162 lines
4.2 KiB
Plaintext
162 lines
4.2 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": "code",
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"execution_count": null,
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"id": "c1070317-3ed9-4659-abe3-828943230e03",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"import os\n",
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"import json\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown\n",
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"from openai import OpenAI"
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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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"\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\n",
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"OLLAMA_BASE_URL = \"http://localhost:11434/v1\""
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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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"\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 api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
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" print(\"API key looks good so far\")\n",
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"else:\n",
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" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
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" \n",
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"openai = OpenAI()\n",
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"ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='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": "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; type over this to ask something new\n",
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"\n",
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"question = \"\"\"\n",
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"Please explain what this code does and why:\n",
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"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\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": "df0d958f",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"\"\"You are individual that possesses a unique\n",
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"and highly valuable combination of deep technical\n",
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"expertise and excellent communication skills.\n",
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"You grasp complex, specialized concepts and then distill\n",
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"them into simple, understandable terms for people without\n",
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"the same technical background.\n",
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"\n",
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"Present your answer as markdown\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": "13506dd4",
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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\": question}\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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"\n",
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"response = openai.chat.completions.create(model=MODEL_GPT, messages=messages)\n",
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"result = response.choices[0].message.content\n",
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"display(Markdown(result))\n",
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"\n"
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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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"\n",
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"response = ollama.chat.completions.create(model=MODEL_LLAMA, messages=messages)\n",
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"result = response.choices[0].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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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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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": "3.11.10"
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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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