week2/community-contributions/beatnik_jokes.ipynb

{
 "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": "2b268b6e-0ba4-461e-af86-74a41f4d681f",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n",
    "            <span style=\"color:#900;\">I'm continually improving these labs, adding more examples and exercises.\n",
    "            At the start of each week, it's worth checking you have the latest code.<br/>\n",
    "            First do a <a href=\"https://chatgpt.com/share/6734e705-3270-8012-a074-421661af6ba9\">git pull and merge your changes as needed</a>. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!<br/><br/>\n",
    "            After you've pulled the code, from the llm_engineering directory, in an Anaconda prompt (PC) or Terminal (Mac), run:<br/>\n",
    "            <code>conda env update --f environment.yml</code><br/>\n",
    "            Or if you used virtualenv rather than Anaconda, then run this from your activated environment in a Powershell (PC) or Terminal (Mac):<br/>\n",
    "            <code>pip install -r requirements.txt</code>\n",
    "            <br/>Then restart the kernel (Kernel menu >> Restart Kernel and Clear Outputs Of All Cells) to pick up the changes.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n",
    "            <span style=\"color:#f71;\">Here's a link to resources for the course. This includes links to all the slides.<br/>\n",
    "            <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
    "            Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "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 could now create API keys for Anthropic and Google in addition to OpenAI.\n",
    "\n",
    "**Please note:** if you'd prefer to avoid extra API costs, feel free to skip setting up Anthopic and Google! You can see me do it, and focus on OpenAI for the course. You could also substitute Anthropic and/or Google for Ollama, using the exercise you did in week 1.\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",
    "### Also - adding DeepSeek if you wish\n",
    "\n",
    "Optionally, if you'd like to also use DeepSeek, create an account [here](https://platform.deepseek.com/), create a key [here](https://platform.deepseek.com/api_keys) and top up with at least the minimum $2 [here](https://platform.deepseek.com/top_up).\n",
    "\n",
    "### Adding API keys to your .env file\n",
    "\n",
    "When you get your API keys, you need to set them as environment variables by adding them to your `.env` file.\n",
    "\n",
    "```\n",
    "OPENAI_API_KEY=xxxx\n",
    "ANTHROPIC_API_KEY=xxxx\n",
    "GOOGLE_API_KEY=xxxx\n",
    "DEEPSEEK_API_KEY=xxxx\n",
    "```\n",
    "\n",
    "Afterwards, you may need to restart the Jupyter Lab Kernel (the Python process that sits behind this notebook) via the Kernel menu, and then rerun the cells from the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 anthropic\n",
    "from IPython.display import Markdown, display, update_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import for google\n",
    "# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n",
    "# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n",
    "\n",
    "import google.generativeai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1179b4c5-cd1f-4131-a876-4c9f3f38d2ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI API Key exists and begins sk-proj-\n",
      "Anthropic API Key exists and begins sk-ant-\n",
      "Google API Key exists and begins AIzaSyCN\n"
     ]
    }
   ],
   "source": [
    "# Load environment variables in a file called .env\n",
    "# Print the key prefixes to help with any debugging\n",
    "\n",
    "load_dotenv(override=True)\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set\")\n",
    "    \n",
    "if anthropic_api_key:\n",
    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
    "else:\n",
    "    print(\"Anthropic API Key not set\")\n",
    "\n",
    "if google_api_key:\n",
    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"Google API Key not set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "797fe7b0-ad43-42d2-acf0-e4f309b112f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to OpenAI, Anthropic\n",
    "\n",
    "openai = OpenAI()\n",
    "\n",
    "claude = anthropic.Anthropic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c072312-4ab1-4a85-8ec0-1c91b281596c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<anthropic.Anthropic object at 0x000001F3583A42F0>\n"
     ]
    }
   ],
   "source": [
    "print(claude)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "425ed580-808d-429b-85b0-6cba50ca1d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the set up code for Gemini\n",
    "# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\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": 7,
   "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": 8,
   "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": 9,
   "id": "3b3879b6-9a55-4fed-a18c-1ea2edfaf397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why did the data scientist break up with the statistician?\n",
      "\n",
      "Because they couldn't find a common mean!\n"
     ]
    }
   ],
   "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": 10,
   "id": "3d2d6beb-1b81-466f-8ed1-40bf51e7adbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why did the data scientist break up with the statistician?  \n",
      "\n",
      "Because she felt like she was just one of the many variables in his life!\n"
     ]
    }
   ],
   "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": 11,
   "id": "f1f54beb-823f-4301-98cb-8b9a49f4ce26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why did the data scientist break up with the logistic regression model?\n",
      "\n",
      "Because it couldn't handle the relationship's complexity and kept giving them mixed signals!\n"
     ]
    }
   ],
   "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": 12,
   "id": "1ecdb506-9f7c-4539-abae-0e78d7f31b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's one for the data scientists:\n",
      "\n",
      "Why did the data scientist bring a ladder to work?\n",
      "\n",
      "Because they heard the data was skewed and needed to be normalized!\n",
      "\n",
      "*Alternative data science jokes:*\n",
      "\n",
      "Why do data scientists make great partners?\n",
      "Because they know the importance of a good correlation!\n",
      "\n",
      "What's a data scientist's favorite drink?\n",
      "Root beer, because it's square root beer! \n",
      "\n",
      "These are pretty nerdy, but I figured they'd get a chuckle out of a room full of data scientists! 😄\n"
     ]
    }
   ],
   "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-latest\",\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": 13,
   "id": "769c4017-4b3b-4e64-8da7-ef4dcbe3fd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's one for the data scientists:\n",
      "\n",
      " a gardener?data scientist become\n",
      "\n",
      " could grow decision trees! 🌳\n",
      "\n",
      " jokes:tive\n",
      "\n",
      " kind of music?a scientist's favorite\n",
      " and blues!m\n",
      "\n",
      "do data scientists always confuse Halloween and Christmas?\n",
      "= Dec 25!  Oct 31 \n",
      " one's a classic binary number system joke)\n",
      "\n",
      " they couldn't find their pencil?y when\n",
      "There's a statistically significant chance someone took it!\"\n",
      "\n",
      " one - I've got datasets full of them! 😄"
     ]
    }
   ],
   "source": [
    "# Claude 3.5 Sonnet again\n",
    "# Now let's add in streaming back results\n",
    "# If the streaming looks strange, then please see the note below this cell!\n",
    "\n",
    "result = claude.messages.stream(\n",
    "    model=\"claude-3-5-sonnet-latest\",\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": "markdown",
   "id": "dd1e17bc-cd46-4c23-b639-0c7b748e6c5a",
   "metadata": {},
   "source": [
    "## A rare problem with Claude streaming on some Windows boxes\n",
    "\n",
    "2 students have noticed a strange thing happening with Claude's streaming into Jupyter Lab's output -- it sometimes seems to swallow up parts of the response.\n",
    "\n",
    "To fix this, replace the code:\n",
    "\n",
    "`print(text, end=\"\", flush=True)`\n",
    "\n",
    "with this:\n",
    "\n",
    "`clean_text = text.replace(\"\\n\", \" \").replace(\"\\r\", \" \")`  \n",
    "`print(clean_text, end=\"\", flush=True)`\n",
    "\n",
    "And it should work fine!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6df48ce5-70f8-4643-9a50-b0b5bfdb66ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why did the data scientist break up with the time series analyst?\n",
      "\n",
      "Because he said she was too predictable, and he needed someone with more VARiety!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The API for Gemini has a slightly different structure.\n",
    "# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n",
    "# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
    "\n",
    "gemini = google.generativeai.GenerativeModel(\n",
    "    model_name='gemini-2.0-flash-exp',\n",
    "    system_instruction=system_message\n",
    ")\n",
    "response = gemini.generate_content(user_prompt)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49009a30-037d-41c8-b874-127f61c4aa3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why did the data scientist break up with the time series model? \n",
      "\n",
      "Because it wasn't very *present*!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# As an alternative way to use Gemini that bypasses Google's python API library,\n",
    "# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n",
    "\n",
    "gemini_via_openai_client = OpenAI(\n",
    "    api_key=google_api_key, \n",
    "    base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
    ")\n",
    "\n",
    "response = gemini_via_openai_client.chat.completions.create(\n",
    "    model=\"gemini-2.0-flash-exp\",\n",
    "    messages=prompts\n",
    ")\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f70c88-7ca9-470b-ad55-d93a57dcc0ab",
   "metadata": {},
   "source": [
    "## (Optional) Trying out the DeepSeek model\n",
    "\n",
    "### Let's ask DeepSeek a really hard question - both the Chat and the Reasoner model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d0019fb-f6a8-45cb-962b-ef8bf7070d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeepSeek API Key exists and begins xxx\n"
     ]
    }
   ],
   "source": [
    "# Optionally if you wish to try DeekSeek, you can also use the OpenAI client library\n",
    "\n",
    "deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
    "\n",
    "if deepseek_api_key:\n",
    "    print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n",
    "else:\n",
    "    print(\"DeepSeek API Key not set - please skip to the next section if you don't wish to try the DeepSeek API\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c72c871e-68d6-4668-9c27-96d52b77b867",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "AuthenticationError",
     "evalue": "Error code: 401 - {'error': {'message': 'Authentication Fails (no such user)', 'type': 'authentication_error', 'param': None, 'code': 'invalid_request_error'}}",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mAuthenticationError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Using DeepSeek Chat\u001b[39;00m\n\u001b[32m      3\u001b[39m deepseek_via_openai_client = OpenAI(\n\u001b[32m      4\u001b[39m     api_key=deepseek_api_key, \n\u001b[32m      5\u001b[39m     base_url=\u001b[33m\"\u001b[39m\u001b[33mhttps://api.deepseek.com\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m      6\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m response = \u001b[43mdeepseek_via_openai_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      9\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdeepseek-chat\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m     10\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     11\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m     13\u001b[39m \u001b[38;5;28mprint\u001b[39m(response.choices[\u001b[32m0\u001b[39m].message.content)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\Projects\\llm_engineering\\llms\\Lib\\site-packages\\openai\\_utils\\_utils.py:279\u001b[39m, in \u001b[36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    277\u001b[39m             msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    278\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m279\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\Projects\\llm_engineering\\llms\\Lib\\site-packages\\openai\\resources\\chat\\completions\\completions.py:879\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, reasoning_effort, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m    837\u001b[39m \u001b[38;5;129m@required_args\u001b[39m([\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m    838\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m    839\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    876\u001b[39m     timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = NOT_GIVEN,\n\u001b[32m    877\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m    878\u001b[39m     validate_response_format(response_format)\n\u001b[32m--> \u001b[39m\u001b[32m879\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    880\u001b[39m \u001b[43m        \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    881\u001b[39m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    882\u001b[39m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m    883\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    884\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    885\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43maudio\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43maudio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    886\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrequency_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    887\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    888\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    889\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    890\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    891\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    892\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    893\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    894\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodalities\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodalities\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    895\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    896\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mparallel_tool_calls\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    897\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mprediction\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mprediction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    898\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpresence_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    899\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    900\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    901\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mseed\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    902\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    903\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    904\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstore\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    905\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    906\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    907\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    908\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    909\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    910\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_logprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_logprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    911\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    912\u001b[39m \u001b[43m                \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43muser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    913\u001b[39m \u001b[43m            \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    914\u001b[39m \u001b[43m            \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    915\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    916\u001b[39m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    917\u001b[39m \u001b[43m            \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m    918\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    919\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    920\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    921\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    922\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\Projects\\llm_engineering\\llms\\Lib\\site-packages\\openai\\_base_client.py:1242\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m   1228\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m   1229\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   1230\u001b[39m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1237\u001b[39m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   1238\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m   1239\u001b[39m     opts = FinalRequestOptions.construct(\n\u001b[32m   1240\u001b[39m         method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m   1241\u001b[39m     )\n\u001b[32m-> \u001b[39m\u001b[32m1242\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\Projects\\llm_engineering\\llms\\Lib\\site-packages\\openai\\_base_client.py:919\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[39m\n\u001b[32m    916\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    917\u001b[39m     retries_taken = \u001b[32m0\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m919\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    920\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    921\u001b[39m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    922\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    923\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    924\u001b[39m \u001b[43m    \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    925\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\Projects\\llm_engineering\\llms\\Lib\\site-packages\\openai\\_base_client.py:1023\u001b[39m, in \u001b[36mSyncAPIClient._request\u001b[39m\u001b[34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[39m\n\u001b[32m   1020\u001b[39m         err.response.read()\n\u001b[32m   1022\u001b[39m     log.debug(\u001b[33m\"\u001b[39m\u001b[33mRe-raising status error\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1023\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._make_status_error_from_response(err.response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1025\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._process_response(\n\u001b[32m   1026\u001b[39m     cast_to=cast_to,\n\u001b[32m   1027\u001b[39m     options=options,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1031\u001b[39m     retries_taken=retries_taken,\n\u001b[32m   1032\u001b[39m )\n",
      "\u001b[31mAuthenticationError\u001b[39m: Error code: 401 - {'error': {'message': 'Authentication Fails (no such user)', 'type': 'authentication_error', 'param': None, 'code': 'invalid_request_error'}}"
     ]
    }
   ],
   "source": [
    "# Using DeepSeek Chat\n",
    "\n",
    "deepseek_via_openai_client = OpenAI(\n",
    "    api_key=deepseek_api_key, \n",
    "    base_url=\"https://api.deepseek.com\"\n",
    ")\n",
    "\n",
    "response = deepseek_via_openai_client.chat.completions.create(\n",
    "    model=\"deepseek-chat\",\n",
    "    messages=prompts,\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50b6e70f-700a-46cf-942f-659101ffeceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "challenge = [{\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
    "             {\"role\": \"user\", \"content\": \"How many words are there in your answer to this prompt\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66d1151c-2015-4e37-80c8-16bc16367cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using DeepSeek Chat with a harder question! And streaming results\n",
    "\n",
    "stream = deepseek_via_openai_client.chat.completions.create(\n",
    "    model=\"deepseek-chat\",\n",
    "    messages=challenge,\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)\n",
    "\n",
    "print(\"Number of words:\", len(reply.split(\" \")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43a93f7d-9300-48cc-8c1a-ee67380db495",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using DeepSeek Reasoner - this may hit an error if DeepSeek is busy\n",
    "# It's over-subscribed (as of 28-Jan-2025) but should come back online soon!\n",
    "# If this fails, come back to this in a few days..\n",
    "\n",
    "response = deepseek_via_openai_client.chat.completions.create(\n",
    "    model=\"deepseek-reasoner\",\n",
    "    messages=challenge\n",
    ")\n",
    "\n",
    "reasoning_content = response.choices[0].message.reasoning_content\n",
    "content = response.choices[0].message.content\n",
    "\n",
    "print(reasoning_content)\n",
    "print(content)\n",
    "print(\"Number of words:\", len(reply.split(\" \")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09e6b5c-6816-4cd3-a5cd-a20e4171b1a0",
   "metadata": {},
   "source": [
    "## Back to OpenAI with a serious question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 that responds in Markdown\"},\n",
    "    {\"role\": \"user\", \"content\": \"How do I decide if a business problem is suitable for an LLM solution? Please respond in Markdown.\"}\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "749f50ab-8ccd-4502-a521-895c3f0808a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Deciding if a business problem is suitable for a Large Language Model (LLM) solution involves assessing various factors related to the problem, the capabilities of LLMs, and the potential impact. Here's a guide to help you make this decision:\n",
       "\n",
       "### 1. **Understand the Problem Domain**\n",
       "\n",
       "- **Nature of the Problem:** Is the problem related to language processing, such as text generation, summarization, sentiment analysis, question answering, translation, etc.?\n",
       "- **Complexity and Ambiguity:** Does the problem involve complex or ambiguous language understanding that might benefit from the nuanced capabilities of an LLM?\n",
       "\n",
       "### 2. **Assess the Suitability of LLMs**\n",
       "\n",
       "- **Language-Centric Tasks:** LLMs are particularly strong in tasks that require understanding and generating human language.\n",
       "- **Need for Contextual Understanding:** If the problem requires understanding context and nuance in language, LLMs are likely suitable.\n",
       "- **Content Generation:** If the task involves creating coherent and contextually relevant text, LLMs can be effective.\n",
       "\n",
       "### 3. **Evaluate Data Availability and Quality**\n",
       "\n",
       "- **Data Requirements:** Do you have access to the necessary data to train or fine-tune an LLM if required?\n",
       "- **Data Quality and Quantity:** Is the data high-quality and sufficient in volume to support the model's needs?\n",
       "\n",
       "### 4. **Consider the Business Impact**\n",
       "\n",
       "- **Value Addition:** Will using an LLM add significant value over existing solutions or methods?\n",
       "- **Cost-Benefit Analysis:** Does the potential benefit outweigh the costs involved in implementing and maintaining an LLM solution?\n",
       "\n",
       "### 5. **Technical Feasibility**\n",
       "\n",
       "- **Infrastructure:** Do you have the necessary infrastructure or resources to deploy and maintain an LLM solution?\n",
       "- **Scalability:** Can the solution scale to meet your business needs?\n",
       "\n",
       "### 6. **Ethical and Compliance Considerations**\n",
       "\n",
       "- **Bias and Fairness:** Are you prepared to address potential biases in LLM outputs and ensure fairness?\n",
       "- **Privacy and Security:** Does the solution comply with data privacy and security regulations?\n",
       "\n",
       "### 7. **Long-term Viability**\n",
       "\n",
       "- **Maintenance and Updates:** Consider the long-term maintenance and the need for updates as new models and techniques emerge.\n",
       "- **Adaptability:** Can the solution adapt to changing business needs or advancements in technology?\n",
       "\n",
       "### Conclusion\n",
       "\n",
       "If your business problem aligns well with the strengths of LLMs, you have the necessary data and resources, and the solution provides a clear business benefit while addressing ethical considerations, then an LLM solution is likely suitable. Otherwise, you may need to explore alternative approaches or refine your problem definition."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "1d10e705-db48-4290-9dc8-9efdb4e31323",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#900;\">Before you continue</h2>\n",
    "            <span style=\"color:#900;\">\n",
    "                Be sure you understand how the conversation above is working, and in particular how the <code>messages</code> list is being populated. Add print statements as needed. Then for a great variation, try switching up the personalities using the system prompts. Perhaps one can be pessimistic, and one optimistic?<br/>\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
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    "# More advanced exercises\n",
    "\n",
    "Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n",
    "\n",
    "Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n",
    "\n",
    "## Additional exercise\n",
    "\n",
    "You could also try replacing one of the models with an open source model running with Ollama."
   ]
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    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#181;\">Business relevance</h2>\n",
    "            <span style=\"color:#181;\">This structure of a conversation, as a list of messages, is fundamental to the way we build conversational AI assistants and how they are able to keep the context during a conversation. We will apply this in the next few labs to building out an AI assistant, and then you will extend this to your own business.</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
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