From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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123 lines
3.8 KiB
123 lines
3.8 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
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"metadata": {}, |
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"source": [ |
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"# Day 2 EXERCISE Solution:\n", |
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"\n", |
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"Upgraded day 1 project that scrapes and summarizes any webpage using an Open Source model running locally via Ollama instead of OpenAI\n", |
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"\n", |
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"## Note:-\n", |
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"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative: \n", |
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"1. Run `ollama pull llama3.2:1b` from a Terminal or Powershell\n", |
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"2. **Ctrl + /** to comment this code line below: `MODEL = \"llama3.2\"`\n", |
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"3. same **Ctrl + /** to uncomment: `MODEL = \"llama3.2:1b\"`" |
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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": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
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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 requests\n", |
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"from bs4 import BeautifulSoup\n", |
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"from IPython.display import Markdown, display\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": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
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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 = \"llama3.2\"\n", |
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"MODEL = \"llama3.2:1b\"\n", |
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"# MODEL = \"deepseek-r1:1.5b\"" |
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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": "6de38216-6d1c-48c4-877b-86d403f4e0f8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"class Website:\n", |
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" def __init__(self, url):\n", |
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" self.url = url\n", |
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" response = requests.get(url)\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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"\n", |
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"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", |
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" and provides a short summary, ignoring text that might be navigation related. \\\n", |
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" Respond in markdown.\"\n", |
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"\n", |
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"\n", |
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"def user_prompt_for(website):\n", |
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" user_prompt = f\"You are looking at a website titled {website.title}\"\n", |
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" user_prompt += \"\\nThe contents of this website is as follows; \\\n", |
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" please provide a short summary of this website in markdown. \\\n", |
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" If it includes news or announcements, then summarize these too.\\n\\n\"\n", |
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" user_prompt += website.text\n", |
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" return user_prompt\n", |
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"\n", |
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"\n", |
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"def messages_for(website):\n", |
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" return [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
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" ]\n", |
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"\n", |
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"\n", |
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"def summary(url):\n", |
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" website = Website(url)\n", |
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" response = ollama.chat(\n", |
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" model = MODEL,\n", |
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" messages = messages_for(website)\n", |
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" )\n", |
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" return display(Markdown(response['message']['content']))\n", |
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"\n", |
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"\n", |
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"summary(\"https://edwarddonner.com\")\n", |
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"# summary(\"https://cnn.com\")\n", |
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"# summary(\"https://anthropic.com\")" |
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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": "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": "3.10.7" |
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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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