155 lines
4.1 KiB
Plaintext
155 lines
4.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
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"metadata": {},
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"source": [
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"# End of week 1 exercise\n",
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"\n",
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"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
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"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0ea775a9-12c7-4a63-a676-d7bd0cdb100c",
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"metadata": {},
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"source": [
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"# imports\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from openai import OpenAI\n",
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"import ollama"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# constants\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set up environment\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found!\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
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"metadata": {},
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"outputs": [],
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"source": [
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"# here is the question\n",
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"question = \"\"\"\n",
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"Please explain why do tennis players often use topspin on their forehand shots, and what advantages does it provide?\n",
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"\"\"\" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "967aac6b-9f9c-4def-8659-d9382b0c59e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"You are a helpful tennis coach who answers questions about tennis rules, techniques, strategies, training, and equipment.\"\n",
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"user_prompt = \"Please give a detailed explanation to the following question: \" + question"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7936b5af-e912-4e0e-b43e-87673c4857cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"messages = [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get gpt-4o-mini to answer, with streaming\n",
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"openai = OpenAI()\n",
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"stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages, stream=True)\n",
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"response = \"\"\n",
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"display_handle = display(Markdown(\"\"), display_id=True)\n",
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"for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
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" update_display(Markdown(response), display_id=display_handle.display_id)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get Llama 3.2 to answer\n",
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"response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n",
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"result = response['message']['content']\n",
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"display(Markdown(result))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "29e9cdd3-5adc-4428-9758-f761dc91783a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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