OpenAI Codex is a powerful AI system that can generate code and natural language for various tasks, such as creating websites, apps, games, and chatbots. In this tutorial, we will show you how to build a simple chatbot with OpenAI Codex and Python, using the OpenAI API and the Streamlit library.<\/p>\n
To follow this tutorial, you will need:<\/p>\n
openai<\/code>, streamlit<\/code>, requests<\/code>. You can use pip<\/code> or conda<\/code> to install them.<\/li>\n- A code editor or IDE of your choice.<\/li>\n<\/ul>\n
Step 1: Define the Chatbot Task<\/h2>\n
The first step is to define the chatbot task that we want OpenAI Codex to perform. This involves writing a prompt that describes the chatbot’s purpose, personality, and behavior. The prompt should also include some examples of how the chatbot should respond to different user inputs.<\/p>\n
For this tutorial, we will create a chatbot that acts as a personal assistant for booking appointments. The chatbot should be friendly, helpful, and professional. It should ask the user for their name, phone number, email, and preferred date and time for the appointment. It should also confirm the booking details and thank the user at the end.<\/p>\n
Here is an example of a prompt that defines this chatbot task:<\/p>\n
You are a personal assistant chatbot that can book appointments for users. You are friendly, helpful, and professional. You ask the user for their name, phone number, email, and preferred date and time for the appointment. You also confirm the booking details and thank the user at the end.\n\nExample:\n\nUser: Hi\nBot: Hello, welcome to the appointment booking service. I am your personal assistant chatbot. What is your name?\nUser: John Smith\nBot: Nice to meet you, John Smith. What is your phone number?\nUser: 123-456-7890\nBot: Thank you. What is your email address?\nUser: john.smith@example.com\nBot: Got it. When would you like to book your appointment?\nUser: Next Monday at 10 am\nBot: Okay, I have booked your appointment for next Monday at 10 am. Please confirm if this is correct.\nUser: Yes, that's correct\nBot: Great. Your appointment has been confirmed. Thank you for using our service. Have a nice day.\n<\/code><\/pre>\nSome additional sentences are:<\/p>\n
Source: Conversation with Bing, 6\/15\/2023
\n(1) Build your own AI assistant in 10 lines of code – Python. https:\/\/community.openai.com\/t\/build-your-own-ai-assistant-in-10-lines-of-code-python\/83210.
\n(2) Building a Chatbot with OpenAI’s GPT-3 engine, Twilio SMS and Python. https:\/\/www.twilio.com\/blog\/openai-gpt-3-chatbot-python-twilio-sms.
\n(3) Create Your First Chatbot Using GPT 3.5, OpenAI, Python and Panel.. https:\/\/towardsai.net\/p\/machine-learning\/create-your-first-chatbot-using-gpt-3-5-openai-python-and-panel.
\n(4) Building AI-Powered Chatbots with OpenAI API: A Step-by-Step Guide. https:\/\/medium.com\/muthoni-wanyoike\/building-ai-powered-chatbots-with-openai-api-a-step-by-step-guide-5f4888b1d65e.
\n(5) Build Your Own Chat Bot in Python with OpenAI\u2019s GPT-3 – Coder’s Den. https:\/\/codersden.hashnode.dev\/build-your-own-chat-bot-in-python-with-openais-gpt-3.
\n(6) Anyone Can Create A Chatbot With Codex – Medium. https:\/\/cobusgreyling.medium.com\/anyone-can-create-a-chatbot-with-codex-5a9e53ffb35e.<\/p>\n
Step 2: Generate Code with OpenAI Codex<\/h2>\n
OpenAI Codex is a system that can generate code and natural language for various tasks, given a natural language prompt. To use OpenAI Codex, we need to access the OpenAI API and use the Completion.create<\/code> method. This method takes several arguments, such as:<\/p>\n\n- The
engine<\/code> argument, which specifies the name of the OpenAI Codex model to use. In this case, we use davinci-codex<\/code>, which is the most powerful and versatile model available.<\/li>\n- The
prompt<\/code> argument, which specifies the natural language prompt that defines the task. In this case, we use the prompt that we wrote in the previous step.<\/li>\n- The
max_tokens<\/code> argument, which specifies how many tokens (words or characters) to generate. In this case, we use 200, which is enough to generate a simple chatbot script.<\/li>\n- The
temperature<\/code> argument, which specifies how creative or diverse you want the output to be. In this case, we use 0.5, which is a moderate value that balances between randomness and consistency.<\/li>\n- The
stop<\/code> argument, which specifies when to stop generating text. In this case, we use ###<\/code>, which is a special token that we add at the end of our prompt to indicate the end of the code.<\/li>\n<\/ul>\nTo generate code with OpenAI Codex, we can use the following Python code:<\/p>\n
# Import openai library\nimport openai\n\n# Set your OpenAI API key\nopenai.api_key = \"<your_api_key>\"\n\n# Choose your OpenAI Codex model\nengine = \"davinci-codex\"\n\n# Write your prompt\nprompt = \"\"\"\nYou are a personal assistant chatbot that can book appointments for users. You are friendly, helpful, and professional. You ask the user for their name, phone number, email, and preferred date and time for the appointment. You also confirm the booking details and thank the user at the end.\n<\/code><\/pre>\nExample:<\/p>\n
User: Hi\nBot: Hello, welcome to the appointment booking service. I am your personal assistant chatbot. What is your name?\nUser: John Smith\nBot: Nice to meet you, John Smith. What is your phone number?\nUser: 123-456-7890\nBot: Thank you. What is your email address?\nUser: john.smith@example.com\nBot: Got it. When would you like to book your appointment?\nUser: Next Monday at 10 am\nBot: Okay, I have booked your appointment for next Monday at 10 am. Please confirm if this is correct.\nUser: Yes, that's correct\nBot: Great. Your appointment has been confirmed. Thank you for using our service. Have a nice day.\n<\/code><\/pre>\n# Write your code below\n\n# Import requests library\nimport requests\n\n# Define a function to send requests to the OpenAI API\ndef openai_request(prompt):\n # Set the URL and headers for the request\n url = \"https:\/\/api.openai.com\/v1\/engines\/davinci-codex\/completions\"\n headers = {\n \"Authorization\": f\"Bearer {openai.api_key}\",\n \"Content-Type\": \"application\/json\"\n }\n # Set the data for the request\n data = {\n \"prompt\": prompt,\n \"max_tokens\": 50,\n \"temperature\": 0.5,\n \"stop\": \"n\"\n }\n # Send a POST request and get the response\n response = requests.post(url, headers=headers, json=data)\n # Parse the response and return the generated text\n result = response.json()\n text = result[\"choices\"][0][\"text\"]\n return text\n\n# Define a function to handle user input and generate bot output\ndef chatbot():\n # Print a welcome message\n print(\"Hello, welcome to the appointment booking service. I am your personal assistant chatbot.\")\n # Start a loop to get user input and generate bot output\n while True:\n # Get user input\n user_input = input(\"User: \")\n # Check if user wants to quit\n if user_input.lower() == \"quit\":\n break\n # Generate bot output using OpenAI Codex\n bot_output = openai_request(user_input)\n # Print bot output\n print(f\"Bot: {bot_output}\")\n<\/code><\/pre>\nThis code does the following:<\/p>\n
\n- Imports the
openai<\/code> and requests<\/code> libraries and sets your OpenAI API key.<\/li>\n- Defines a function called
openai_request<\/code> that takes a prompt as an argument and sends a request to the OpenAI API using the Completion.create<\/code> method. The function returns the generated text from the response.<\/li>\n- Defines a function called
chatbot<\/code> that handles user input and generates bot output using the openai_request<\/code> function. The function prints a welcome message and starts a loop to get user input and generate bot output. The loop breaks if the user types “quit”.<\/li>\n<\/ul>\nStep 3: Create a Web Interface with Streamlit<\/h2>\n
Streamlit is a library that allows you to create interactive web apps with Python. It is very easy to use and requires minimal code. To use Streamlit, we need to install it using pip<\/code> or conda<\/code> and run it using the streamlit run<\/code> command.<\/p>\nTo create a web interface for the chatbot, we can use the following Python code:<\/p>\n
# Import streamlit library\nimport streamlit as st\n\n# Import openai_request function from previous step\nfrom openai_request import openai_request\n\n# Create a title for the web app\nst.title(\"Appointment Booking Chatbot\")\n\n# Create a sidebar for user input\nuser_input = st.sidebar.text_input(\"Type your message here\")\n\n# Create a button to send user input\nsend_button = st.sidebar.button(\"Send\")\n\n# Create a container for chat history\nchat_history = st.empty()\n\n# Initialize an empty list to store chat messages\nmessages = []\n\n# Define a function to format chat messages\ndef format_message(role, text):\n # Use different colors for user and bot messages\n if role == \"User\":\n color = \"#008080\"\n elif role == \"Bot\":\n color = \"#800080\"\n # Return the formatted message as HTML\n return f\"<div style='color:{color}; font-size: 18px; padding: 10px; border-radius: 10px;'>{role}: {text}<\/div>\"\n\n# Check if send button is clicked\nif send_button:\n # Append user input to messages list\n messages.append(format_message(\"User\", user_input))\n # Generate bot output using openai_request function\n bot_output = openai_request(user_input)\n # Append bot output to messages list\n messages.append(format_message(\"Bot\", bot_output))\n # Update chat history with messages list\n chat_history.markdown(\"\".join(messages), unsafe_allow_html=True)\n<\/code><\/pre>\nThis code does the following:<\/p>\n
\n- Imports the
streamlit<\/code> library and the openai_request<\/code> function from the previous step.<\/li>\n- Creates a title for the web app using the
st.title<\/code> function.<\/li>\n- Creates a sidebar for user input using the
st.sidebar.text_input<\/code> function.<\/li>\n- Creates a button to send user input using the
st.sidebar.button<\/code> function.<\/li>\n- Creates a container for chat history using the
st.empty<\/code> function.<\/li>\n- Initializes an empty list to store chat messages.<\/li>\n
- Defines a function called
format_message<\/code> that takes a role and a text as arguments and returns a formatted message as HTML. The function uses different colors for user and bot messages.<\/li>\n- Checks if the send button is clicked using an
if<\/code> statement.<\/li>\n- Appends user input to the messages list using the
format_message<\/code> function.<\/li>\n- Generates bot output using the
openai_request<\/code> function.<\/li>\n- Appends bot output to the messages list using the
format_message<\/code> function.<\/li>\n- Updates chat history with the messages list using the
chat_history.markdown<\/code> function. The function takes an argument called unsafe_allow_html<\/code> to render HTML code.<\/li>\n<\/ul>\nStep 4: Test and Deploy the Chatbot<\/h2>\n
To test the chatbot, we can run the code using the streamlit run<\/code> command in the terminal. This will launch a local web server and open a browser window with the web app. We can then type our messages in the sidebar and click the send button to see the chatbot’s responses.<\/p>\nTo deploy the chatbot, we can use Streamlit Cloud, which is a service that allows you to host and share your Streamlit apps online. To use Streamlit Cloud, we need to:<\/p>\n