Build an AI Voice Agent for Healthcare

Create a healthcare AI voice agent with our detailed guide. Learn to build, test, and deploy using VideoSDK.

Introduction to AI Voice Agents in Healthcare

AI Voice Agents are sophisticated software systems designed to interact with users through voice commands. These agents utilize technologies such as Speech-to-Text (STT), Text-to-Speech (TTS), and Large Language Models (LLM) to understand and respond to user queries. In the healthcare industry, AI Voice Agents can assist in patient interactions, provide general health advice, and help schedule appointments, thus enhancing the efficiency and accessibility of healthcare services.
In this tutorial, you will learn how to build a healthcare-focused AI Voice Agent using the VideoSDK framework. This agent will be capable of understanding user queries related to common health issues and providing general advice, while ensuring compliance with privacy regulations.

Architecture and Core Concepts

High-Level Architecture Overview

The AI Voice Agent architecture involves a seamless flow of data from user speech to agent response. The process begins with capturing the user's voice input, converting it into text using

Deepgram STT Plugin for voice agent

, processing the text with an

OpenAI LLM Plugin for voice agent

to generate a response, and finally converting the response back to speech using

ElevenLabs TTS Plugin for voice agent

. This entire flow is managed by the VideoSDK framework, which integrates various plugins to handle each step efficiently.
1sequenceDiagram
2    participant User
3    participant Agent
4    participant STT
5    participant LLM
6    participant TTS
7    User->>STT: Speak
8    STT->>LLM: Convert to Text
9    LLM->>TTS: Generate Response
10    TTS->>User: Speak Response
11

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

Before starting, ensure you have Python 3.11+ installed and a VideoSDK account at app.videosdk.live.

Step 1: Create a Virtual Environment

Create a virtual environment to manage your project dependencies:
1python -m venv venv
2source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk
2

Step 3: Configure API Keys in a .env file

Create a .env file to store your API keys securely:
1VIDEOSDK_API_KEY=your_api_key_here
2

Building the AI Voice Agent: A Step-by-Step Guide

Below is the complete, runnable code for the AI Voice Agent. We will break it down to understand each component.
1import asyncio, os
2from videosdk.agents import Agent, AgentSession, CascadingPipeline, JobContext, RoomOptions, WorkerJob, ConversationFlow
3from videosdk.plugins.silero import SileroVAD
4from videosdk.plugins.turn_detector import TurnDetector, pre_download_model
5from videosdk.plugins.deepgram import DeepgramSTT
6from videosdk.plugins.openai import OpenAILLM
7from videosdk.plugins.elevenlabs import ElevenLabsTTS
8from typing import AsyncIterator
9
10# Pre-downloading the Turn Detector model
11pre_download_model()
12
13agent_instructions = "You are a helpful healthcare assistant AI Voice Agent designed to assist users in the healthcare industry. Your primary capabilities include answering questions about common symptoms, providing general healthcare advice, and assisting with scheduling appointments with healthcare providers. You must always include a disclaimer that you are not a medical professional and that users should consult a doctor for medical advice. You are designed to operate within the constraints of privacy and data protection regulations, ensuring that all user interactions are confidential and secure. You should not provide any diagnosis or treatment plans, and you must always encourage users to seek professional medical advice for any health concerns."
14
15class MyVoiceAgent(Agent):
16    def __init__(self):
17        super().__init__(instructions=agent_instructions)
18    async def on_enter(self): await self.session.say("Hello! How can I help?")
19    async def on_exit(self): await self.session.say("Goodbye!")
20
21async def start_session(context: JobContext):
22    # Create agent and conversation flow
23    agent = MyVoiceAgent()
24    conversation_flow = ConversationFlow(agent)
25
26    # Create pipeline
27    pipeline = CascadingPipeline(
28        stt=DeepgramSTT(model="nova-2", language="en"),
29        llm=OpenAILLM(model="gpt-4o"),
30        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
31        vad=SileroVAD(threshold=0.35),
32        turn_detector=TurnDetector(threshold=0.8)
33    )
34
35    session = AgentSession(
36        agent=agent,
37        pipeline=pipeline,
38        conversation_flow=conversation_flow
39    )
40
41    try:
42        await context.connect()
43        await session.start()
44        # Keep the session running until manually terminated
45        await asyncio.Event().wait()
46    finally:
47        # Clean up resources when done
48        await session.close()
49        await context.shutdown()
50
51def make_context() -> JobContext:
52    room_options = RoomOptions(
53    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
54        name="VideoSDK Cascaded Agent",
55        playground=True
56    )
57
58    return JobContext(room_options=room_options)
59
60if __name__ == "__main__":
61    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
62    job.start()
63

Step 4.1: Generating a VideoSDK Meeting ID

To generate a meeting ID, use the following curl command:
1curl -X POST https://api.videosdk.live/v1/meetings \
2-H "Authorization: Bearer YOUR_API_KEY"
3

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class defines the behavior of your AI Voice Agent. It extends the Agent class, using the provided agent_instructions to guide interactions. The on_enter and on_exit methods handle greetings and farewells.

Step 4.3: Defining the Core Pipeline

The [CascadingPipeline](https://docs.videosdk.live/ai_agents/core-components/cascading-pipeline) orchestrates the flow of audio data through various processing stages:
  • STT (DeepgramSTT): Converts speech to text using the Nova-2 model.
  • LLM (OpenAILLM): Processes the text and generates a response using GPT-4o.
  • TTS (ElevenLabsTTS): Converts the text response back to speech.
  • VAD (SileroVAD): Detects voice activity to manage when the agent listens.
  • TurnDetector: Determines when the user has finished speaking.

Step 4.4: Managing the Session and Startup Logic

The [AI voice Agent Sessions](https://docs.videosdk.live/ai_agents/core-components/agent-session) function initializes the agent session, managing the connection and lifecycle of the conversation. The make_context function sets up the meeting context, using RoomOptions to configure the session. The if __name__ == "__main__": block starts the agent by creating a WorkerJob that runs the session.

Running and Testing the Agent

Step 5.1: Running the Python Script

Execute the script using Python:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, a link to the VideoSDK playground will be displayed. Use this link to interact with your AI Voice Agent and test its capabilities.

Advanced Features and Customizations

Extending Functionality with Custom Tools

Enhance your agent's capabilities by integrating custom tools using the function_tool feature, allowing for specialized processing or data handling.

Exploring Other Plugins

Consider experimenting with different STT, LLM, and TTS plugins to optimize performance and functionality for your specific use case.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file and that your account has the necessary permissions.

Audio Input/Output Problems

Verify that your microphone and speaker settings are correctly configured and that the required permissions are granted.

Dependency and Version Conflicts

Check for any version conflicts between installed packages and resolve them by updating or downgrading as necessary.

Conclusion

Summary of What You've Built

You have successfully built an AI Voice Agent tailored for the healthcare industry using the VideoSDK framework. This agent can interact with users, provide general health advice, and assist with appointment scheduling.

Next Steps and Further Learning

Explore further customization options, integrate additional plugins, and consider deploying your agent in a real-world healthcare setting to enhance patient engagement and support. For a comprehensive guide, refer to the

Voice Agent Quick Start Guide

.

Start Building With Free $20 Balance

No credit card required to start.

Want to level-up your learning? Subscribe now

Subscribe to our newsletter for more tech based insights

FAQ