From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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131 lines
3.5 KiB
131 lines
3.5 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "6418dce8-3ad0-4da9-81de-b3bf57956086", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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": "75b7849a-841b-4525-90b9-b9fd003516fb", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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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" def __init__(self, url):\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": "45c07164-3276-47f3-8620-a5d0ca6a8d24", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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.\"" |
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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": "b334629a-cf2a-49fa-b198-edd73493720f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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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] |
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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": "e4dd0855-302d-4423-9b8b-80c4bbb9ab31", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"website = Website(\"https://cnn.com\")" |
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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": "65c6cc43-a16a-4337-8c3d-4ab10ee0377a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"messages = [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}]" |
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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": "59799f7b-a244-4572-9296-34e4b87ba026", |
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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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"MODEL = \"llama3.2\"\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": "code", |
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"execution_count": null, |
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"id": "a0c03050-60d2-4165-9d8a-27eb57455704", |
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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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