{ "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", "\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\")" ] }, { "cell_type": "code", "execution_count": null, "id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb", "metadata": {}, "outputs": [], "source": [ "# Initialize\n", "\n", "openai = OpenAI()\n", "MODEL = 'gpt-4.1-mini'" ] }, { "cell_type": "code", "execution_count": null, "id": "e16839b5-c03b-4d9d-add6-87a0f6f37575", "metadata": {}, "outputs": [], "source": [ "# Again, I'll be in scientist-mode and change this global during the lab\n", "\n", "system_message = \"You are a helpful assistant\"" ] }, { "cell_type": "markdown", "id": "98e97227-f162-4d1a-a0b2-345ff248cbe7", "metadata": {}, "source": [ "## And now, writing a new callback\n", "\n", "We now need to write a function called:\n", "\n", "`chat(message, history)`\n", "\n", "Which will be a callback function we will give gradio.\n", "\n", "### The job of this function\n", "\n", "Take a message, take the prior conversation, and return the response.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "354ce793", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " return \"bananas\"" ] }, { "cell_type": "code", "execution_count": null, "id": "e87f3417", "metadata": {}, "outputs": [], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "5d4996e8", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " return f\"You said {message} and the history is {history} but I still say bananas\"" ] }, { "cell_type": "code", "execution_count": null, "id": "434a0417", "metadata": {}, "outputs": [], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "7890cac3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "f7330d7f", "metadata": {}, "source": [ "## OK! Let's write a slightly better chat callback!" ] }, { "cell_type": "code", "execution_count": null, "id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1", "metadata": {}, "outputs": [], "source": [ "\n", "def chat(message, history):\n", " history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n", " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", " response = openai.chat.completions.create(model=MODEL, messages=messages)\n", " return response.choices[0].message.content\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0ab706f9", "metadata": {}, "outputs": [], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch()" ] }, { "cell_type": "code", "execution_count": null, "id": "3bce145a", "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n", " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", " stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\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": "b8beeca6", "metadata": {}, "outputs": [], "source": [ "gr.ChatInterface(fn=chat, type=\"messages\").launch()" ] }, { "cell_type": "markdown", "id": "1334422a-808f-4147-9c4c-57d63d9780d0", "metadata": {}, "source": [ "## OK let's keep going!\n", "\n", "Using a system message to add context, and to give an example answer.. this is \"one shot prompting\" again" ] }, { "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": "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": [ "\n", "def chat(message, history):\n", " history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n", " relevant_system_message = system_message\n", " if 'belt' in message.lower():\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": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Business Applications

\n", " 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", "

\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.\n", "
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