149 lines
4.2 KiB
Plaintext
149 lines
4.2 KiB
Plaintext
{
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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": "f38e9ebb-453d-4b40-84f6-bc3e9bf4d7ef",
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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 os\n",
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"import requests\n",
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"import json\n",
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"import ollama\n",
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"from typing import List\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from openai import OpenAI\n",
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"\n",
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"# constants\n",
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"\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\n",
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
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"HEADERS = {\"Content-Type\": \"application/json\"}"
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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": "f367c5bb-80a2-4d78-8f27-823f5dafe7c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set up environment\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"openai = OpenAI()\n",
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"\n",
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"# System prompt for the AI TECHNICAL LLM AND PYTHON TUTOR.\"\n",
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"\n",
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"system_prompt = \"You are an EXPERT in AI, LLMS and Python \\\n",
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"Provide the answer with example ALLWAYS when necessary. \\\n",
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"If you do not know the answer just say 'I don't know the answer' \\\n",
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"Respond in markdown in Spanish.\"\n",
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"\n",
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"# messages\n",
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"def messages_for(question):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": question}\n",
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" ]\n",
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"\n",
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"# here is the question; type over this to ask something new\n",
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"\n",
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"question = \"\"\"\n",
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"Please explain what this code does and why:\n",
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"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
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"\"\"\"\n",
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"question = question[:5_000] # Truncate if more than 5,000 characters"
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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": "a90d726d-d494-401f-9cd6-0260f5c781e0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# METHODS TO DISPLAY\n",
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"def display_summary_ollama(question):\n",
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" response = ollama.chat(\n",
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" model = MODEL_LLAMA,\n",
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" messages = messages_for(question)\n",
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" ) \n",
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" summary = response['message']['content']\n",
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" display(Markdown(summary))\n",
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"\n",
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"def display_summary_gpt(question):\n",
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" stream = openai.chat.completions.create(\n",
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" model = MODEL_GPT,\n",
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" messages = messages_for(question),\n",
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" stream=True\n",
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" )\n",
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" response = \"\"\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" for chunk in stream:\n",
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" response += chunk.choices[0].delta.content or ''\n",
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" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
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" update_display(Markdown(response), display_id=display_handle.display_id)\n",
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" \n",
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"def display_summary(llm, question):\n",
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" if llm.startswith(\"llama3.2\"):\n",
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" display_summary_ollama(question)\n",
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" else:\n",
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" display_summary_gpt(question)"
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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": "4e993b6d-8fee-43f3-9e36-f86701a5cc57",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get gpt-4o-mini to answer, with streaming\n",
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"\n",
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"display_summary(MODEL_GPT, question)"
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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": "31f6283a-ee57-415e-9a57-83d07261b7f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get Llama 3.2 to answer\n",
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"\n",
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"display_summary(MODEL_LLAMA, question)"
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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 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.11.11"
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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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