Build AI Voice Assistant for Logistics

Step-by-step guide to building an AI voice assistant for logistics using VideoSDK with complete code examples.

Introduction to AI Voice Agents in Logistics

In the fast-paced logistics industry, efficiency and accuracy are paramount. AI voice agents are revolutionizing how logistics operations are managed, providing real-time assistance and streamlining complex processes. But what exactly is an AI

voice agent

?

What is an AI

Voice Agent

?

An AI

voice agent

 is a software application designed to interact with users through voice commands, processing spoken language into actionable responses. These agents utilize technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Large Language Models (LLM) to understand and respond to user queries.

Why are they important for the Logistics Industry?

In logistics, voice agents can assist with shipment tracking, inventory management, and logistics optimization. They enable hands-free operations, allowing logistics professionals to focus on critical tasks without manual data entry.

Core Components of a

Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Large Language Model): Processes text to generate intelligent responses.
  • TTS (Text-to-Speech): Converts text back into speech for user interaction.

What You’ll Build in This Tutorial

In this tutorial, you will build a fully functional AI voice assistant tailored for the logistics industry using the VideoSDK framework. You’ll learn to set up the development environment, create a custom agent, and test it in a real-world scenario.

Architecture and Core Concepts

Understanding the architecture of your AI

voice agent

 is crucial for effective implementation. Here’s a high-level overview of how the system processes user input to generate responses.

High-Level Architecture Overview

The AI

voice agent

 listens to user speech, processes it through a series of steps, and responds intelligently. The process involves:
  1. Voice

    Activity Detection

     (VAD):     Determines when the user is speaking.
  2. Speech-to-Text (STT): Converts speech into text.
  3. Large Language Model (LLM): Analyzes text and generates a response.
  4. Text-to-Speech (TTS): Converts the response back into speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Before diving into coding, ensure your development environment is ready.

Prerequisites

  • Python 3.11+
  • VideoSDK Account (sign up at app.videosdk.live)

Step 1: Create a Virtual Environment

To maintain a clean workspace, create 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 Python 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

Let’s dive into building your AI voice agent. Below is the complete code block for the agent:
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 logistics assistant AI Voice Agent designed to support the logistics industry. Your primary role is to assist logistics professionals by providing accurate information and guidance on logistics operations, supply chain management, and transportation planning. You can answer questions related to shipment tracking, inventory management, and logistics optimization strategies. However, you are not a certified logistics consultant, and users should verify critical decisions with a qualified professional. You must include a disclaimer advising users to consult with logistics experts for complex issues. Your responses should be concise, informative, and tailored to the logistics industry, ensuring clarity and relevance in all interactions."
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 = [AI voice Agent Sessions](https://docs.videosdk.live/ai_agents/core-components/agent-session)(
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 your agent can interact in a meeting, you need a meeting ID. Use the following curl command to generate one:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the base Agent class, providing custom behavior for entering and exiting a session:
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 CascadingPipeline is central to your agent’s functionality, integrating 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

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session, while make_context sets up the room options:
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
10async def start_session(context: JobContext):
11    # Create agent and conversation flow
12    agent = MyVoiceAgent()
13    conversation_flow = ConversationFlow(agent)
14
15    # Create pipeline
16    pipeline = CascadingPipeline(
17        stt=DeepgramSTT(model="nova-2", language="en"),
18        llm=OpenAILLM(model="gpt-4o"),
19        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
20        vad=SileroVAD(threshold=0.35),
21        turn_detector=TurnDetector(threshold=0.8)
22    )
23
24    session = AgentSession(
25        agent=agent,
26        pipeline=pipeline,
27        conversation_flow=conversation_flow
28    )
29
30    try:
31        await context.connect()
32        await session.start()
33        # Keep the session running until manually terminated
34        await asyncio.Event().wait()
35    finally:
36        # Clean up resources when done
37        await session.close()
38        await context.shutdown()
39
40if __name__ == "__main__":
41    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
42    job.start()
43

Running and Testing the Agent

Step 5.1: Running the Python Script

To start your AI voice agent, run the Python script:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, find the playground link in the console output. Use this link to join the meeting and interact with your agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can enhance your agent by integrating custom tools using the function_tool feature, allowing for specialized tasks and operations.

Exploring Other Plugins

Consider experimenting with other plugins for STT, LLM, and TTS to customize your agent’s capabilities further.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly set in the .env file and that your VideoSDK account is active.

Audio Input/Output Problems

Check your microphone and speaker settings, and ensure they are correctly configured in your system preferences.

Dependency and Version Conflicts

Verify that all dependencies are installed and compatible with Python 3.11+.

Conclusion

Summary of What You’ve Built

You’ve successfully built an AI voice assistant tailored for the logistics industry, capable of assisting with various logistics tasks.

Next Steps and Further Learning

Explore additional features of the VideoSDK framework and consider integrating more advanced AI models to enhance your agent’s capabilities.

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