Added my contributions to community-contributions
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John Stoops
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week1/community-contributions/week1-tech-question-jds.ipynb
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week1/community-contributions/week1-tech-question-jds.ipynb
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{
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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": 1,
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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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"\n",
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"import os\n",
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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display, update_display\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": 2,
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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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"\n",
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"OLLAMA_API = \"http://localhost:11434/v1\"\n",
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"HEADERS = {\"Content-Type\": \"application/json\"}"
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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": 3,
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"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"API key looks good so far\n"
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]
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}
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],
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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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" "
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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": 4,
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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 system prompt and payloads;\n",
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"\n",
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"system_prompt = \"\"\"\n",
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"You are an expert on LLMs and writing python code. You are able to answer complex questions with\n",
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"detailed answers and explain what every line of code does. You can refactor the code when asked.\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": 7,
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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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"# Function to get answer, with streaming\n",
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"\n",
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"def llm_copilot(question, model):\n",
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" if 'llama' in model.lower():\n",
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" openai = OpenAI(base_url=OLLAMA_API, api_key='ollama')\n",
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" else:\n",
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" openai = OpenAI()\n",
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" \n",
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" stream = openai.chat.completions.create(\n",
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" model=model,\n",
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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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" ],\n",
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" stream=True\n",
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" )\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": 8,
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"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"Here's a revised version of your code:\n",
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"\n",
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"python\n",
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"if 'llama' in model.lower():\n",
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"\n",
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"\n",
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"OR if you want to keep the original style, you can modify it as follows:\n",
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"\n",
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"python\n",
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"if model.split('.')[-1] == 'llama3.2':\n",
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"\n",
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"\n",
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"In this second example, we use string indexing (`-1`) to get the last part of the `model` string after splitting at the dot (`.`) character.\n",
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"\n",
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"The first revised version uses Python's built-in string method `lower()` to convert `model` to lowercase and then checks if 'llama' is present in it. It returns True if the text contains \"llama\", otherwise, it will return False. \n",
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"\n",
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"However, both of these codes are using lazy evaluation, which means if you do this check inside a loop:\n",
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"\n",
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"python\n",
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"for i in range(100):\n",
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" print('llama')\n",
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"\n",
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"\n",
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"Python will use 'a' instead of 'llame' most of the time until `i == 98` because it has to wait for the condition to be met (and also does some lookup and look-around). If you want a case-insensitive search without this slowness, consider using a regular expression as shown below\n",
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"\n",
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"python\n",
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"import re\n",
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"\n",
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"if re.search(r' llama.', model):\n",
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"\n",
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"\n",
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"And if you still want that specific code structure, then use `replace` function as follows:\n",
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"\n",
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"python\n",
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"model = model.replace('llama', '')\n",
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"if model == '3.2':\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"None\n"
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]
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}
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],
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"source": [
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"# Ask question\n",
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"question = \"\"\"\n",
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"Change this code to check for just the 'llama' portion of text instead of the entire string:\n",
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"if model == 'llama3.2':\n",
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"\"\"\"\n",
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"\n",
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"print(llm_copilot(question, MODEL_LLAMA))"
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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": "1a4026cd-8967-4961-b26b-e3997307c4ba",
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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": "3.11.11"
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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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