{ "cells": [ { "cell_type": "markdown", "id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", "metadata": {}, "source": [ "## Also trying the amazing reasoning model DeepSeek\n", "\n", "Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", "This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", "\n", "Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" ] }, { "cell_type": "code", "execution_count": null, "id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", "metadata": {}, "outputs": [], "source": [ "!ollama pull deepseek-r1:1.5b" ] }, { "cell_type": "code", "execution_count": null, "id": "4bdcd35a", "metadata": {}, "outputs": [], "source": [ "!ollama pull deepseek-r1:8b" ] }, { "cell_type": "markdown", "id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", "metadata": {}, "source": [ "# NOW the exercise for you\n", "\n", "Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." ] }, { "cell_type": "code", "execution_count": null, "id": "1c106420", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import requests\n", "import ollama\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display" ] }, { "cell_type": "code", "execution_count": null, "id": "22d62f00", "metadata": {}, "outputs": [], "source": [ "# Constants\n", "\n", "OLLAMA_API = \"http://localhost:11434/api/chat\"\n", "HEADERS = {\"Content-Type\": \"application/json\"}\n", "MODEL = \"deepseek-r1:8b\"" ] }, { "cell_type": "code", "execution_count": null, "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", "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": null, "id": "4449b7dc", "metadata": {}, "outputs": [], "source": [ "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n", "\n", "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": null, "id": "daca9448", "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": null, "id": "0ec9d5d2", "metadata": {}, "outputs": [], "source": [ "# See how this function creates exactly the format above\n", "\n", "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": null, "id": "6e1ab04a", "metadata": {}, "outputs": [], "source": [ "# And now: call the OpenAI API. You will get very familiar with this!\n", "\n", "def summarize(url):\n", " website = Website(url)\n", " response = ollama.chat(\n", " model = MODEL,\n", " messages = messages_for(website)\n", " )\n", " return response['message']['content']" ] }, { "cell_type": "code", "execution_count": null, "id": "0d3b5628", "metadata": {}, "outputs": [], "source": [ "def display_summary(url):\n", " summary = summarize(url)\n", " display(Markdown(summary))" ] }, { "cell_type": "code", "execution_count": null, "id": "938e5633", "metadata": {}, "outputs": [], "source": [ "display_summary(\"https://edwarddonner.com\")" ] } ], "metadata": { "kernelspec": { "display_name": "llms", "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 }