From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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159 lines
5.2 KiB
159 lines
5.2 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "568fd96a-8cf6-42aa-b9cf-74b7aa383595", |
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"metadata": {}, |
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"source": [ |
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"# Ollama Website Summarizer\n", |
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"## Scrape websites and summarize them locally using Ollama\n", |
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"\n", |
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"This script is a complete example of the day 1 program, which uses OpenAI API to summarize websites, altered to use techniques from the day 2 exercise to call Ollama models locally." |
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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": "a9502a0f-d7be-4489-bb7f-173207e802b6", |
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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 ollama\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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"\n", |
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"MODEL = \"llama3.2\"\n", |
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"\n", |
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"# A class to represent a Webpage\n", |
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"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n", |
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"\n", |
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"# Some websites need you to use proper headers when fetching them:\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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"# A function that writes a User Prompt that asks for summaries of websites:\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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"# Create a messages list for a summarize prompt given a website\n", |
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"\n", |
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"def create_summarize_prompt(website):\n", |
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" return [\n", |
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" {\"role\": \"system\", \"content\": \"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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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", |
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" ]\n", |
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"\n", |
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"# And now: call Ollama to summarize\n", |
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"\n", |
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"def summarize(url):\n", |
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" website = Website(url)\n", |
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" messages = create_summarize_prompt(website)\n", |
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" response = ollama.chat(model=MODEL, messages=messages)\n", |
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" return response['message']['content']\n", |
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" \n", |
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"# A function to display this nicely in the Jupyter output, using markdown\n", |
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"\n", |
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"def display_summary(url):\n", |
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" summary = summarize(url)\n", |
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" display(Markdown(summary))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "037627b0-b039-4ca4-a6d4-84ad8fc6a013", |
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"metadata": {}, |
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"source": [ |
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"## Pre-requisites\n", |
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"\n", |
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"Before we can run the script above, we need to make sure Ollama is running on your machine!\n", |
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"\n", |
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"Simply visit ollama.com and install!\n", |
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"\n", |
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"Once complete, the ollama server should already be running locally.\n", |
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"If you visit:\n", |
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"http://localhost:11434/\n", |
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"\n", |
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"You should see the message Ollama is running." |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6c2d84fd-2a9b-476d-84ad-4b8522d47023", |
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"metadata": {}, |
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"source": [ |
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"## Run!\n", |
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"\n", |
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"Shift+Enter the code below to summarize a website.\n", |
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"\n", |
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"### NOTE!\n", |
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"\n", |
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"This will only work with websites that return HTML content, and may return unexpected results for SPAs that are created with JS." |
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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": "100829ba-8278-409b-bc0a-82ac28e1149f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "ffe4e760-dfa6-43fa-89c4-beea547707ac", |
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"metadata": {}, |
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"source": [ |
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"Edit the URL above, or add code blocks of your own to try it out!" |
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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": "3.11.11" |
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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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