From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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129 lines
4.5 KiB
129 lines
4.5 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": "d25b0aef-3e5e-4026-90ee-2b373bf262b7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Step 0: Import Libraries\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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"import ollama\n", |
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"from openai import OpenAI\n", |
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"import requests\n", |
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"\n", |
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"# Step 1: Set Constants and Variables\n", |
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"print(\"[INFO] Setting constants and variable ...\")\n", |
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"WEBSITE_URL = \"https://arxiv.org/\"\n", |
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"MODEL = \"llama3.2\"\n", |
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"approaches = [\"local-call\", \"python-package\", \"openai-python-library\"]\n", |
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"approach = approaches[2]\n", |
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"\n", |
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"# Step 1: Scrape Website\n", |
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"print(\"[INFO] Scraping website ...\")\n", |
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"url_response = requests.get(\n", |
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" url=WEBSITE_URL,\n", |
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" headers={\"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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"soup = BeautifulSoup(\n", |
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" markup=url_response.content,\n", |
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" features=\"html.parser\"\n", |
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" )\n", |
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"website_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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"website_text = soup.body.get_text(\n", |
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" separator=\"\\n\",\n", |
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" strip=True\n", |
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" )\n", |
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"\n", |
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"# Step 2: Create Prompts\n", |
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"print(\"[INFO] Creating system prompt ...\")\n", |
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"system_prompt = \"You are an assistant that analyzes the contents of a \\\n", |
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" website and provides a short summary, ignoring text that might be \\\n", |
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" navigation related. Respond in markdown.\"\n", |
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"\n", |
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"print(\"[INFO] Creating user prompt ...\")\n", |
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"user_prompt = f\"You are looking at a website titled {website_title}\"\n", |
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"user_prompt += \"\\nBased on the contents of the website, please provide \\\n", |
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" a short summary of this website in markdown. If the website \\\n", |
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" includes news or announcements, summarize them, too. The contents \\\n", |
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" of this website are as follows:\\n\\n\"\n", |
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"user_prompt += website_text\n", |
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"\n", |
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"# Step 3: Make Messages List\n", |
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"print(\"[INFO] Making messages list ...\")\n", |
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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}\n", |
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"]\n", |
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"\n", |
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"# Step 4: Call Model and Print Results\n", |
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"if approach == \"local-call\":\n", |
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" response = requests.post(\n", |
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" url=\"http://localhost:11434/api/chat\",\n", |
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" json={\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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" },\n", |
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" headers={\"Content-Type\": \"application/json\"}\n", |
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" )\n", |
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" print(\"[INFO] Printing result ...\")\n", |
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" display(Markdown(response.json()[\"message\"][\"content\"]))\n", |
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"elif approach == \"python-package\":\n", |
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" response = ollama.chat(\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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" )\n", |
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" print(\"[INFO] Printing result ...\")\n", |
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" display(Markdown(response[\"message\"][\"content\"]))\n", |
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"elif approach == \"openai-python-library\":\n", |
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" ollama_via_openai = OpenAI(\n", |
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" base_url=\"http://localhost:11434/v1\",\n", |
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" 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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" print(\"[INFO] Printing result ...\")\n", |
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" display(Markdown(response.choices[0].message.content))\n", |
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"else:\n", |
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" raise ValueError(f\"[INFO] Invalid approach! Please select an approach from {approaches} and try again.\")" |
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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": "b0a6676e-fb43-4725-9389-2acd74c13c4e", |
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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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"language_info": { |
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"codemirror_mode": { |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.12.8" |
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"nbformat": 4, |
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