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jkumarkannan 3 weeks ago committed by GitHub
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  1. 241
      week1/community-contributions/day1_summarize website using local llama.ipynb

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week1/community-contributions/day1_summarize website using local llama.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1b809d22-d170-4db3-a298-1740ce06b534",
"metadata": {},
"outputs": [],
"source": [
"#Udemy Course >> LLM Engineering: Master AI and LLMs\n",
"#Student: Jay\n",
"#Date: Apr 20, 2025\n",
"#Home work: Day1 - Summmarize website using local LLama\n",
"\n",
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "01e91579-7e32-4c4d-9cc9-c06d13c16209",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8d780fba-868c-4216-88f5-1e3ca5ad43ed",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"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",
"}\n",
"\n",
"class Website:\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "839b645f-90ee-434d-b0bd-1cb4e574a8de",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ef2453e8-3eca-4f6d-8ccf-9e5274b589a7",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6ec397d5-e9b0-411d-8bdb-66605273cb11",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "76aed9eb-a085-4687-859d-817c771156fa",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "26de4682-cf4f-4b7e-8cb2-049f7f46b758",
"metadata": {},
"outputs": [],
"source": [
"def summarize(url):\n",
" website = Website(url)\n",
" ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"\n",
" response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages_for(website) \n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "16b2532a-d44c-4903-83ec-0b828a2d1b92",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "86af4905-5d5c-47c9-b9b2-27257452ff94",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Anthropic Website Summary**\n",
"=====================================\n",
"\n",
"### Mission and Values\n",
"\n",
"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",
"\n",
"### Notable Releases\n",
"\n",
"#### 2025\n",
"\n",
"* **Claude 3.7 Sonnet**: Anthropic's most intelligent AI model, now available.\n",
"* Recent news articles:\n",
"\t+ \"Tracing the thoughts of a large language model: Interpretability\"\n",
"\t+ \"Anthropic Economic Index: Societal Impacts\"\n",
"\n",
"### Products and Solutions\n",
"\n",
"* **Claude**: A suite of AI tools for building applications and custom experiences with human benefit in mind.\n",
"* **Claude Overview**, **API Platform**, and various other products, including:\n",
"\t+ **Claude 3.5 Haiku**\n",
"\t+ **Claude 3 Opus**\n",
"\n",
"### Research and Commitments\n",
"\n",
"* The Anthropic Academy: A learning platform for developers to build AI solutions with Claude.\n",
"* Responsible scaling policy and alignment science initiatives.\n",
"\n",
"### News Section (Selection)**\n",
"\n",
"Anthropic's recent news articles:\n",
"* \"Claude extended thinking\"\n",
"* \"Alignment faking in large language models\"\n",
"\n",
"### Company Information\n",
"\n",
"For more information on Anthropic, including company, careers, and help resources, follow the provided links."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_summary(\"https://anthropic.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5151062-614e-44ff-b341-d3f64e28aa93",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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