Build an AI Voice Agent for Retail

Step-by-step guide to building an AI Voice Agent for retail using VideoSDK, complete with code and testing instructions.

Introduction to AI Voice Agents in the Retail Industry

In the rapidly evolving retail landscape, AI Voice Agents are becoming indispensable tools. These agents enhance customer experiences by providing instant responses, personalized recommendations, and seamless shopping assistance. But what exactly is an AI

Voice Agent

?

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a software application that uses artificial intelligence to interact with users through voice commands. It processes spoken language, understands the intent, and provides appropriate responses. These agents leverage technologies such as Speech-to-Text (STT), Text-to-Speech (TTS), and Language Models (LLM) to facilitate natural conversations.

Why are they important for the retail industry?

In retail, AI Voice Agents can transform customer service by offering 24/7 assistance, reducing wait times, and providing tailored shopping experiences. They can assist with product inquiries, check stock availability, and even guide customers through the purchasing process.

Core Components of a

Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Language Model): Understands and processes the text to generate responses.
  • TTS (Text-to-Speech): Converts the generated text back into speech.

What You'll Build in This Tutorial

In this guide, you'll learn how to build a fully functional AI Voice Agent tailored for the retail industry using the VideoSDK framework. We'll cover everything from setting up your development environment to deploying and testing your agent.

Architecture and Core Concepts

Before diving into code, let's explore the architecture and core concepts that underpin our AI Voice Agent.

High-Level Architecture Overview

The AI Voice Agent architecture involves a seamless flow of data from user speech to agent response. Here's a simplified view:
  • User Speech: Captured and processed by the

    Silero Voice Activity Detection

     (VAD).
  • STT Conversion: Converts speech to text.
  • LLM Processing: Analyzes the text to understand intent and generate a response.
  • TTS Conversion: Converts the response back to speech.
  • Agent Response: Delivered to the user.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: Represents your bot, handling interactions and responses.
  • CascadingPipeline: Manages the flow of data through STT, LLM, and TTS.
  • VAD & TurnDetector: Ensure the agent listens and responds at the right times.

Setting Up the Development Environment

To build your AI Voice Agent, you'll need to set up a development environment. Here's how:

Prerequisites

  • Python 3.11+: Ensure you have Python 3.11 or later installed.
  • VideoSDK Account: Sign up at app.videosdk.live to access API keys.

Step 1: Create a Virtual Environment

Create an isolated environment for your project:
1python -m venv retail-voice-agent
2source retail-voice-agent/bin/activate  # On Windows use `retail-voice-agent\\Scripts\\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk-agents videosdk-plugins
2

Step 3: Configure API Keys in a .env file

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

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

Let's build the AI Voice Agent step-by-step. We'll start by presenting the complete, runnable code, then break it down for detailed explanations.
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 knowledgeable retail assistant AI Voice Agent designed to enhance customer experience in the retail industry. Your primary role is to assist customers by providing information about products, checking stock availability, and guiding them through the purchasing process. You can also offer personalized recommendations based on customer preferences and past purchases. However, you must clearly state that you are not a human and that your suggestions are based on available data and algorithms. You cannot process payments or handle sensitive customer information. Always encourage customers to reach out to human staff for complex inquiries or issues beyond your capabilities."
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 interact with your agent, you'll need a meeting ID. Use the following curl command to generate one:
1curl -X POST "https://api.videosdk.live/v1/rooms" \
2-H "Authorization: Bearer your_api_key_here" \
3-H "Content-Type: application/json"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where you define the agent's behavior. It inherits from the Agent class and customizes the on_enter and on_exit methods to greet and bid farewell to users.
1class MyVoiceAgent(Agent):
2    def __init__(self):
3        super().__init__(instructions=agent_instructions)
4    async def on_enter(self): await self.session.say("Hello! How can I help?")
5    async def on_exit(self): await self.session.say("Goodbye!")
6

Step 4.3: Defining the Core Pipeline

The

Cascading Pipeline in AI voice Agents

 integrates various plugins to handle speech processing. Each plugin has a specific role:
  • STT (DeepgramSTT): Converts speech to text.
  • LLM (OpenAILLM): Processes the text to generate responses.
  • TTS (ElevenLabsTTS): Converts the response text back to speech.
  • VAD (SileroVAD): Detects when the user is speaking.
  • TurnDetector: Determines when to switch between listening and speaking.
1pipeline = CascadingPipeline(
2    stt=DeepgramSTT(model="nova-2", language="en"),
3    llm=OpenAILLM(model="gpt-4o"),
4    tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
5    vad=SileroVAD(threshold=0.35),
6    turn_detector=TurnDetector(threshold=0.8)
7)
8

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the

AI voice Agent Sessions

 and manages the lifecycle of the conversation. The make_context function sets up the room options, and the if __name__ == "__main__": block starts the job.
1async def start_session(context: JobContext):
2    # Create agent and conversation flow
3    agent = MyVoiceAgent()
4    conversation_flow = ConversationFlow(agent)
5
6    # Create pipeline
7    pipeline = CascadingPipeline(
8        stt=DeepgramSTT(model="nova-2", language="en"),
9        llm=OpenAILLM(model="gpt-4o"),
10        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
11        vad=SileroVAD(threshold=0.35),
12        turn_detector=TurnDetector(threshold=0.8)
13    )
14
15    session = AgentSession(
16        agent=agent,
17        pipeline=pipeline,
18        conversation_flow=conversation_flow
19    )
20
21    try:
22        await context.connect()
23        await session.start()
24        # Keep the session running until manually terminated
25        await asyncio.Event().wait()
26    finally:
27        # Clean up resources when done
28        await session.close()
29        await context.shutdown()
30
31def make_context() -> JobContext:
32    room_options = RoomOptions(
33    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
34        name="VideoSDK Cascaded Agent",
35        playground=True
36    )
37
38    return JobContext(room_options=room_options)
39
40if __name__ == "__main__":
41    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
42    job.start()
43

Running and Testing the Agent

After building your agent, it's time to run and test it.

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, you'll receive a playground link in the console. Open this link in a browser to interact with your AI Voice Agent. Speak to the agent and observe how it responds based on the pipeline configuration.

Advanced Features and Customizations

Enhance your AI Voice Agent with advanced features and customizations.

Extending Functionality with Custom Tools

You can extend the agent's capabilities by integrating custom tools. This involves creating additional functions that the agent can call to perform specific tasks.

Exploring Other Plugins

The VideoSDK framework supports various plugins. Consider exploring other STT, LLM, and TTS options to optimize your agent's performance.

Troubleshooting Common Issues

Here are some common issues you might encounter and how to resolve them.

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file. Double-check for typos or missing keys.

Audio Input/Output Problems

Verify your microphone and speaker settings. Ensure they are correctly configured and accessible by the agent.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions. Use a virtual environment to avoid conflicts with other projects.

Conclusion

Congratulations! You've built a fully functional AI Voice Agent for the retail industry. This agent can assist customers with product inquiries, stock checks, and more. As next steps, consider exploring additional features and integrating more complex functionalities to further enhance your agent's capabilities. For a comprehensive understanding of the

AI voice Agent core components overview

 and

AI voice Agent deployment

, refer to the VideoSDK documentation.

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