From a3e8be560f3aca88b2a6ece5fe39c676a78b2bb8 Mon Sep 17 00:00:00 2001 From: nandoclal Date: Thu, 23 Oct 2025 10:21:53 -0600 Subject: [PATCH] week1: add Fernando notebooks (day2 + exercise), outputs cleared --- .../fernando/day2.ipynb | 494 ++++++++++++++++++ .../fernando/week1 EXERCISE.ipynb | 175 +++++++ 2 files changed, 669 insertions(+) create mode 100644 week1/community-contributions/fernando/day2.ipynb create mode 100644 week1/community-contributions/fernando/week1 EXERCISE.ipynb diff --git a/week1/community-contributions/fernando/day2.ipynb b/week1/community-contributions/fernando/day2.ipynb new file mode 100644 index 0000000..4a6e7b5 --- /dev/null +++ b/week1/community-contributions/fernando/day2.ipynb @@ -0,0 +1,494 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", + "metadata": {}, + "source": [ + "# Welcome to the Day 2 Lab!\n" + ] + }, + { + "cell_type": "markdown", + "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Just before we get started --

\n", + " I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.
\n", + " https://edwarddonner.com/2024/11/13/llm-engineering-resources/
\n", + " Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "79ffe36f", + "metadata": {}, + "source": [ + "## First - let's talk about the Chat Completions API\n", + "\n", + "1. The simplest way to call an LLM\n", + "2. It's called Chat Completions because it's saying: \"here is a conversation, please predict what should come next\"\n", + "3. The Chat Completions API was invented by OpenAI, but it's so popular that everybody uses it!\n", + "\n", + "### We will start by calling OpenAI again - but don't worry non-OpenAI people, your time is coming!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e38f17a0", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv(override=True)\n", + "api_key = os.getenv('OPENAI_API_KEY')\n", + "\n", + "if not api_key:\n", + " print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", + "elif not api_key.startswith(\"sk-proj-\"):\n", + " print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n", + "else:\n", + " print(\"API key found and looks good so far!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "97846274", + "metadata": {}, + "source": [ + "## Do you know what an Endpoint is?\n", + "\n", + "If not, please review the Technical Foundations guide in the guides folder\n", + "\n", + "And, here is an endpoint that might interest you..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5af5c188", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "headers = {\"Authorization\": f\"Bearer {api_key}\", \"Content-Type\": \"application/json\"}\n", + "\n", + "payload = {\n", + " \"model\": \"gpt-5-nano\",\n", + " \"messages\": [\n", + " {\"role\": \"user\", \"content\": \"Tell me a fun fact\"}]\n", + "}\n", + "\n", + "payload" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d0ab242", + "metadata": {}, + "outputs": [], + "source": [ + "response = requests.post(\n", + " \"https://api.openai.com/v1/chat/completions\",\n", + " headers=headers,\n", + " json=payload\n", + ")\n", + "\n", + "response.json()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb11a9f6", + "metadata": {}, + "outputs": [], + "source": [ + "response.json()[\"choices\"][0][\"message\"][\"content\"]" + ] + }, + { + "cell_type": "markdown", + "id": "cea3026a", + "metadata": {}, + "source": [ + "# What is the openai package?\n", + "\n", + "It's known as a Python Client Library.\n", + "\n", + "It's nothing more than a wrapper around making this exact call to the http endpoint.\n", + "\n", + "It just allows you to work with nice Python code instead of messing around with janky json objects.\n", + "\n", + "But that's it. It's open-source and lightweight. Some people think it contains OpenAI model code - it doesn't!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "490fdf09", + "metadata": {}, + "outputs": [], + "source": [ + "# Create OpenAI client\n", + "\n", + "from openai import OpenAI\n", + "openai = OpenAI()\n", + "\n", + "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n", + "\n", + "response.choices[0].message.content\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7739cda", + "metadata": {}, + "source": [ + "## And then this great thing happened:\n", + "\n", + "OpenAI's Chat Completions API was so popular, that the other model providers created endpoints that are identical.\n", + "\n", + "They are known as the \"OpenAI Compatible Endpoints\".\n", + "\n", + "For example, google made one here: https://generativelanguage.googleapis.com/v1beta/openai/\n", + "\n", + "And OpenAI decided to be kind: they said, hey, you can just use the same client library that we made for GPT. We'll allow you to specify a different endpoint URL and a different key, to use another provider.\n", + "\n", + "So you can use:\n", + "\n", + "```python\n", + "gemini = OpenAI(base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\", api_key=\"AIz....\")\n", + "gemini.chat.completions.create(...)\n", + "```\n", + "\n", + "And to be clear - even though OpenAI is in the code, we're only using this lightweight python client library to call the endpoint - there's no OpenAI model involved here.\n", + "\n", + "If you're confused, please review Guide 9 in the Guides folder!\n", + "\n", + "And now let's try it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f74293bc", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n", + "\n", + "google_api_key = os.getenv(\"GOOGLE_API_KEY\")\n", + "\n", + "if not google_api_key:\n", + " print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", + "elif not google_api_key.startswith(\"AIz\"):\n", + " print(\"An API key was found, but it doesn't start AIz\")\n", + "else:\n", + " print(\"API key found and looks good so far!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fc5520d", + "metadata": {}, + "outputs": [], + "source": [ + "import google.generativeai as genai\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "load_dotenv()\n", + "genai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\n", + "\n", + "# Lista de modelos disponibles\n", + "for model in genai.list_models():\n", + " print(model.name, \"-\", model.supported_generation_methods)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d060f484", + "metadata": {}, + "outputs": [], + "source": [ + "import google.generativeai as genai\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "load_dotenv()\n", + "genai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\n", + "\n", + "model = genai.GenerativeModel(\"models/gemini-2.5-pro\") # Usa el modelo que viste en la lista, ejemplo \"gemini-1.5-pro\" o \"gemini-1.5-flash\"\n", + "response = model.generate_content(\"Tell me a fun fact\")\n", + "\n", + "print(response.text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)\n", + "\n", + "response = gemini.chat.completions.create(model=\"models/gemini-2.5-pro\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n", + "\n", + "response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5b069be", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "65272432", + "metadata": {}, + "source": [ + "## And Ollama also gives an OpenAI compatible endpoint\n", + "\n", + "...and it's on your local machine!\n", + "\n", + "If the next cell doesn't print \"Ollama is running\" then please open a terminal and run `ollama serve`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f06280ad", + "metadata": {}, + "outputs": [], + "source": [ + "requests.get(\"http://localhost:11434\").content" + ] + }, + { + "cell_type": "markdown", + "id": "c6ef3807", + "metadata": {}, + "source": [ + "### Download llama3.2 from meta\n", + "\n", + "Change this to llama3.2:1b if your computer is smaller.\n", + "\n", + "Don't use llama3.3 or llama4! They are too big for your computer.." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e633481d", + "metadata": {}, + "outputs": [], + "source": [ + "!ollama pull llama3.2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce240975", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "response = requests.get(\"http://localhost:11434/v1/models\")\n", + "print(response.json())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9419762", + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "\n", + "OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n", + "\n", + "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2456cdf", + "metadata": {}, + "outputs": [], + "source": [ + "# Get a fun fact\n", + "\n", + "response = ollama.chat.completions.create(model=\"llama3.2\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n", + "\n", + "response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d7cebd7", + "metadata": {}, + "outputs": [], + "source": [ + "# Now let's try deepseek-r1:1.5b - this is DeepSeek \"distilled\" into Qwen from Alibaba Cloud\n", + "\n", + "!ollama pull deepseek-r1:1.5b" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25002f25", + "metadata": {}, + "outputs": [], + "source": [ + "#response = ollama.chat.completions.create(model=\"deepseek-r1:1.5b\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n", + "#response.choices[0].message.content\n", + "\n", + "from ollama import chat # pip install ollama\n", + "\n", + "resp = chat(\n", + " model='deepseek-r1:1.5b',\n", + " messages=[{'role': 'user', 'content': 'Tell me a fun fact'}],\n", + ")\n", + "\n", + "print(resp['message']['content'])\n", + "# o\n", + "print(resp.message.content)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", + "metadata": {}, + "source": [ + "# HOMEWORK EXERCISE ASSIGNMENT\n", + "\n", + "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", + "\n", + "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", + "\n", + "**Benefits:**\n", + "1. No API charges - open-source\n", + "2. Data doesn't leave your box\n", + "\n", + "**Disadvantages:**\n", + "1. Significantly less power than Frontier Model\n", + "\n", + "## Recap on installation of Ollama\n", + "\n", + "Simply visit [ollama.com](https://ollama.com) and install!\n", + "\n", + "Once complete, the ollama server should already be running locally. \n", + "If you visit: \n", + "[http://localhost:11434/](http://localhost:11434/)\n", + "\n", + "You should see the message `Ollama is running`. \n", + "\n", + "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", + "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", + "Then try [http://localhost:11434/](http://localhost:11434/) again.\n", + "\n", + "If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "from dotenv import load_dotenv\n", + "from scraper import fetch_website_contents\n", + "from IPython.display import Markdown, display\n", + "from ollama import Client \n", + "\n", + "# Cliente Ollama local\n", + "ollama = Client()\n", + "\n", + "system_prompt = \"\"\"\n", + "You are a helpful assistant that analyzes the contents of a website,\n", + "and provides a short, snarky, humorous summary, ignoring text that might be navigation related.\n", + "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n", + "\"\"\"\n", + "\n", + "user_prompt_prefix = \"\"\"\n", + "Here are the contents of a website.\n", + "Provide a short summary of this website.\n", + "If it includes news or announcements, then summarize these too.\n", + "\"\"\"\n", + "\n", + "def messages_for(website):\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n", + " ]\n", + "\n", + "def summarize(url):\n", + " website = fetch_website_contents(url)\n", + " response = ollama.chat(\n", + " model='llama3.2',\n", + " messages=messages_for(website)\n", + " )\n", + " return response['message']['content']\n", + "\n", + "def display_summary(url):\n", + " summary = summarize(url)\n", + " display(Markdown(summary))\n", + "\n", + "# Ejecuta el resumen\n", + "display_summary(\"https://www.reforma.com\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/week1/community-contributions/fernando/week1 EXERCISE.ipynb b/week1/community-contributions/fernando/week1 EXERCISE.ipynb new file mode 100644 index 0000000..c152cb7 --- /dev/null +++ b/week1/community-contributions/fernando/week1 EXERCISE.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", + "metadata": {}, + "source": [ + "# End of week 1 exercise\n", + "\n", + "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", + "and responds with an explanation. This is a tool that you will be able to use yourself during the course!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1070317-3ed9-4659-abe3-828943230e03", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "from openai import OpenAI\n", + "from dotenv import load_dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a456906-915a-4bfd-bb9d-57e505c5093f", + "metadata": {}, + "outputs": [], + "source": [ + "# constants\n", + "MODEL_GPT = 'gpt-4o-mini'\n", + "MODEL_LLAMA = 'llama3.2'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", + "metadata": {}, + "outputs": [], + "source": [ + "# set up environment\n", + "system_prompt = \"\"\"\n", + "You are a technical expert of AI and LLMs.\n", + "\"\"\"\n", + "\n", + "user_prompt_prefix = \"\"\"\n", + "Provide deep explanations of the provided text.\n", + "\"\"\"\n", + "\n", + "user_prompt = \"\"\"\n", + "Explain the provided text.\n", + "\"\"\"\n", + "client = OpenAI()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f0d0137-52b0-47a8-81a8-11a90a010798", + "metadata": {}, + "outputs": [], + "source": [ + "# here is the question; type over this to ask something new\n", + "\n", + "question = \"\"\"\n", + "Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get gpt-4o-mini to answer, with streaming\n", + "def messages_for(question):\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n", + " ]\n", + "\n", + "def run_model_streaming(model_name, question):\n", + " stream = client.chat.completions.create(\n", + " model=model_name,\n", + " messages=messages_for(question),\n", + " stream=True\n", + " )\n", + " for chunk in stream:\n", + " content = chunk.choices[0].delta.content\n", + " if content:\n", + " print(content, end=\"\", flush=True)\n", + "\n", + "run_model_streaming(MODEL_GPT, question)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", + "metadata": {}, + "outputs": [], + "source": [ + "# Get Llama 3.2 to answer\n", + "# imports\n", + "import os\n", + "from openai import OpenAI\n", + "from dotenv import load_dotenv\n", + "\n", + "# set up environment\n", + "client = OpenAI(\n", + " base_url=os.getenv(\"OPENAI_BASE_URL\", \"http://localhost:11434/v1\"),\n", + " api_key=os.getenv(\"OPENAI_API_KEY\", \"ollama\")\n", + ")\n", + "\n", + "system_prompt = \"\"\"\n", + "You are a technical expert of AI and LLMs.\n", + "\"\"\"\n", + "\n", + "user_prompt_prefix = \"\"\"\n", + "Provide deep explanations of the provided text.\n", + "\"\"\"\n", + "\n", + "# question\n", + "question = \"\"\"\n", + "Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n", + "\"\"\"\n", + "\n", + "# message\n", + "def messages_for(question):\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n", + " ]\n", + "\n", + "# response\n", + "def run_model(model_name, question):\n", + " response = client.chat.completions.create(\n", + " model=model_name,\n", + " messages=messages_for(question)\n", + " )\n", + " return response.choices[0].message.content\n", + "\n", + "# run and print result\n", + "print(run_model(MODEL_LLAMA, question))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}