From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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232 lines
7.6 KiB
232 lines
7.6 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 solution\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!\n", |
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"\n", |
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"After week 2 you'll be able to add a User Interface to this tool, giving you a valuable application." |
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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": "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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"from dotenv import load_dotenv\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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"# import ollama" |
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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": 3, |
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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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"source": [ |
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"# set up environment\n", |
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"\n", |
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"load_dotenv()\n", |
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"openai = OpenAI()\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": 5, |
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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": 6, |
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"id": "8595807b-8ae2-4e1b-95d9-e8532142e8bb", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# prompts\n", |
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"\n", |
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"system_prompt = \"You are a helpful technical tutor who answers questions about python code, software engineering, data science and LLMs\"\n", |
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"user_prompt = \"Please give a detailed explanation to the following question: \" + 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": 7, |
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"id": "9605cbb6-3d3f-4969-b420-7f4cae0b9328", |
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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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"\n", |
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"messages = [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": user_prompt}\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": 8, |
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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! Let's break down the code snippet you've provided to understand what it does and why it operates that way.\n", |
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"\n", |
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"The code snippet is:\n", |
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"\n", |
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"python\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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"### Explanation of Components\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\")}`\n", |
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" - This is a set comprehension, which is a concise way to create a set in Python.\n", |
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" - **Iterates**: It iterates over a collection named `books`.\n", |
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" - **Extraction**: `book.get(\"author\")` attempts to retrieve the value associated with the key `\"author\"` from each `book` dictionary.\n", |
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" - **Filtering**: The `if book.get(\"author\")` condition filters out any books that do not have an `\"author\"` key or where the value is `None` or an empty string. This means only those books that have a valid author will be included in the set.\n", |
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"\n", |
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"2. **Set**:\n", |
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" - The output of the set comprehension is a set of unique author names from the `books` collection. \n", |
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" - Sets automatically handle duplicates, so if multiple books have the same author, their name will only appear once in the resulting set.\n", |
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"\n", |
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"3. **Yielding with `yield from`**:\n", |
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" - `yield from` is a syntax used in Python to delegate part of a generator’s operations to another generator.\n", |
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" - In this context, it means that each element obtained from the set (the unique authors) will be yielded one by one.\n", |
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"\n", |
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"### Overall Functionality\n", |
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"\n", |
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"- This code essentially extracts all unique authors from the list of `books` (where each `book` is presumably a dictionary containing various attributes) and yields each author one at a time from a generator function.\n", |
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"\n", |
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"### Practical Implications\n", |
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"\n", |
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"- When this line of code is executed within a generator function, it allows the generator to yield each unique author efficiently, enabling the caller to iterate over them.\n", |
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"- Utilizing a set ensures that authors are only returned once, even if they appear multiple times across different books.\n", |
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"\n", |
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"### Summary\n", |
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"\n", |
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"To summarize, the code snippet:\n", |
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"\n", |
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"1. Extracts authors from a list of book dictionaries.\n", |
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"2. Filters out any entries without an author.\n", |
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"3. Collects unique authors in a set.\n", |
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"4. Uses `yield from` to yield each author one at a time.\n", |
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"\n", |
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"This approach is efficient and concise, leveraging the power of Python's set comprehension and generator functions to handle potentially large data sets with an emphasis on uniqueness and iteration simplicity." |
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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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"\n", |
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"stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,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)" |
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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\n", |
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"\n", |
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"response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n", |
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"reply = response['message']['content']\n", |
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"display(Markdown(reply))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "7e14bcdb-b928-4b14-961e-9f7d8c7335bf", |
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"metadata": {}, |
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"source": [ |
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"# Congratulations!\n", |
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"\n", |
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"You could make it better by taking in the question using \n", |
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"`my_question = input(\"Please enter your question:\")`\n", |
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"\n", |
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"And then creating the prompts and making the calls interactively." |
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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": "da663d73-dd2a-4fff-84df-2209cf2b330b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "llms", |
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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": "3.11.10" |
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} |
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}, |
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"nbformat": 4, |
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}
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