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|
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{ |
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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": "41136d6f-07bc-4f6f-acba-784b8e5707b1", |
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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": "8612b4f7-5c31-48f3-8423-261914509617", |
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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": "508bd442-7860-4215-b0f2-57f7adefd807", |
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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": "cc7e8ada-4f8d-4090-be64-4aa72e03ac58", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's just make sure the model is loaded\n", |
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"\n", |
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"!ollama pull 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": "4afd2e56-191a-4e31-949e-9b9376a39b5a", |
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"metadata": { |
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"scrolled": true |
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}, |
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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": "code", |
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"execution_count": null, |
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"id": "365f3d83-2601-42fb-89cc-98a4e1f79e0d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n", |
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"response = ollama_via_openai.chat.completions.create(model=MODEL, messages=[{\"role\":\"user\", \"content\":message}])\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": "code", |
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"execution_count": null, |
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"id": "29c383ae-bf5b-41bc-b5af-a22f851745dc", |
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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": "dc61e30f-653f-4554-b1cd-6e61a0e2430a", |
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"metadata": { |
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"scrolled": true |
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}, |
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"outputs": [], |
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"source": [ |
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"ed = Website(\"https://edwarddonner.com\")\n", |
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"print(ed.title)\n", |
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"print(ed.text)" |
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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": "db2066fb-3079-4775-832a-dcc0f19beb6e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"\n", |
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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": "af81b070-b6fe-4b18-aa0b-c03cd76a0adf", |
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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": null, |
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"id": "4e66291b-23b1-4915-b6a3-11a4b6a4db66", |
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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": null, |
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"id": "67c92f47-4a3b-491f-af00-07fda470087e", |
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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": null, |
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"id": "db1b9085-e5e7-4ec9-a264-acc389085ada", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"messages_for(ed)" |
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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": "677bfc2f-19ac-46a0-b67e-a2b2ddf9cf6b", |
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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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" 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": null, |
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"id": "ee3242ba-b695-4b1e-8a91-2fdeb536c2e7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"summarize(\"https://edwarddonner.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": "85142cb8-ce0c-4c31-8b26-bb1744cf99ec", |
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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": null, |
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"id": "63db51a7-dd03-4514-8954-57156967f82c", |
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"metadata": { |
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"scrolled": true |
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}, |
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"outputs": [], |
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"source": [ |
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"display_summary(\"https://app.daily.dev/posts/bregman-arie-devops-exercises-linux-jenkins-aws-sre-prometheus-docker-python-ansible-git-k-yli9wthnf\")" |
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] |
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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 [conda env:base] *", |
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"language": "python", |
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"name": "conda-base-py" |
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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": "3.12.7" |
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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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