From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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161 lines
4.6 KiB
161 lines
4.6 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 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": null, |
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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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"import os\n", |
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"import ollama\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" |
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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": "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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"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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"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", |
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" print(\"API key looks good so far\")\n", |
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"else:\n", |
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" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\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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"\n", |
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"openai = 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": null, |
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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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"system_prompt = \"You are an assistant that analyzes a technical question \\\n", |
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"and responds with a short, clear, structured explanation. Response in markdown\"\n", |
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"\n", |
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"def get_user_prompt(question):\n", |
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" user_prompt = f\"You are looking at a technical questions as following: \\n\"\n", |
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" user_prompt += question\n", |
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" user_prompt += f\"\\nPlease response answer with a logical explanation\"\n", |
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" return user_prompt" |
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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": null, |
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"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", |
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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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"def stream_QA(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 = [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": get_user_prompt(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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" 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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"stream_QA(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": "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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"def ollama_QA(question):\n", |
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" response = ollama.chat(model=MODEL_LLAMA, messages = [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": get_user_prompt(question)}\n", |
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" ])\n", |
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" return Markdown(response['message']['content'])\n", |
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"\n", |
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"ollama_QA(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": "6b484100-e5cf-40db-827c-d5618b154654", |
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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": "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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