241 lines
7.6 KiB
Plaintext
241 lines
7.6 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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"\n",
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"import re, requests, ollama\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": 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'"
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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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"headers = {\n",
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" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
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"}\n",
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"\n",
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"class Website:\n",
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"\n",
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" def __init__(self, url):\n",
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" \"\"\"\n",
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" Create this Website object from the given url using the BeautifulSoup library\n",
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" \"\"\"\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.content, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
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" irrelevant.decompose()\n",
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
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"\n",
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"openai = OpenAI()\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": "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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"# \"\"\"\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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"# Popular dev site https://projecteuler.net/\n",
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"# \"\"\"\n",
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"\n",
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"# question = \"\"\"\n",
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"# Who is Blessed Goodteam (https://www.linkedin.com/in/blessed-goodteam-49b3ab30a)? \\\n",
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"# How relevant is her work at Paint and Sip Uganda (https://paintandsipuganda.com/). \\\n",
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"# What can we learn from her?\n",
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"# \"\"\"\n",
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"\n",
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"question = \"\"\"\n",
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"How good at Software Development is Elijah Rwothoromo? \\\n",
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"He has a Wordpress site https://rwothoromo.wordpress.com/. \\\n",
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"He also has a LinkedIn profile https://www.linkedin.com/in/rwothoromoelaijah/. \\\n",
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"What can we learn from him?\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": "e14fd3a1-0aca-4794-a0e0-57458e111fc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Process URLs in the question to improve the prompt\n",
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"\n",
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"# Extract all URLs from the question string using regular expressions\n",
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"urls = re.findall(r'https?://[^\\s)]+', question)\n",
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"# print(urls)\n",
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"\n",
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"if len(urls) > 0:\n",
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" \n",
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" # Fetch the content for each URL using the Website class\n",
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" scraped_content = []\n",
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" for url in urls:\n",
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" print(f\"Scraping: {url}\")\n",
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" try:\n",
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" site = Website(url)\n",
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" content = f\"Content from {url}:\\n---\\n{site.text}\\n---\\n\" # delimiter ---\n",
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" scraped_content.append(content)\n",
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" except Exception as e:\n",
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" print(f\"Could not scrape {url}: {e}\")\n",
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" scraped_content.append(f\"Could not retrieve content from {url}.\\n\")\n",
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" \n",
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" # Combine all the scraped text into one string\n",
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" all_scraped_text = \"\\n\".join(scraped_content)\n",
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" \n",
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" # Update the question with the scraped content\n",
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" updated_question = f\"\"\"\n",
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" Based on the following information, please answer the user's original question.\n",
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" \n",
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" --- TEXT FROM WEBSITES ---\n",
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" {all_scraped_text}\n",
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" --- END TEXT FROM WEBSITES ---\n",
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" \n",
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" --- ORIGINAL QUESTION ---\n",
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" {question}\n",
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" \"\"\"\n",
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"else:\n",
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" updated_question = question\n",
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"\n",
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"# print(updated_question)\n",
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"\n",
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"# system prompt to be more accurate for AI to just analyze the provided text.\n",
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"system_prompt = \"You are an expert assistant. \\\n",
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"Analyze the user's question and the provided text from relevant websites to synthesize a comprehensive answer in markdown format.\\\n",
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"Provide a short summary, ignoring text that might be navigation-related.\"\n",
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"\n",
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"# Create the messages list with the newly updated prompt\n",
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"messages = [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": updated_question},\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": "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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"def get_gpt_response(question):\n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL_GPT,\n",
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" messages=messages,\n",
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" stream=True\n",
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" )\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)\n",
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"\n",
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"get_gpt_response(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": "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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"def get_llama_response(question):\n",
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" response = ollama.chat(\n",
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" model=MODEL_LLAMA,\n",
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" messages=messages,\n",
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" stream=False # just get the results, don't stream them\n",
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" )\n",
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" return response['message']['content']\n",
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"\n",
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"display(Markdown(get_llama_response(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": "157d5bb3-bed7-4fbd-9a5d-f2a14aaac869",
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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": "3.11.7"
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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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