From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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199 lines
6.9 KiB
199 lines
6.9 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", |
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"metadata": {}, |
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"source": [ |
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"# End of week 1 exercise\n", |
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"\n", |
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"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n", |
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"and responds with an explanation. This is a tool that you will be able to use yourself during the course!" |
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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": 1, |
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"id": "c1070317-3ed9-4659-abe3-828943230e03", |
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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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"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" |
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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": 2, |
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"id": "4a456906-915a-4bfd-bb9d-57e505c5093f", |
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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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"MODEL_GPT = 'gpt-4o-mini'\n", |
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"MODEL_LLAMA = '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": 4, |
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"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"API key found and looks good so far!\n" |
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] |
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} |
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], |
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"source": [ |
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"# set up environment\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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"\n", |
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"# Check the key\n", |
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"\n", |
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"if not api_key:\n", |
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n", |
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"elif not api_key.startswith(\"sk-proj-\"):\n", |
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" 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", |
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"elif api_key.strip() != api_key:\n", |
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n", |
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"else:\n", |
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" print(\"API key found and looks good so far!\")" |
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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": "3f0d0137-52b0-47a8-81a8-11a90a010798", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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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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"\"\"\"" |
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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": 5, |
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"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/markdown": [ |
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"Certainly! This line of code is using a combination of Python set comprehension and the `yield from` statement. Let's break it down step-by-step:\n", |
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"\n", |
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"1. **Set Comprehension**: \n", |
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" - `{book.get(\"author\") for book in books if book.get(\"author\")}` is a set comprehension. It constructs a set containing the authors from the `books` collection.\n", |
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" - `books` is assumed to be an iterable, such as a list or tuple, of dictionaries, where each dictionary represents a book with various attributes, including an \"author\".\n", |
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" - `book.get(\"author\")` attempts to retrieve the value associated with the \"author\" key from each book dictionary.\n", |
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" - The `if book.get(\"author\")` part ensures that only books with a non-None value for the \"author\" key are included in the set. This helps in filtering out entries where the \"author\" key doesn't exist or is set to `None`.\n", |
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"\n", |
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"2. **Yield From**: \n", |
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" - `yield from` is a statement in Python that delegates part of a generator's operations to another iterable.\n", |
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" - `yield from` in this context is used to yield each element from the set constructed by the set comprehension one at a time.\n", |
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"\n", |
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"3. **Purpose**:\n", |
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" - The combination of set comprehension and `yield from` results in yielding each distinct author found in the list of books, but importantly, without duplicates, since sets naturally eliminate duplicates.\n", |
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" - This might be part of a generator function, which is a function allowing iteration over the unique authors.\n", |
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"\n", |
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"In summary, this code is used to retrieve and yield each unique author's name from a list of book dictionaries, one by one, eliminating any duplicates. The use of `yield from` makes it efficient for building generators that process data as an iterable sequence." |
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], |
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"text/plain": [ |
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"<IPython.core.display.Markdown object>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"# Get gpt-4o-mini to answer, with streaming\n", |
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"from IPython.display import display, Markdown\n", |
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"import openai\n", |
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"\n", |
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"# Your model\n", |
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"MODEL = \"gpt-4o\" \n", |
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"# System prompt (sets the assistant's role)\n", |
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"system_prompt = \"You are a helpful Python expert who explains code clearly and concisely.\"\n", |
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"\n", |
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"# The user question\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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"\n", |
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"# Streaming function for explanation\n", |
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"def stream_code_explanation(code_question):\n", |
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" stream = openai.chat.completions.create(\n", |
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" model=MODEL,\n", |
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" messages=[\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": code_question}\n", |
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" ],\n", |
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" stream=True\n", |
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" )\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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" \n", |
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" for chunk in stream:\n", |
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" content = chunk.choices[0].delta.content or \"\"\n", |
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" response += content\n", |
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" response_cleaned = response.replace(\"```\", \"\").replace(\"markdown\", \"\")\n", |
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" display_handle.update(Markdown(response_cleaned))\n", |
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"\n", |
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"# Run it!\n", |
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"stream_code_explanation(question)\n" |
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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": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", |
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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" |
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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": "venv", |
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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": "3.10.0rc2" |
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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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