You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 

353 lines
12 KiB

{
"cells": [
{
"cell_type": "markdown",
"id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
"metadata": {},
"source": [
"# Day 3 - Conversational AI - aka Chatbot!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "231605aa-fccb-447e-89cf-8b187444536a",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv(override=True)\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
"metadata": {},
"outputs": [],
"source": [
"# Initialize\n",
"\n",
"openai = OpenAI()\n",
"MODEL = 'gpt-4o-mini'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant\""
]
},
{
"cell_type": "markdown",
"id": "98e97227-f162-4d1a-a0b2-345ff248cbe7",
"metadata": {},
"source": [
"# Please read this! A change from the video:\n",
"\n",
"In the video, I explain how we now need to write a function called:\n",
"\n",
"`chat(message, history)`\n",
"\n",
"Which expects to receive `history` in a particular format, which we need to map to the OpenAI format before we call OpenAI:\n",
"\n",
"```\n",
"[\n",
" {\"role\": \"system\", \"content\": \"system message here\"},\n",
" {\"role\": \"user\", \"content\": \"first user prompt here\"},\n",
" {\"role\": \"assistant\", \"content\": \"the assistant's response\"},\n",
" {\"role\": \"user\", \"content\": \"the new user prompt\"},\n",
"]\n",
"```\n",
"\n",
"But Gradio has been upgraded! Now it will pass in `history` in the exact OpenAI format, perfect for us to send straight to OpenAI.\n",
"\n",
"So our work just got easier!\n",
"\n",
"We will write a function `chat(message, history)` where: \n",
"**message** is the prompt to use \n",
"**history** is the past conversation, in OpenAI format \n",
"\n",
"We will combine the system message, history and latest message, then call OpenAI."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
"metadata": {},
"outputs": [],
"source": [
"# Simpler than in my video - we can easily create this function that calls OpenAI\n",
"# It's now just 1 line of code to prepare the input to OpenAI!\n",
"\n",
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" print(\"History is:\")\n",
" print(history)\n",
" print(\"And messages is:\")\n",
" print(messages)\n",
"\n",
" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" yield response"
]
},
{
"cell_type": "markdown",
"id": "1334422a-808f-4147-9c4c-57d63d9780d0",
"metadata": {},
"source": [
"## And then enter Gradio's magic!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0866ca56-100a-44ab-8bd0-1568feaf6bf2",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n",
"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
"For example, if the customer says 'I'm looking to buy a hat', \\\n",
"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
"Encourage the customer to buy hats if they are unsure what to get.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd361bec-e5ea-46c7-a359-b6a0e3f1858f",
"metadata": {},
"outputs": [],
"source": [
"history = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab86c705-6911-4044-9cb5-41447c455e7a",
"metadata": {},
"outputs": [],
"source": [
"finetune_history = [\n",
" {'role': 'user', 'content': 'Hello'},\n",
" {'role': 'assistant', 'content': 'Hello! How can I assist you today? Are you looking for something specific, or perhaps just browsing?'}, \n",
" {'role': 'user', 'content': 'I want to buy outdoor sandals'}, \n",
" {'role': 'assistant', 'content': \"Great choice! We have some lovely outdoor sandals in stock. While you're looking at those, you might also want to check out our hats. They're currently 60% off, which is a fantastic deal! A nice hat could be the perfect complement to your new sandals. Would you like to see some hats as well?\"}, \n",
" {'role': 'user', 'content': 'No just shoes'}, \n",
" {'role': 'assistant', 'content': 'No problem at all! Let me show you our selection of outdoor sandals. They’re stylish and perfect for any outdoor activities. What size are you looking for in sandals?'},\n",
" {'role': 'user', 'content': '38'}, \n",
" {'role': 'assistant', 'content': 'Perfect! Let me grab some outdoor sandals in size 38 for you. Meanwhile, if you have any specific colors or styles in mind, just let me know!'}, \n",
" {'role': 'user', 'content': 'i like them colourful'},\n",
" {'role': 'assistant', 'content': 'Colorful outdoor sandals are a great choice for adding a fun touch to your outfit. I\\'ll see if we have some vibrant options in size 38.'}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "169edca1-b165-4bc1-b981-e27ccda2bc31",
"metadata": {},
"outputs": [],
"source": [
"system_message = f\"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n",
"the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
"For example, if the customer says 'I'm looking to buy a hat', \\\n",
"you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
"Encourage the customer to buy hats if they are unsure what to get. \\\n",
"Don't be pushy about the sale items. If the customer refuses to see sales once, accept it and go ahead with customer wishes. \\\n",
"An ideal conversation could look like this: \\n{finetune_history}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e5be3ec-c26c-42bc-ac16-c39d369883f6",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d75f0ffa-55c8-4152-b451-945021676837",
"metadata": {},
"outputs": [],
"source": [
"system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n",
"but remind the customer to look at hats!\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c602a8dd-2df7-4eb7-b539-4e01865a6351",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a987a66-1061-46d6-a83a-a30859dc88bf",
"metadata": {},
"outputs": [],
"source": [
"# Fixed a bug in this function brilliantly identified by student Gabor M.!\n",
"# I've also improved the structure of this function\n",
"\n",
"def chat(message, history):\n",
"\n",
" relevant_system_message = system_message\n",
" if 'belt' in message:\n",
" relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n",
" \n",
" messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
"\n",
" response = \"\"\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" yield response"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20570de2-eaad-42cc-a92c-c779d71b48b6",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
},
{
"cell_type": "markdown",
"id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business Applications</h2>\n",
" <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n",
"<br/><br/>\n",
"Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dfb9e21-df67-4c2b-b952-5e7e7961b03d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}