{ "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 them through their APIs.." ] }, { "cell_type": "markdown", "id": "2b268b6e-0ba4-461e-af86-74a41f4d681f", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Important Note - Please read me

\n", " 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.
\n", " First do a git pull and merge your changes as needed. Check out the GitHub guide for instructions. Any problems? Try asking ChatGPT to clarify how to merge - or contact me!
\n", " \n", "
\n", "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Reminder about the resources page

\n", " Here's a link to resources for the course. This includes links to all the slides.
\n", " https://edwarddonner.com/2024/11/13/llm-engineering-resources/
\n", " Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "85cfe275-4705-4d30-abea-643fbddf1db0", "metadata": {}, "source": [ "## Setting up your keys - OPTIONAL!\n", "\n", "We're now going to try asking a bunch of models some questions!\n", "\n", "This is totally optional. If you have keys to Anthropic, Gemini or others, then you can add them in.\n", "\n", "If you'd rather not spend the extra, then just watch me do it!\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", "For DeepSeek, visit https://platform.deepseek.com/ \n", "For Groq, visit https://console.groq.com/ \n", "For Grok, visit https://console.x.ai/ \n", "\n", "\n", "You can also use OpenRouter as your one-stop-shop for many of these! OpenRouter is \"the unified interface for LLMs\":\n", "\n", "For OpenRouter, visit https://openrouter.ai/ \n", "\n", "\n", "With each of the above, you typically have to navigate to:\n", "1. Their billing page to add the minimum top-up (except Gemini, Groq, Google, OpenRouter may have free tiers)\n", "2. Their API key page to collect your API key\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", "GROQ_API_KEY=xxxx\n", "GROK_API_KEY=xxxx\n", "OPENROUTER_API_KEY=xxxx\n", "```\n", "\n", "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Any time you change your .env file

\n", " Remember to Save it! And also rerun load_dotenv(override=True)
\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "de23bb9e-37c5-4377-9a82-d7b6c648eeb6", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import requests\n", "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "from IPython.display import Markdown, display" ] }, { "cell_type": "code", "execution_count": null, "id": "b0abffac", "metadata": {}, "outputs": [], "source": [ "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", "deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n", "groq_api_key = os.getenv('GROQ_API_KEY')\n", "grok_api_key = os.getenv('GROK_API_KEY')\n", "openrouter_api_key = os.getenv('OPENROUTER_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 (and this is optional)\")\n", "\n", "if google_api_key:\n", " print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n", "else:\n", " print(\"Google API Key not set (and this is optional)\")\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 (and this is optional)\")\n", "\n", "if groq_api_key:\n", " print(f\"Groq API Key exists and begins {groq_api_key[:4]}\")\n", "else:\n", " print(\"Groq API Key not set (and this is optional)\")\n", "\n", "if grok_api_key:\n", " print(f\"Grok API Key exists and begins {grok_api_key[:4]}\")\n", "else:\n", " print(\"Grok API Key not set (and this is optional)\")\n", "\n", "if openrouter_api_key:\n", " print(f\"OpenRouter API Key exists and begins {openrouter_api_key[:3]}\")\n", "else:\n", " print(\"OpenRouter API Key not set (and this is optional)\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "985a859a", "metadata": {}, "outputs": [], "source": [ "# Connect to OpenAI client library\n", "# A thin wrapper around calls to HTTP endpoints\n", "\n", "openai = OpenAI()\n", "\n", "# For Gemini, DeepSeek and Groq, we can use the OpenAI python client\n", "# Because Google and DeepSeek have endpoints compatible with OpenAI\n", "# And OpenAI allows you to change the base_url\n", "\n", "anthropic_url = \"https://api.anthropic.com/v1/\"\n", "gemini_url = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", "deepseek_url = \"https://api.deepseek.com\"\n", "groq_url = \"https://api.groq.com/openai/v1\"\n", "grok_url = \"https://api.x.ai/v1\"\n", "openrouter_url = \"https://openrouter.ai/api/v1\"\n", "ollama_url = \"http://localhost:11434/v1\"\n", "\n", "anthropic = OpenAI(api_key=anthropic_api_key, base_url=anthropic_url)\n", "gemini = OpenAI(api_key=google_api_key, base_url=gemini_url)\n", "deepseek = OpenAI(api_key=deepseek_api_key, base_url=deepseek_url)\n", "groq = OpenAI(api_key=groq_api_key, base_url=groq_url)\n", "grok = OpenAI(api_key=grok_api_key, base_url=grok_url)\n", "openrouter = OpenAI(base_url=openrouter_url, api_key=openrouter_api_key)\n", "ollama = OpenAI(api_key=\"ollama\", base_url=ollama_url)" ] }, { "cell_type": "code", "execution_count": null, "id": "16813180", "metadata": {}, "outputs": [], "source": [ "tell_a_joke = [\n", " {\"role\": \"user\", \"content\": \"Tell a joke for a student on the journey to becoming an expert in LLM Engineering\"},\n", "]" ] }, { "cell_type": "code", "execution_count": null, "id": "23e92304", "metadata": {}, "outputs": [], "source": [ "response = openai.chat.completions.create(model=\"gpt-4.1-mini\", messages=tell_a_joke)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "e03c11b9", "metadata": {}, "outputs": [], "source": [ "response = anthropic.chat.completions.create(model=\"claude-sonnet-4-5-20250929\", messages=tell_a_joke)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "id": "ab6ea76a", "metadata": {}, "source": [ "## Training vs Inference time scaling" ] }, { "cell_type": "code", "execution_count": null, "id": "afe9e11c", "metadata": {}, "outputs": [], "source": [ "easy_puzzle = [\n", " {\"role\": \"user\", \"content\": \n", " \"You toss 2 coins. One of them is heads. What's the probability the other is tails? Answer with the probability only.\"},\n", "]" ] }, { "cell_type": "code", "execution_count": null, "id": "4a887eb3", "metadata": {}, "outputs": [], "source": [ "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=easy_puzzle, reasoning_effort=\"minimal\")\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "5f854d01", "metadata": {}, "outputs": [], "source": [ "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=easy_puzzle, reasoning_effort=\"low\")\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "f45fc55b", "metadata": {}, "outputs": [], "source": [ "response = openai.chat.completions.create(model=\"gpt-5-mini\", messages=easy_puzzle, reasoning_effort=\"minimal\")\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "id": "ca713a5c", "metadata": {}, "source": [ "## Testing out the best models on the planet" ] }, { "cell_type": "code", "execution_count": null, "id": "df1e825b", "metadata": {}, "outputs": [], "source": [ "hard = \"\"\"\n", "On a bookshelf, two volumes of Pushkin stand side by side: the first and the second.\n", "The pages of each volume together have a thickness of 2 cm, and each cover is 2 mm thick.\n", "A worm gnawed (perpendicular to the pages) from the first page of the first volume to the last page of the second volume.\n", "What distance did it gnaw through?\n", "\"\"\"\n", "hard_puzzle = [\n", " {\"role\": \"user\", \"content\": hard}\n", "]" ] }, { "cell_type": "code", "execution_count": null, "id": "8f6a7827", "metadata": {}, "outputs": [], "source": [ "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=hard_puzzle, reasoning_effort=\"minimal\")\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "d693ac0d", "metadata": {}, "outputs": [], "source": [ "response = anthropic.chat.completions.create(model=\"claude-sonnet-4-5-20250929\", messages=hard_puzzle)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "7de7818f", "metadata": {}, "outputs": [], "source": [ "response = openai.chat.completions.create(model=\"gpt-5\", messages=hard_puzzle)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "de1dc5fa", "metadata": {}, "outputs": [], "source": [ "response = gemini.chat.completions.create(model=\"gemini-2.5-pro\", messages=hard_puzzle)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "id": "9a9faf98", "metadata": {}, "source": [ "## A spicy challenge to test the competitive spirit" ] }, { "cell_type": "code", "execution_count": null, "id": "fc1824ad", "metadata": {}, "outputs": [], "source": [ "dilemma_prompt = \"\"\"\n", "You and a partner are contestants on a game show. You're each taken to separate rooms and given a choice:\n", "Cooperate: Choose \"Share\" — if both of you choose this, you each win $1,000.\n", "Defect: Choose \"Steal\" — if one steals and the other shares, the stealer gets $2,000 and the sharer gets nothing.\n", "If both steal, you both get nothing.\n", "Do you choose to Steal or Share? Pick one.\n", "\"\"\"\n", "\n", "dilemma = [\n", " {\"role\": \"user\", \"content\": dilemma_prompt},\n", "]\n" ] }, { "cell_type": "code", "execution_count": null, "id": "09807f1a", "metadata": {}, "outputs": [], "source": [ "response = anthropic.chat.completions.create(model=\"claude-sonnet-4-5-20250929\", messages=dilemma)\n", "display(Markdown(response.choices[0].message.content))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "230f49d6", "metadata": {}, "outputs": [], "source": [ "response = groq.chat.completions.create(model=\"openai/gpt-oss-120b\", messages=dilemma)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "421f08df", "metadata": {}, "outputs": [], "source": [ "response = deepseek.chat.completions.create(model=\"deepseek-reasoner\", messages=dilemma)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "2599fc6e", "metadata": {}, "outputs": [], "source": [ "response = grok.chat.completions.create(model=\"grok-4\", messages=dilemma)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "id": "162752e9", "metadata": {}, "source": [ "## Going local\n", "\n", "Just use the OpenAI library pointed to localhost:11434/v1" ] }, { "cell_type": "code", "execution_count": null, "id": "ba03ee29", "metadata": {}, "outputs": [], "source": [ "requests.get(\"http://localhost:11434/\").content\n", "\n", "# If not running, run ollama serve at a command line" ] }, { "cell_type": "code", "execution_count": null, "id": "f363cd6b", "metadata": {}, "outputs": [], "source": [ "!ollama pull llama3.2" ] }, { "cell_type": "code", "execution_count": null, "id": "96e97263", "metadata": {}, "outputs": [], "source": [ "# Only do this if you have a large machine - at least 16GB RAM\n", "\n", "!ollama pull gpt-oss:20b" ] }, { "cell_type": "code", "execution_count": null, "id": "a3bfc78a", "metadata": {}, "outputs": [], "source": [ "response = ollama.chat.completions.create(model=\"llama3.2\", messages=easy_puzzle)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "9a5527a3", "metadata": {}, "outputs": [], "source": [ "response = ollama.chat.completions.create(model=\"gpt-oss:20b\", messages=easy_puzzle)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "id": "a0628309", "metadata": {}, "source": [ "## Gemini and Anthropic Client Library\n", "\n", "We're going via the OpenAI Python Client Library, but the other providers have their libraries too" ] }, { "cell_type": "code", "execution_count": null, "id": "f0a8ab2b-6134-4104-a1bc-c3cd7ea4cd36", "metadata": {}, "outputs": [], "source": [ "from google import genai\n", "\n", "client = genai.Client()\n", "\n", "response = client.models.generate_content(\n", " model=\"gemini-2.5-flash-lite\", contents=\"Describe the color Blue to someone who's never been able to see in 1 sentence\"\n", ")\n", "print(response.text)" ] }, { "cell_type": "code", "execution_count": null, "id": "df7b6c63", "metadata": {}, "outputs": [], "source": [ "from anthropic import Anthropic\n", "\n", "client = Anthropic()\n", "\n", "response = client.messages.create(\n", " model=\"claude-sonnet-4-5-20250929\",\n", " messages=[{\"role\": \"user\", \"content\": \"Describe the color Blue to someone who's never been able to see in 1 sentence\"}],\n", " max_tokens=100\n", ")\n", "print(response.content[0].text)" ] }, { "cell_type": "markdown", "id": "45a9d0eb", "metadata": {}, "source": [ "## Routers and Abtraction Layers\n", "\n", "Starting with the wonderful OpenRouter.ai - it can connect to all the models above!\n", "\n", "Visit openrouter.ai and browse the models.\n", "\n", "Here's one we haven't seen yet: GLM 4.5 from Chinese startup z.ai" ] }, { "cell_type": "code", "execution_count": null, "id": "9fac59dc", "metadata": {}, "outputs": [], "source": [ "response = openrouter.chat.completions.create(model=\"z-ai/glm-4.5\", messages=tell_a_joke)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "markdown", "id": "b58908e6", "metadata": {}, "source": [ "## And now a first look at the powerful, mighty (and quite heavyweight) LangChain" ] }, { "cell_type": "code", "execution_count": null, "id": "02e145ad", "metadata": {}, "outputs": [], "source": [ "from langchain_openai import ChatOpenAI\n", "\n", "llm = ChatOpenAI(model=\"gpt-5-mini\")\n", "response = llm.invoke(tell_a_joke)\n", "\n", "display(Markdown(response.content))" ] }, { "cell_type": "markdown", "id": "92d49785", "metadata": {}, "source": [ "## Finally - my personal fave - the wonderfully lightweight LiteLLM" ] }, { "cell_type": "code", "execution_count": null, "id": "63e42515", "metadata": {}, "outputs": [], "source": [ "from litellm import completion\n", "response = completion(model=\"openai/gpt-4.1\", messages=tell_a_joke)\n", "reply = response.choices[0].message.content\n", "display(Markdown(reply))" ] }, { "cell_type": "code", "execution_count": null, "id": "36f787f5", "metadata": {}, "outputs": [], "source": [ "print(f\"Input tokens: {response.usage.prompt_tokens}\")\n", "print(f\"Output tokens: {response.usage.completion_tokens}\")\n", "print(f\"Total tokens: {response.usage.total_tokens}\")\n", "print(f\"Total cost: {response._hidden_params[\"response_cost\"]*100:.4f} cents\")" ] }, { "cell_type": "markdown", "id": "28126494", "metadata": {}, "source": [ "## Now - let's use LiteLLM to illustrate a Pro-feature: prompt caching" ] }, { "cell_type": "code", "execution_count": null, "id": "f8a91ef4", "metadata": {}, "outputs": [], "source": [ "with open(\"hamlet.txt\", \"r\", encoding=\"utf-8\") as f:\n", " hamlet = f.read()\n", "\n", "loc = hamlet.find(\"Speak, man\")\n", "print(hamlet[loc:loc+100])" ] }, { "cell_type": "code", "execution_count": null, "id": "7f34f670", "metadata": {}, "outputs": [], "source": [ "question = [{\"role\": \"user\", \"content\": \"In Hamlet, when Laertes asks 'Where is my father?' what is the reply?\"}]" ] }, { "cell_type": "code", "execution_count": null, "id": "9db6c82b", "metadata": {}, "outputs": [], "source": [ "response = completion(model=\"gemini/gemini-2.5-flash-lite\", messages=question)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "228b7e7c", "metadata": {}, "outputs": [], "source": [ "print(f\"Input tokens: {response.usage.prompt_tokens}\")\n", "print(f\"Output tokens: {response.usage.completion_tokens}\")\n", "print(f\"Total tokens: {response.usage.total_tokens}\")\n", "print(f\"Total cost: {response._hidden_params[\"response_cost\"]*100:.4f} cents\")" ] }, { "cell_type": "code", "execution_count": null, "id": "11e37e43", "metadata": {}, "outputs": [], "source": [ "question[0][\"content\"] += \"\\n\\nFor context, here is the entire text of Hamlet:\\n\\n\"+hamlet" ] }, { "cell_type": "code", "execution_count": null, "id": "37afb28b", "metadata": {}, "outputs": [], "source": [ "response = completion(model=\"gemini/gemini-2.5-flash-lite\", messages=question)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "d84edecf", "metadata": {}, "outputs": [], "source": [ "print(f\"Input tokens: {response.usage.prompt_tokens}\")\n", "print(f\"Output tokens: {response.usage.completion_tokens}\")\n", "print(f\"Cached tokens: {response.usage.prompt_tokens_details.cached_tokens}\")\n", "print(f\"Total cost: {response._hidden_params[\"response_cost\"]*100:.4f} cents\")" ] }, { "cell_type": "code", "execution_count": null, "id": "515d1a94", "metadata": {}, "outputs": [], "source": [ "response = completion(model=\"gemini/gemini-2.5-flash-lite\", messages=question)\n", "display(Markdown(response.choices[0].message.content))" ] }, { "cell_type": "code", "execution_count": null, "id": "eb5dd403", "metadata": {}, "outputs": [], "source": [ "print(f\"Input tokens: {response.usage.prompt_tokens}\")\n", "print(f\"Output tokens: {response.usage.completion_tokens}\")\n", "print(f\"Cached tokens: {response.usage.prompt_tokens_details.cached_tokens}\")\n", "print(f\"Total cost: {response._hidden_params[\"response_cost\"]*100:.4f} cents\")" ] }, { "cell_type": "markdown", "id": "00f5a3b7", "metadata": {}, "source": [ "## Prompt Caching with OpenAI\n", "\n", "For OpenAI:\n", "\n", "https://platform.openai.com/docs/guides/prompt-caching\n", "\n", "> Cache hits are only possible for exact prefix matches within a prompt. To realize caching benefits, place static content like instructions and examples at the beginning of your prompt, and put variable content, such as user-specific information, at the end. This also applies to images and tools, which must be identical between requests.\n", "\n", "\n", "Cached input is 4X cheaper\n", "\n", "https://openai.com/api/pricing/" ] }, { "cell_type": "markdown", "id": "b98964f9", "metadata": {}, "source": [ "## Prompt Caching with Anthropic\n", "\n", "https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching\n", "\n", "You have to tell Claude what you are caching\n", "\n", "You pay 25% MORE to \"prime\" the cache\n", "\n", "Then you pay 10X less to reuse from the cache with inputs.\n", "\n", "https://www.anthropic.com/pricing#api" ] }, { "cell_type": "markdown", "id": "67d960dd", "metadata": {}, "source": [ "## Gemini supports both 'implicit' and 'explicit' prompt caching\n", "\n", "https://ai.google.dev/gemini-api/docs/caching?lang=python" ] }, { "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-4.1-mini and Claude-3.5-haiku\n", "# We're using cheap versions of models so the costs will be minimal\n", "\n", "gpt_model = \"gpt-4.1-mini\"\n", "claude_model = \"claude-3-5-haiku-latest\"\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", " response = openai.chat.completions.create(model=gpt_model, messages=messages)\n", " return response.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 = [{\"role\": \"system\", \"content\": claude_system}]\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", " response = anthropic.chat.completions.create(model=claude_model, messages=messages)\n", " return response.choices[0].message.content" ] }, { "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", "display(Markdown(f\"### GPT:\\n{gpt_messages[0]}\\n\"))\n", "display(Markdown(f\"### Claude:\\n{claude_messages[0]}\\n\"))\n", "\n", "for i in range(5):\n", " gpt_next = call_gpt()\n", " display(Markdown(f\"### GPT:\\n{gpt_next}\\n\"))\n", " gpt_messages.append(gpt_next)\n", " \n", " claude_next = call_claude()\n", " display(Markdown(f\"### Claude:\\n{claude_next}\\n\"))\n", " claude_messages.append(claude_next)" ] }, { "cell_type": "markdown", "id": "1d10e705-db48-4290-9dc8-9efdb4e31323", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
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Before you continue

\n", " \n", " Be sure you understand how the conversation above is working, and in particular how the messages 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?
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac", "metadata": {}, "source": [ "# 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", "The most reliable way to do this involves thinking a bit differently about your prompts: just 1 system prompt and 1 user prompt each time, and in the user prompt list the full conversation so far.\n", "\n", "Something like:\n", "\n", "```python\n", "system_prompt = \"\"\"\n", "You are Alex, a chatbot who is very argumentative; you disagree with anything in the conversation and you challenge everything, in a snarky way.\n", "You are in a conversation with Blake and Charlie.\n", "\"\"\"\n", "\n", "user_prompt = f\"\"\"\n", "You are Alex, in conversation with Blake and Charlie.\n", "The conversation so far is as follows:\n", "{conversation}\n", "Now with this, respond with what you would like to say next, as Alex.\n", "\"\"\"\n", "```\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." ] }, { "cell_type": "markdown", "id": "446c81e3-b67e-4cd9-8113-bc3092b93063", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
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Business relevance

\n", " 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.\n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "c23224f6-7008-44ed-a57f-718975f4e291", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 }