Building AI Voice Agents with Fallback Responses

Step-by-step guide to building AI voice agents with fallback responses using VideoSDK.

Introduction to AI Voice Agents in Fallback Responses for Voice Agents

In the rapidly evolving world of artificial intelligence, AI voice agents have become indispensable tools in various industries. These agents are designed to interact with users through voice commands, providing seamless and efficient communication. In this tutorial, we will focus on building an AI

voice agent

 capable of providing fallback responses, ensuring smooth user interactions even when the agent cannot directly fulfill a request.

What is an AI

Voice Agent

?

An AI

voice agent

 is a software application that uses artificial intelligence to process and respond to voice commands. These agents leverage technologies such as speech-to-text (STT), text-to-speech (TTS), and large language models (LLM) to understand and generate human-like responses.

Why are They Important for Fallback Responses?

Fallback responses are crucial for maintaining user satisfaction. When an AI

voice agent

 cannot process a user's request, providing a helpful fallback response can guide the user towards a successful interaction, reducing frustration and enhancing the overall experience.

Core Components of a

Voice Agent

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

What You'll Build in This Tutorial

In this tutorial, you will learn how to build an AI

voice agent

 using the VideoSDK framework. This agent will specialize in providing fallback responses, ensuring that user interactions are smooth and effective.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI voice agent involves several key components working together to process user input and generate responses. The flow begins with capturing user speech, converting it to text, processing the text with an LLM, and finally converting the response back to speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

To get started, ensure you have Python 3.11+ installed and a VideoSDK account. You can sign up at app.videosdk.live.

Step 1: Create a Virtual Environment

1python -m venv myenv
2source myenv/bin/activate  # On Windows use `myenv\\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

To build our AI voice agent, we'll start by presenting the complete code block, then break it down into manageable parts for detailed explanations.
1import asyncio, os
2from videosdk.agents import Agent, [AI voice Agent Sessions](https://docs.videosdk.live/ai_agents/core-components/agent-session), 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 efficient AI Voice Agent specializing in providing fallback responses for voice agents. Your primary role is to ensure seamless user interactions by offering helpful suggestions and guidance when the user's request cannot be fulfilled directly. \n\nCapabilities:\n1. Identify when a user's request cannot be processed or understood.\n2. Provide clear and concise fallback responses that guide the user towards successful interaction.\n3. Offer alternative options or suggestions to help users achieve their goals.\n4. Maintain a polite and professional tone throughout the interaction.\n\nConstraints:\n1. You must not provide specific advice or solutions outside of your programmed capabilities.\n2. Always include a disclaimer that the user should consult a human representative for complex issues.\n3. Avoid making assumptions about the user's needs beyond the information provided.\n4. Ensure that fallback responses are relevant and contextually appropriate to the user's initial request."
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 need a meeting ID. You can generate one using 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

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where we define the behavior of our agent. It inherits from the Agent class and uses the agent_instructions to guide its interactions. The on_enter and on_exit methods define what the agent says when a session starts and ends.
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 crucial for processing user input and generating responses. It coordinates the STT, LLM, and TTS plugins, ensuring that each step of the conversation flow is handled efficiently.
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 and manages the conversation flow. It ensures that the agent connects to the VideoSDK platform and remains active until manually terminated.
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 room options, enabling the playground mode for testing.
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 if __name__ == "__main__": block ensures that the agent starts when the script is executed.
1if __name__ == "__main__":
2    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
3    job.start()
4

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your agent, execute the Python script:
1python main.py
2

Step 5.2: Interacting with the Agent in the

AI Agent playground

Once the script is running, look for the playground link in the console output. Open this link in your browser to interact with your agent. Speak to the agent and observe how it handles fallback responses.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's functionality by integrating custom tools. This enables the agent to perform specialized tasks beyond basic conversation.

Exploring Other Plugins

While this tutorial uses specific plugins for STT, LLM, and TTS, the VideoSDK framework supports various options. Explore other plugins to customize your agent's capabilities further.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly configured in the .env file. Double-check the VideoSDK dashboard for accurate credentials.

Audio Input/Output Problems

Verify your microphone and speaker settings. Ensure that your system's audio devices are correctly configured.

Dependency and Version Conflicts

Use a virtual environment to manage dependencies and avoid version conflicts. Ensure all required packages are installed and up to date.

Conclusion

Summary of What You've Built

In this tutorial, you've built a fully functional AI voice agent capable of providing fallback responses. You've learned how to set up the development environment, create a custom agent class, define a processing pipeline, and test your agent using the VideoSDK framework.

Next Steps and Further Learning

Explore additional features of the VideoSDK framework to enhance your agent's capabilities. Consider integrating custom tools and experimenting with different plugins to create a more versatile voice agent.

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