116 lines
4.1 KiB
Plaintext
116 lines
4.1 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": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
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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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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"\n",
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"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
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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": "7b87cadb-d513-4303-baee-a37b6f938e4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\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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"# 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!\")\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": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()\n",
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"\n",
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"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
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"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions"
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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": "00743dac-0e70-45b7-879a-d7293a6f68a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Step 1: Create your prompts\n",
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"\n",
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"system_prompt = \"Eres un analista acostumbrado a trabajar con correos electrónicos que contiene un gran conocimiento sobre la mejor manera de resumir contenido releveante \\\n",
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"dejando de lado cualquier información que no despierte interés o no sea el tema principal del correo. Tu función será leer contenido de correos y definir un listado de las 3 mejores opciones con el formato: Opción *numero de la opción*: *sujeto* Motivo: *que palabras clave dentro del texto has utilizado para llegar a esa conclusion y la relación semántica con tu idea\"\n",
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"user_prompt = \"\"\"\n",
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"Tengo un correo que le quiero enviar a mi profesor pero no se muy bien como llamarlo, ayudame. El correo es el siguiente:\n",
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"Hola profe,\n",
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"Ultimamente estoy disfrutando mucho sus clases y la información que presenta me parece muy importante. Este fin de semana me voy de vacaciones y no podré\n",
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"ir a sus clases la semana que viene. Me gustaría si pudiera pasarme los pdfs de la siguiente semana para echarle un vistazo por mi cuenta durante mi ausencia en Francia.\n",
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"\n",
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"Un saludo,\n",
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"Daniel.\n",
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"\"\"\"\n",
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"\n",
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"# Step 2: Make the messages list\n",
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"\n",
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"messages = [{\"role\" : \"system\" , \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}]\n",
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"\n",
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"# Step 3: Call OpenAI\n",
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"\n",
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"response = openai.chat.completions.create( \n",
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" model = \"gpt-4o-mini\",\n",
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" messages = messages)\n",
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"\n",
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"# Step 4: print the result\n",
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"\n",
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"print(response.choices[0].message.content)"
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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": "3.11.13"
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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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