_user_msg(claude, claude_name, gemini, gemini_name)})\n",
    "    if return_msgs: return messages\n",
    "    completion = openai.chat.completions.create(\n",
    "        model=gpt_model,\n",
    "        messages=messages\n",
    "    )\n",
    "    return completion.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36365e7a-43b4-4a0d-a241-fff1440509d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "call_gpt(return_msgs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ebebebe-8255-4ca5-9335-258f93e3181f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_claude(return_msgs=False):\n",
    "    messages = []\n",
    "    for gpt, claude_msg, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
    "        messages.append({\"role\": \"user\", \"content\": construct_joined_user_msg(gemini, gemini_name, gpt, gpt_name)})\n",
    "        messages.append({\"role\": \"assistant\", \"content\": claude_msg})\n",
    "    messages.append({\"role\": \"user\", \"content\": gpt_name + \" said \" + gpt_messages[-1]})\n",
    "    if return_msgs: return messages\n",
    "    message = claude.messages.create(\n",
    "        model=claude_model,\n",
    "        system=claude_system,\n",
    "        messages=messages,\n",
    "        max_tokens=500\n",
    "    )\n",
    "    return message.content[0].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2da7997-a1fe-43fe-9970-4014f665e501",
   "metadata": {},
   "outputs": [],
   "source": [
    "call_claude(return_msgs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94497894-8cb9-4da4-8671-edede74055f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_gemini(return_msgs=False):\n",
    "    messages = []\n",
    "    for gpt, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
    "        messages.append({\"role\": \"user\", \"parts\": construct_joined_user_msg(gpt, gpt_name, claude, claude_name)})\n",
    "        messages.append({\"role\": \"model\", \"parts\": gemini})\n",
    "    messages.append({\"role\": \"user\", \"parts\": construct_joined_user_msg(gpt_messages[-1], gpt_name, claude_messages[-1], claude_name)})\n",
    "    if return_msgs: return messages\n",
    "    message = gemini_instance.generate_content(messages)\n",
    "    return message.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fddff0fc-0cc3-4473-9d55-effe445ef1ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "call_gemini(return_msgs=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1988abf3-1986-40f0-b804-c02b54472b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
    "print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
    "print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
    "\n",
    "for i in range(5):\n",
    "    gpt_next = call_gpt()\n",
    "    print(f\"GPT aka {gpt_name}:\\n{gpt_next}\\n\")\n",
    "    gpt_messages.append(gpt_next)\n",
    "    \n",
    "    claude_next = call_claude()\n",
    "    print(f\"Claude aka {claude_name}:\\n{claude_next}\\n\")\n",
    "    claude_messages.append(claude_next)\n",
    "\n",
    "    gemini_next = call_gemini()\n",
    "    print(f\"Gemini aka {gemini_name}:\\n{gemini_next}\\n\")\n",
    "    gemini_messages.append(gemini_next)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b72906b3-8c4a-4c15-8508-01118d33782a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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