From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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455 lines
15 KiB
455 lines
15 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
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"metadata": {}, |
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"source": [ |
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"# Welcome to your first assignment!\n", |
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"\n", |
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"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
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"metadata": {}, |
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"source": [ |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
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" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
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" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
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" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
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" </span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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] |
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}, |
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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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"# HOMEWORK EXERCISE ASSIGNMENT\n", |
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"\n", |
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"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
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"\n", |
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"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
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"\n", |
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"**Benefits:**\n", |
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"1. No API charges - open-source\n", |
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"2. Data doesn't leave your box\n", |
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"\n", |
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"**Disadvantages:**\n", |
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"1. Significantly less power than Frontier Model\n", |
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"\n", |
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"## Recap on installation of Ollama\n", |
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"\n", |
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"Simply visit [ollama.com](https://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/](http://localhost:11434/)\n", |
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"\n", |
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"You should see the message `Ollama is running`. \n", |
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"\n", |
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"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
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"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
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"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
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"\n", |
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"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `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" |
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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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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
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"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
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"MODEL = \"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": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Create a messages list using the same format that we used for OpenAI\n", |
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"\n", |
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"messages = [\n", |
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" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\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": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"payload = {\n", |
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" \"model\": MODEL,\n", |
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" \"messages\": messages,\n", |
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" \"stream\": False\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": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
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"print(response.json()['message']['content'])" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
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"metadata": {}, |
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"source": [ |
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"# Introducing the ollama package\n", |
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"\n", |
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"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
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"\n", |
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"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
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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": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import ollama\n", |
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"\n", |
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"response = ollama.chat(model=MODEL, messages=messages)\n", |
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"print(response['message']['content'])" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", |
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"metadata": {}, |
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"source": [ |
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"## Alternative approach - using OpenAI python library to connect to 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": "23057e00-b6fc-4678-93a9-6b31cb704bff", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# There's actually an alternative approach that some people might prefer\n", |
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"# You can use the OpenAI client python library to call Ollama:\n", |
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"\n", |
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"from openai import OpenAI\n", |
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"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", |
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"\n", |
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"response = ollama_via_openai.chat.completions.create(\n", |
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" model=MODEL,\n", |
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" messages=messages\n", |
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")\n", |
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"\n", |
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"print(response.choices[0].message.content)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
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"metadata": {}, |
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"source": [ |
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"# NOW the exercise for you\n", |
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"\n", |
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"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." |
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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": "402d5686-4e76-4110-b65a-b3906c35c0a4", |
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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 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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"from openai import OpenAI\n", |
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"from requests.exceptions import RequestException\n", |
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"# If you get an error running this cell, then please head over to the troubleshooting notebook!" |
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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": "9cfeb2c1-a2e1-47af-bd62-253b703d8130", |
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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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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
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"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
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"MODEL = \"llama3.2\"\n", |
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"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', 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": "76991489-946f-492c-9f62-9f73a9e53b43", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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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"\n", |
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"class Website:\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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" self.title = \"No title found\"\n", |
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" self.text = \"No content found\"\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/91.0.4472.124 Safari/537.36'\n", |
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" }\n", |
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" \n", |
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" try:\n", |
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" response = requests.get(url, headers=headers, timeout=10)\n", |
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" response.raise_for_status() # Raises an HTTPError for bad responses\n", |
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" \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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" \n", |
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" # Get text content\n", |
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" self.text = ' '.join([p.get_text() for p in soup.find_all('p')])\n", |
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" if not self.text:\n", |
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" self.text = \"No content found\"\n", |
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"\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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" except requests.exceptions.SSLError:\n", |
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" raise ConnectionError(f\"SSL Certificate verification failed for {url}\")\n", |
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" except requests.exceptions.ConnectionError:\n", |
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" raise ConnectionError(f\"Failed to connect to {url}. Please check if the URL is correct and accessible.\")\n", |
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" except requests.exceptions.Timeout:\n", |
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" raise ConnectionError(f\"Connection timed out while trying to access {url}\")\n", |
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" except requests.exceptions.RequestException as e:\n", |
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" raise ConnectionError(f\"An error occurred while fetching the website: {str(e)}\")\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": "61d58b43-5ec5-4580-b963-8336aee8681e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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)" |
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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": "d5fe4e6a-24f4-483a-ae24-1e7ed1bf2a6f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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 summarise these too. Ignoring text that might be navigation related. \\n --- \\n\"\n", |
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" user_prompt += website.text\n", |
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" return user_prompt" |
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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": "e9c9876a-45ff-43ef-8315-b10acfd4b872", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"ms=Website(\"https://technicallysimple.me\")\n", |
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"print(user_prompt_for(ms))" |
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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": "f2369302-7b8c-465e-8606-b9cc0a51cb78", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Create a messages list using the same format that we used for OpenAI\n", |
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"def messages_for(website):\n", |
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" return [\n", |
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\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": "a73d69c1-87fb-4e82-94f1-50dd76fb5e60", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"me=Website(\"https://technicallysimple.me\")\n", |
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"print(messages_for(me))" |
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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": "16ae9026-1684-4cc3-9859-f2cd7d22fb52", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def summarise(url):\n", |
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" try:\n", |
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" aWebsite = Website(url)\n", |
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" response = ollama_via_openai.chat.completions.create(\n", |
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" model=MODEL,\n", |
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" messages=messages_for(aWebsite)\n", |
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" )\n", |
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" return response.choices[0].message.content\n", |
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" except ConnectionError as e:\n", |
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" return f\"Error: {str(e)}\"\n", |
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" except Exception as e:\n", |
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" return f\"An unexpected error occurred: {str(e)}\"" |
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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": "4e0f7264-9622-4751-83b9-a31c3c0c4589", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def display_summary(url):\n", |
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" try:\n", |
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" summary = summarise(url)\n", |
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" display(Markdown(summary))\n", |
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" except Exception as e:\n", |
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" print(f\"Failed to display summary: {str(e)}\")" |
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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": "40aba8f2-577f-4003-bf46-377cc815f243", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"address=input(\"Enter URL: \")\n", |
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"summarise(address)" |
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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": "d8659653-d3a1-4cc1-bbd8-a003fb22041f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"address=input(\"Enter URL: \")\n", |
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"display_summary(address)" |
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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": "aff32906-2401-4d92-b377-c91ee572e208", |
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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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