242 lines
7.3 KiB
Plaintext
242 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "1b809d22-d170-4db3-a298-1740ce06b534",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Udemy Course >> LLM Engineering: Master AI and LLMs\n",
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"#Student: Jay\n",
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"#Date: Apr 20, 2025\n",
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"#Home work: Day1 - Summmarize website using local LLama\n",
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"\n",
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"import os\n",
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"import requests\n",
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"from dotenv import load_dotenv\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"
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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": 10,
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"id": "01e91579-7e32-4c4d-9cc9-c06d13c16209",
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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": 2,
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"id": "8d780fba-868c-4216-88f5-1e3ca5ad43ed",
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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": 3,
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"id": "839b645f-90ee-434d-b0bd-1cb4e574a8de",
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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": 4,
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"id": "ef2453e8-3eca-4f6d-8ccf-9e5274b589a7",
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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"
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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": 5,
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"id": "6ec397d5-e9b0-411d-8bdb-66605273cb11",
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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\": \"You are a snarky assistant\"},\n",
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" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\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": 6,
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"id": "76aed9eb-a085-4687-859d-817c771156fa",
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"metadata": {},
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"outputs": [],
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"source": [
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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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" ]"
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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": 7,
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"id": "26de4682-cf4f-4b7e-8cb2-049f7f46b758",
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"metadata": {},
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"outputs": [],
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"source": [
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"def summarize(url):\n",
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" website = Website(url)\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_for(website) \n",
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" )\n",
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" return response.choices[0].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": 8,
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"id": "16b2532a-d44c-4903-83ec-0b828a2d1b92",
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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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" 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": "code",
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"execution_count": 11,
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"id": "86af4905-5d5c-47c9-b9b2-27257452ff94",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"**Anthropic Website Summary**\n",
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"=====================================\n",
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"\n",
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"### Mission and Values\n",
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"\n",
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"Anthropic's mission is to build AI that serves humanity's long-term well-being. They focus on designing powerful technologies with human benefit at their foundation, aiming to demonstrate responsible AI development in practice.\n",
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"\n",
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"### Notable Releases\n",
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"\n",
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"#### 2025\n",
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"\n",
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"* **Claude 3.7 Sonnet**: Anthropic's most intelligent AI model, now available.\n",
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"* Recent news articles:\n",
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"\t+ \"Tracing the thoughts of a large language model: Interpretability\"\n",
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"\t+ \"Anthropic Economic Index: Societal Impacts\"\n",
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"\n",
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"### Products and Solutions\n",
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"\n",
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"* **Claude**: A suite of AI tools for building applications and custom experiences with human benefit in mind.\n",
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"* **Claude Overview**, **API Platform**, and various other products, including:\n",
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"\t+ **Claude 3.5 Haiku**\n",
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"\t+ **Claude 3 Opus**\n",
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"\n",
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"### Research and Commitments\n",
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"\n",
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"* The Anthropic Academy: A learning platform for developers to build AI solutions with Claude.\n",
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"* Responsible scaling policy and alignment science initiatives.\n",
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"\n",
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"### News Section (Selection)**\n",
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"\n",
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"Anthropic's recent news articles:\n",
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"* \"Claude extended thinking\"\n",
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"* \"Alignment faking in large language models\"\n",
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"\n",
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"### Company Information\n",
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"\n",
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"For more information on Anthropic, including company, careers, and help resources, follow the provided links."
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"display_summary(\"https://anthropic.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": "a5151062-614e-44ff-b341-d3f64e28aa93",
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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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