Build an AI Voice Assistant for Supply Chain

Step-by-step guide to building an AI voice assistant for the supply chain industry using VideoSDK.

Introduction to AI Voice Agents in the Supply Chain Industry

In today's fast-paced world, the supply chain industry is constantly seeking innovative ways to enhance efficiency and responsiveness. One such innovation is the integration of AI Voice Agents. But what exactly is an AI

Voice Agent

? At its core, an AI

Voice Agent

 is a software application capable of interpreting and responding to spoken language. It acts as an intermediary between human users and complex data systems, providing real-time assistance and insights.

Why are they important for the supply chain industry?

AI Voice Agents are particularly valuable in the supply chain sector due to their ability to streamline operations, improve communication, and enhance decision-making. They can assist in tracking shipments, managing inventory, and providing updates on logistics. By offering hands-free interaction, they enable supply chain professionals to access critical information quickly and efficiently.

Core Components of a

Voice Agent

To build a functional AI

Voice Agent

, several core components are essential:
  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Model (LLM): Processes the text to generate meaningful 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'll learn how to build an AI Voice Assistant tailored for the supply chain industry using the VideoSDK framework. We'll guide you through the process of setting up the environment, building the agent, and testing it in a real-world scenario.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI

Voice Agent

 involves several stages, from capturing user speech to generating a spoken response. Here's a high-level overview of the process:
  1. User Input: The user speaks into the system.
  2. Speech-to-Text (STT): The spoken words are converted into text.
  3. Language Processing: The text is processed by a language model to understand the intent and generate a response.
  4. Text-to-Speech (TTS): The response is converted back into speech.
  5. User Output: The system speaks the response back to the user.
Diagram

Understanding Key Concepts in the VideoSDK Framework

The VideoSDK framework provides several key components to facilitate the development of AI Voice Agents:
  • Agent: Represents the core bot logic, handling interactions with users.
  • CascadingPipeline: Manages the flow of audio processing, integrating STT, LLM, and TTS.
  • VAD & TurnDetector: These components help the agent determine when to listen and when to respond, ensuring smooth interaction. For more details, check out the

    Turn detector for AI voice Agents

    .

Setting Up the Development Environment

To get started with building your AI

Voice Agent

, you'll need to set up your development environment. Here's how:

Prerequisites

  • Python 3.11+: Ensure you have Python installed on your system.
  • VideoSDK Account: Sign up at app.videosdk.live to access the necessary APIs.

Step 1: Create a Virtual Environment

First, create a virtual environment to manage your project dependencies:
1python -m venv voice-agent-env
2source voice-agent-env/bin/activate  # On Windows use `voice-agent-env\\Scripts\\activate`
3

Step 2: Install Required Packages

Next, install the required 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 key:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Now that your environment is set up, let's dive into building the AI Voice Agent. Below is the complete code that we'll break down and explain step-by-step:
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 AI Voice Assistant specialized in the supply chain industry. Your primary role is to assist users by providing insights and information related to supply chain management, logistics, and operations. You can answer questions about supply chain processes, offer guidance on optimizing logistics, and provide updates on industry trends. However, you are not a certified supply chain professional, and users should consult with a qualified expert for critical business decisions. Always remind users to verify information with industry standards and regulations. Your responses should be concise, informative, and relevant to the supply chain context."
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

Before you can run your agent, you'll need a meeting ID. You can generate one using the VideoSDK API. Here's an example using curl:
1curl -X POST \
2  https://api.videosdk.live/v1/meetings \
3  -H "Authorization: Bearer your_api_key_here"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where we define the behavior of our AI Voice Agent. This class extends the Agent class from the VideoSDK framework. Here's a breakdown:
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
  • __init__ Method: Initializes the agent with specific instructions tailored for the supply chain industry.
  • on_enter Method: Defines the welcome message when a session starts.
  • on_exit Method: Defines the goodbye message when a session ends.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is crucial as it defines how audio is processed. Here's how it's set up:
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
  • STT: Uses Deepgram for speech-to-text conversion.
  • LLM: Employs OpenAI's GPT-4 for language processing.
  • TTS: Utilizes ElevenLabs for text-to-speech conversion.
  • VAD: Uses Silero for voice

    activity detection

    .
  • TurnDetector: Determines when the agent should listen or speak.

Step 4.4: Managing the Session and Startup Logic

Finally, we manage the session and startup logic with the following functions:
1def make_context() -> JobContext:
2    room_options = RoomOptions(
3    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
4        name="VideoSDK Cascaded Agent",
5        playground=True
6    )
7
8    return JobContext(room_options=room_options)
9
10if __name__ == "__main__":
11    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
12    job.start()
13
  • make_context Function: Sets up the room options for the agent.
  • Main Block: Initiates the agent session.

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your AI Voice Agent, execute the following command:
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 your browser to interact with your agent. Speak into the microphone and watch your agent respond in real-time.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's functionality using custom tools. This enables you to integrate additional features tailored to your specific needs.

Exploring Other Plugins

While we've used specific plugins for STT, LLM, and TTS, the VideoSDK framework supports various other options. Explore different plugins to find the best fit for your application.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly configured in the .env file. Double-check for typos and verify your account status on the VideoSDK dashboard.

Audio Input/Output Problems

Check your microphone and speaker settings. Ensure permissions are granted for audio input/output in your browser.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions. Use a virtual environment to manage packages effectively.

Conclusion

Summary of What You've Built

Congratulations! You've built a fully functional AI Voice Assistant for the supply chain industry. You've learned how to set up the environment, build the agent, and test it in a real-world scenario.

Next Steps and Further Learning

Consider exploring advanced features and customizations to enhance your agent's capabilities. Dive deeper into the VideoSDK documentation to discover more possibilities, including managing

AI voice Agent Sessions

.

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