diff --git a/week1/community-contributions/ranskills-week1-mathxpert.ipynb b/week1/community-contributions/ranskills-week1-mathxpert.ipynb new file mode 100644 index 0000000..fb5d4a6 --- /dev/null +++ b/week1/community-contributions/ranskills-week1-mathxpert.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", + "metadata": {}, + "source": [ + "# End of week 1 exercise\n", + "\n", + "## Dynamically pick an LLM provider to let MathXpert answer your math questions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1070317-3ed9-4659-abe3-828943230e03", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from enum import StrEnum\n", + "from getpass import getpass\n", + "\n", + "from dotenv import load_dotenv\n", + "from openai import OpenAI\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, clear_output, Markdown, Latex\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "markdown", + "id": "f169118a-645e-44e1-9a98-4f561adfbb08", + "metadata": {}, + "source": [ + "## Free Cloud Providers\n", + "\n", + "Grab your free API Keys from these generous sites:\n", + "\n", + "- https://openrouter.ai/\n", + "- https://ollama.com/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a456906-915a-4bfd-bb9d-57e505c5093f", + "metadata": {}, + "outputs": [], + "source": [ + "class Provider(StrEnum):\n", + " OLLAMA = 'Ollama'\n", + " OPENROUTER = 'OpenRouter'\n", + "\n", + "models: dict[Provider, str] = {\n", + " Provider.OLLAMA: 'gpt-oss:120b-cloud',\n", + " Provider.OPENROUTER: 'qwen/qwen3-4b:free'\n", + "}\n", + "\n", + "def get_api_key(env_name: str) -> str:\n", + " '''Gets the value from the environment, otherwise ask the user for it if not set'''\n", + " key = os.environ.get(env_name)\n", + " if not key:\n", + " key = getpass(f'Enter {env_name}:').strip()\n", + "\n", + " if key:\n", + " print(f'✅ {env_name} provided')\n", + " else:\n", + " print(f'❌ {env_name} provided')\n", + " return key\n", + "\n", + "\n", + "providers: dict[Provider, OpenAI] = {}\n", + "\n", + "if api_key := get_api_key('OLLAMA_API_KEY'):\n", + " providers[Provider.OLLAMA] = OpenAI(base_url='https://ollama.com/v1', api_key=api_key)\n", + "\n", + "if api_key := get_api_key('OPENROUTER_API_KEY'):\n", + " providers[Provider.OPENROUTER] = OpenAI(base_url='https://openrouter.ai/api/v1', api_key=api_key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", + "metadata": {}, + "outputs": [], + "source": [ + "def get_messages(question: str) -> list[dict[str, str]]:\n", + " \"\"\"Generate messages for the chat models.\"\"\"\n", + "\n", + " system_prompt = '''\n", + " You are MathXpert, an expert Mathematician who makes math fun to learn by relating concepts to real \n", + " practical usage to whip up the interest in learners.\n", + " \n", + " Explain step-by-step thoroughly how to solve a math problem. Respond in **LaTex**'\n", + " '''\n", + "\n", + " return [\n", + " {'role': 'system', 'content': system_prompt },\n", + " {'role': 'user', 'content': question},\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef72c85e", + "metadata": {}, + "outputs": [], + "source": [ + "get_messages('Explain how to solve a differentiation problem')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aae1579b-7a02-459d-81c6-0f775d2a1410", + "metadata": {}, + "outputs": [], + "source": [ + "selected_provider, client = next(iter(providers.items()))\n", + "\n", + "def on_provider_change(change):\n", + " global selected_provider, client\n", + "\n", + " selected_provider = change['new']\n", + " client = providers.get(selected_provider)\n", + "\n", + "\n", + "provider_selector = widgets.Dropdown(\n", + " options=list(providers.keys()),\n", + " description='Select an LLM provider:',\n", + " style={'description_width': 'initial'},\n", + ")\n", + "\n", + "provider_selector.observe(on_provider_change, names='value')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", + "metadata": {}, + "outputs": [], + "source": [ + "handle = display(None, display_id=True)\n", + "\n", + "def ask(client: OpenAI, model: str, question: str):\n", + " try:\n", + " prompt = get_messages(question=question)\n", + " response = client.chat.completions.create(\n", + " model=model,\n", + " messages=prompt,\n", + " stream=True,\n", + " )\n", + " \n", + " output = ''\n", + " for chunk in response:\n", + " output += chunk.choices[0].delta.content or ''\n", + " \n", + " handle.update(Latex(output))\n", + " except Exception as e:\n", + " clear_output(wait=True) \n", + " print(f'🔥 An error occurred: {e}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09bc9a11-adb4-4a9c-9c77-73b2b5a665cf", + "metadata": {}, + "outputs": [], + "source": [ + "display(provider_selector)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e01069b2-fd2c-446f-b385-09c1d9624225", + "metadata": {}, + "outputs": [], + "source": [ + "input_label = \"Ask your question (Type 'q' to quit): \"\n", + "question = input(input_label)\n", + "\n", + "while question.strip().lower() not in ['quit', 'q']:\n", + " clear_output(wait=True)\n", + " print(selected_provider, models[selected_provider])\n", + " model = models[selected_provider]\n", + " ask(client, model, question)\n", + "\n", + " question = input(input_label)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}