{ "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()" ] }, { "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": "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": [ "\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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