Build an AI Voice Agent for Live Commerce

Create an AI Voice Agent for live commerce with our detailed guide. Includes code examples and testing instructions.

Introduction to AI Voice Agents in Live Commerce

In today's fast-paced digital world, AI Voice Agents are revolutionizing the way businesses interact with customers. These agents are particularly transformative in the live commerce industry, where real-time interactions can significantly enhance customer experiences and drive sales.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a sophisticated software entity capable of understanding and responding to human speech. It leverages technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Language Models (LLM) to process and generate human-like responses. These agents can handle various tasks, from answering queries to providing personalized recommendations.

Why are they important for the live commerce industry?

In live commerce, where customer engagement is crucial, AI Voice Agents play a pivotal role. They can assist customers in real-time, answer product-related questions, and even guide them through the checkout process. This not only enhances the shopping experience but also increases conversion rates and customer satisfaction.

Core Components of a

Voice Agent

  • Speech-to-Text (STT): Converts spoken language into text.
  • Language Model (LLM): Processes text input to understand context and generate responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.
For a comprehensive understanding, you can refer to the

AI voice Agent core components overview

.

What You'll Build in This Tutorial

In this tutorial, you will build a fully functional AI

Voice Agent

 tailored for live commerce using the VideoSDK framework. You'll learn how to set up the development environment, create a custom agent, and test it in a real-world scenario.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI

Voice Agent

 involves a seamless flow of data, starting from capturing user speech to generating a spoken response. Here's a simplified view:
  1. User Speech: Captured by the agent.
  2. Speech-to-Text (STT): Transcribes the speech.
  3. Language Model (LLM): Analyzes and processes the text.
  4. Text-to-Speech (TTS): Converts the response text back to speech.
  5. Agent Response: Delivered to the user.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class that defines the behavior and capabilities of your bot.
  • CascadingPipeline: Manages the flow of audio processing, integrating STT, LLM, and TTS components. Learn more about the

    Cascading pipeline in AI voice Agents

    .
  • VAD & TurnDetector: Ensure the agent listens and responds at appropriate times by detecting voice activity and conversation turns. Explore the

    Turn detector for AI voice Agents

    .

Setting Up the Development Environment

Prerequisites

Before you start, ensure you have Python 3.11+ installed and a VideoSDK account, which you can create at app.videosdk.live.

Step 1: Create a Virtual Environment

Open your terminal and run the following commands to create and activate a virtual environment:
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
2pip install python-dotenv
3

Step 3: Configure API Keys in a .env File

Create a .env file in your project directory and add your VideoSDK API key:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Here is the complete code for your AI Voice Agent. We'll break it down step-by-step in the following sections.
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
10pre_download_model()
11
12agent_instructions = "You are an AI Voice Agent specialized in live commerce. Your persona is that of a knowledgeable and engaging shopping assistant. Your primary capabilities include assisting customers with product inquiries, providing real-time inventory updates, and facilitating seamless checkout processes. You can also offer personalized product recommendations based on customer preferences and browsing history. However, you must adhere to certain constraints: you cannot process payments directly, and you must always ensure customer data privacy by not storing any personal information. Additionally, you should remind users that product availability and prices are subject to change and encourage them to verify details on the official website. Your goal is to enhance the live shopping experience by providing accurate and timely information, while maintaining a friendly and professional demeanor."
13
14class MyVoiceAgent(Agent):
15    def __init__(self):
16        super().__init__(instructions=agent_instructions)
17    async def on_enter(self): await self.session.say("Hello! How can I help?")
18    async def on_exit(self): await self.session.say("Goodbye!")
19
20async def start_session(context: JobContext):
21    agent = MyVoiceAgent()
22    conversation_flow = ConversationFlow(agent)
23
24    pipeline = CascadingPipeline(
25        stt=DeepgramSTT(model="nova-2", language="en"),
26        llm=OpenAILLM(model="gpt-4o"),
27        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
28        vad=SileroVAD(threshold=0.35),
29        turn_detector=TurnDetector(threshold=0.8)
30    )
31
32    session = AgentSession(
33        agent=agent,
34        pipeline=pipeline,
35        conversation_flow=conversation_flow
36    )
37
38    try:
39        await context.connect()
40        await session.start()
41        await asyncio.Event().wait()
42    finally:
43        await session.close()
44        await context.shutdown()
45
46def make_context() -> JobContext:
47    room_options = RoomOptions(
48        name="VideoSDK Cascaded Agent",
49        playground=True
50    )
51
52    return JobContext(room_options=room_options)
53
54if __name__ == "__main__":
55    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
56    job.start()
57

Step 4.1: Generating a VideoSDK Meeting ID

To create a meeting ID, use the following curl command:
1curl -X POST \\
2  https://api.videosdk.live/v1/meetings \\
3  -H "Authorization: Bearer YOUR_API_TOKEN" \\
4  -H "Content-Type: application/json"
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class, defining the agent's behavior. It uses the agent_instructions to guide interactions. The on_enter and on_exit methods manage greetings and farewells.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline integrates various plugins:
  • DeepgramSTT: Transcribes speech to text.
  • OpenAILLM: Processes text to generate responses. You can explore more about the

    OpenAI LLM Plugin for voice agent

    .
  • ElevenLabsTTS: Converts responses to speech.
  • SileroVAD & TurnDetector: Manage voice activity and conversation turns.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent and pipeline, starting the session. The make_context function configures the room options for the agent's environment. Finally, the if __name__ == "__main__": block runs the agent.

Running and Testing the Agent

Step 5.1: Running the Python Script

Execute the script with:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

After running the script, find the

AI Agent playground

 link in the console. Use it to join the session and interact with your AI Voice Agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

Enhance your agent by adding custom tools to handle specific tasks beyond the default capabilities.

Exploring Other Plugins

Consider experimenting with different STT, LLM, and TTS plugins to optimize performance and cost.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correct and has the necessary permissions.

Audio Input/Output Problems

Check your microphone and speaker settings and ensure they are properly configured.

Dependency and Version Conflicts

Verify that all dependencies are installed and compatible with your Python version.

Conclusion

Summary of What You've Built

You've created a robust AI Voice Agent for live commerce, capable of interacting with users in real-time.

Next Steps and Further Learning

Explore more advanced features and consider integrating additional plugins to further enhance your agent's capabilities.

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