Build a conversational AI assistant by integrating OpenAI’s ChatGPT with Plivo’s programmable Voice API

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Get started with Plivo

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Connect your number to a Application

<Response>
	<GetInput inputType="speech" action="
		{your_webhook_url}">
		<Speak>Welcome to Gleaneagles Hospital. I'm Ivy, and I'm here to assist you in scheduling an appointment. Please describe your symptoms, or if you have a specific doctor in mind, please provide their name.</Speak>
	</GetInput>
</Response>

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Integrate Voice API with ChatGPT

1. Initiate a call to the designated number linked to an application that accepts voice calls (similar to the above example).
2. Configure the “speak” element with an initial greeting (text-to-speech).
3. When a customer speaks, speech from the caller is transcribed using Plivo’s GetInput element (speech-to-text).
4. The transcribed text is then sent to your WebHook URL.
5. You then need to send this transcribed text to ChatGPT for further processing.
6. OpenAI’s Chat Completion API, is designed to simplify multi-turn conversations. It takes a list of messages as input and generates a response, which is then sent back to your WebHook server.
7. Upon receiving the response from ChatGPT, the speak element (text-to-speech) converts it into speech.

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Development guide

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Generate a response

1. Import the necessary libraries and install a ChatGPT client using your OpenAI API key.
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   from openai import OpenAI
   client = OpenAI(
   api_key='<OPEN_AI_API_KEY>',
   )
   

2. Create the context for ChatGPT
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   messages = []
   
   system_msg = """Your task is to serve as an interactive and supportive assistant, aiding in scheduling and issuing appointments for Gleaneagles Hospital. The user can either directly tell the doctor's name or tell his symptoms based on which you can suggest a doctor. If user provides symptoms, query him more so that you can correctly identify the appropriate doctor. You wait to collect the entire appointment details, then summarize it and check for a final time if the patient wants to add anything else. Makesure to clarify all options, departments, doctors, timings to uniquely identify the department from the departments. You respond in a short, very conversational friendly style. 
   If user asks any other question, you will let him know politely that you can't help him with this and tell him to ask relevant question.
   The departments includes:
   Cardiologist
   Neurologist
   Gynaecologist
   Urologist
   Physiotherapist
   Dermatologist
   General Physician
   
   Available list of Doctors:
   Dr Leena S Sridhar, Gynaecologist
   Dr Barnali Ghosh, Gynaecologist
   Dr Anand R Shenoy, Cardiologist
   Dr Deepak Padmanabhan, Cardiologist
   Dr Akash J, Neurologist
   Dr Lakshmi Krishna V, Neurologist
   Dr Manohar Bhadrappa, Urologist
   Dr Harsha R, Urologist
   Dr Akshi Bansal, Dermatologist
   Dr Sachith Abraham, Dermatologist
   Dr Ria Emmanuel, General Physician
   Dr Satish Babu K, General Physician
   Abraham S S, Physiotherapist
   Ashwin Bhat H, Physiotherapist
   
   The timings includes: 9:00, 10:00, 11:00, 12:00, 15:00, 16:00, 17:00
   The Doctors will be available from Monday to Saturday. 
   
   If it is an emergency, the patient can come anytime in the Emergency Unit and can meet the available doctor.
   """
   
   messages.append({"role": "system", "content": system_msg})
   

3. Define Python functions to get messages and assistance based on the above messages.
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   def generate_response(input_message):
       messages.append({"role": "user", "content": input_message})
       response = client.chat.completions.create(
           model="gpt-3.5-turbo",
           messages=messages)
       reply = response.choices[0].message.content
       messages.append({"role": "assistant", "content": reply})
       return reply
   

4. Develop a local application capable of receiving callbacks from Plivo. Establish a WebHook URL linked to this local application. While we utilized localtunnel, any cross-platform application enabling the exposure of local web servers to the internet can be employed.
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   @app.route('/webhook', methods=['POST'])
   def webhook():
       input_message = request.form.get('Speech')
       response = generate_response(input_message)
       xml_response = create_xml(response)
       return Response(xml_response, mimetype='text/xml')
       
   def create_xml(text):
       element = plivoxml.ResponseElement()
       response = element.add(
           plivoxml.GetInputElement().
           set_action('<action url>').
           set_input_type('speech').
           set_language('en-US').
           set_execution_timeout(10).
           set_speech_end_timeout(10).
           add_speak(content=text, language='en-US')
       ).to_string(True)
       print(response)
       return response
   

   This application retrieves transcribed text from callbacks and sends it to ‘generate_response’ to obtain a response from ChatGPT. Additionally, it includes another method called ‘create_xml,’ utilizing the Plivo Python SDK to generate GetInput response XML for further user responses.