170 lines
4.5 KiB
Plaintext
170 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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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": "1",
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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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"from IPython.display import Markdown, display, update_display\n",
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"from dotenv import load_dotenv\n",
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"import os\n",
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"import openai\n",
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"from openai import 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": null,
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"id": "2",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# constants\n",
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"models = {\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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"\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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"# To use ollama using openai API (ensure that ollama is running on localhost)\n",
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"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
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"\n",
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"def model_choices(model):\n",
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" if model in models:\n",
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" return models[model]\n",
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" else:\n",
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" raise ValueError(f\"Model {model} not found in models dictionary\")\n",
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"\n",
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"def get_model_api(model='MODEL_GPT'):\n",
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" if model == 'MODEL_GPT':\n",
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" return openai, model_choices(model)\n",
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" elif model == 'MODEL_LLAMA':\n",
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" return ollama_via_openai, model_choices(model)\n",
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" else:\n",
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" raise ValueError(f\"Model {model} not found in models dictionary\")\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": "3",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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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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"system_prompt = \"\"\" You are an AI assistant helping a user find information about a product. \n",
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"The user asks you a technical question about code, and you provide a response with code snippets and explanations.\"\"\"\n",
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"\n",
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"def stream_brochure(question, model):\n",
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" api, model_name = get_model_api(model)\n",
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" stream = api.chat.completions.create(\n",
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" model=model_name,\n",
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" messages=[\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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" 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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]
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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": "4",
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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": "5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the model of your choice (choices appeared below) to answer, with streaming \n",
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"\n",
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"\"\"\"models = {\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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"\n",
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"stream_brochure(question,'MODEL_GPT')"
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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": "6",
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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": "3.11.13"
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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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