Conversational AI for Retail: Build a Voice Agent

Step-by-step guide to building a conversational AI voice agent for retail using VideoSDK.

Introduction to AI Voice Agents in Conversational AI for Retail

In today's fast-paced retail environment, providing exceptional customer service is crucial. AI Voice Agents are revolutionizing the way businesses interact with customers by offering personalized, efficient, and round-the-clock assistance. But what exactly is an AI

Voice Agent

?

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a sophisticated software application designed to interact with users through voice commands. These agents leverage cutting-edge technologies such as Speech-to-Text (STT), Large Language Models (LLM), and Text-to-Speech (TTS) to understand and respond to user queries in a human-like manner.

Why are they important for the Conversational AI for Retail industry?

In the retail sector, AI Voice Agents can enhance customer experience by providing instant support for product inquiries, personalized recommendations, and store information. They can handle multiple customer interactions simultaneously, ensuring no query goes unanswered.

Core Components of a

Voice Agent

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

AI voice Agent core components overview

.

What You'll Build in This Tutorial

In this tutorial, you'll learn how to build a conversational AI

voice agent

 tailored for the retail industry using the VideoSDK framework. We'll guide you through setting up the environment, developing the agent, and testing it in a real-world scenario.

Architecture and Core Concepts

Understanding the architecture and core concepts is essential before diving into the implementation.

High-Level Architecture Overview

The AI Voice Agent processes user input through a series of stages: capturing audio, converting it to text, processing the text with an LLM, and generating a voice response. Here's a visual representation:
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Before we start building, let's set up the necessary tools and environment.

Prerequisites

  • Python 3.11+
  • VideoSDK Account: Sign up at app.videosdk.live

Step 1: Create a Virtual Environment

Creating a virtual environment helps manage dependencies and avoid conflicts.
1python3.11 -m venv retail_ai_env
2source retail_ai_env/bin/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 credentials.
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Let's dive into building our AI Voice Agent. Here's the complete code block for reference:
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 friendly and knowledgeable retail assistant AI designed to enhance the shopping experience for customers. Your primary role is to assist customers by providing information about products, helping them find items, and offering personalized recommendations based on their preferences. You can also assist with checking product availability, store locations, and operating hours. However, you are not authorized to process payments or handle sensitive customer information such as credit card details. Always remind customers to visit the store or the official website for final purchases and to verify any critical information. Your goal is to make shopping more convenient and enjoyable for customers while maintaining a professional and courteous demeanor."
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
Now, let's break down the code into smaller sections for a clearer understanding.

Step 4.1: Generating a VideoSDK Meeting ID

Before starting the agent, generate a meeting ID using the VideoSDK API. Use the following curl command:
1curl -X POST \
2  https://api.videosdk.live/v1/meetings \
3  -H "Authorization: Bearer YOUR_API_KEY" \
4  -H "Content-Type: application/json"
5
This command returns a meeting ID, which you can use to join a session.

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where we define the agent's behavior. It inherits from the Agent class and uses the agent_instructions to guide its interactions.
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
This class handles the initial greeting and farewell messages.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is crucial for processing audio input and generating responses. It integrates various plugins for STT, LLM, TTS, VAD, and turn detection.
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
Each component plays a specific role in the agent's operation.

Step 4.4: Managing the Session and Startup Logic

The start_session function manages the agent's lifecycle, including starting and stopping the session.
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4    pipeline = CascadingPipeline(
5        stt=DeepgramSTT(model="nova-2", language="en"),
6        llm=OpenAILLM(model="gpt-4o"),
7        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
8        vad=SileroVAD(threshold=0.35),
9        turn_detector=TurnDetector(threshold=0.8)
10    )
11    session = AgentSession(
12        agent=agent,
13        pipeline=pipeline,
14        conversation_flow=conversation_flow
15    )
16    try:
17        await context.connect()
18        await session.start()
19        await asyncio.Event().wait()
20    finally:
21        await session.close()
22        await context.shutdown()
23
The make_context function sets up the environment for the agent to operate in.
1def make_context() -> JobContext:
2    room_options = RoomOptions(
3        name="VideoSDK Cascaded Agent",
4        playground=True
5    )
6    return JobContext(room_options=room_options)
7
Finally, the main block initializes and starts the agent.
1if __name__ == "__main__":
2    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
3    job.start()
4

Running and Testing the Agent

Now that the agent is built, let's run and test it.

Step 5.1: Running the Python Script

Start the agent by running the script:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the agent is running, you'll receive a playground link in the console. Open this link in your browser to interact with the agent. Speak naturally and see how the agent responds.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend the agent's functionality using function_tool. This enables custom logic and integrations.

Exploring Other Plugins

While this tutorial uses specific plugins, VideoSDK supports various STT, LLM, and TTS plugins. Experiment with different options to find the best fit for your needs.

Troubleshooting Common Issues

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 properly connected and configured.

Dependency and Version Conflicts

Use a virtual environment to manage dependencies. Check for version conflicts and resolve them by updating or downgrading packages.

Conclusion

Summary of What You've Built

Congratulations! You've built a fully functional AI Voice Agent for the retail industry. This agent can assist customers with product inquiries and recommendations.

Next Steps and Further Learning

Explore additional features and plugins in the VideoSDK framework. Consider building more complex agents for other industries or integrating additional data sources for richer interactions. For more on managing sessions, refer to

AI voice Agent Sessions

.

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