Build an AI Voice Agent for Recruitment

Step-by-step guide to building an AI Voice Agent for recruitment using VideoSDK.

Introduction to AI Voice Agents in Recruitment

AI Voice Agents are transforming industries by automating interactions and providing real-time assistance. In the recruitment industry, these agents streamline processes by answering queries, scheduling interviews, and offering guidance to candidates and recruiters alike. This tutorial will guide you through building an AI Voice Agent tailored for recruitment using the VideoSDK framework.

What is an AI Voice Agent?

An AI Voice Agent is a software application that uses artificial intelligence to understand and respond to human speech. It integrates technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Natural Language Processing (NLP) to facilitate seamless communication.

Why are they important for the recruitment industry?

In recruitment, AI Voice Agents can handle repetitive tasks, provide instant responses to candidate queries, and assist recruiters by managing schedules and reminders. This automation leads to increased efficiency and allows human recruiters to focus on more strategic tasks.

Core Components of a Voice Agent

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

AI voice Agent core components overview

.

What You'll Build in This Tutorial

In this tutorial, you'll create a fully functional AI Voice Agent for recruitment. The agent will interact with users, provide information, and manage interview schedules.

Architecture and Core Concepts

High-Level Architecture Overview

The AI Voice Agent's architecture involves converting user speech into text, processing the text to generate a response, and converting the response back to speech. This process is managed by a

cascading pipeline in AI voice Agents

that integrates various plugins for each task.
1sequenceDiagram
2    participant User
3    participant Agent
4    participant STT
5    participant LLM
6    participant TTS
7    User->>Agent: Speak
8    Agent->>STT: Convert Speech to Text
9    STT->>Agent: Text
10    Agent->>LLM: Process Text
11    LLM->>Agent: Response
12    Agent->>TTS: Convert Text to Speech
13    TTS->>Agent: Speech
14    Agent->>User: Respond
15

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot.
  • CascadingPipeline: Manages the flow of audio processing from STT to LLM to TTS.
  • VAD & TurnDetector: These components help the agent determine when to listen and when to speak.

Setting Up the Development Environment

Prerequisites

Before you begin, ensure you have Python 3.11+ installed and a VideoSDK account. Sign up at app.videosdk.live to access the API keys required for this tutorial.

Step 1: Create a Virtual Environment

Create a virtual environment to manage dependencies:
1python -m venv venv
2source venv/bin/activate  # On Windows use `venv\\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
2VIDEOSDK_SECRET_KEY=your_secret_key
3

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

Here's the complete code to create your AI Voice 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 and efficient AI Voice Agent specialized in the recruitment industry. Your primary role is to assist recruiters and job seekers by providing information and guidance throughout the recruitment process. You can answer questions about job openings, application procedures, interview tips, and company culture. Additionally, you can schedule interviews and send reminders to candidates. However, you are not a human recruiter and cannot make hiring decisions or provide personalized career advice. Always remind users to consult with a human recruiter for personalized guidance and final decisions. Your responses should be concise, informative, and professional, ensuring a seamless experience for both recruiters and candidates."
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 to understand each component.

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/meetings" -H "Authorization: Bearer YOUR_ACCESS_TOKEN"
2

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class defines the behavior of your agent. It inherits from Agent and initializes with specific instructions for handling recruitment-related queries.
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 the backbone of your agent, orchestrating the flow of data through various plugins. For more details, you can explore the

Voice Agent Quick Start Guide

.
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 manages the lifecycle of the agent's session, connecting to the VideoSDK service and handling cleanup.
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

Running and Testing the Agent

Step 5.1: Running the Python Script

Run your script with:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

After starting the agent, you'll see a

playground

link in the console. Open this link in a browser to interact with your agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

Enhance your agent by integrating custom tools to handle specific tasks, such as parsing resumes or providing detailed job descriptions.

Exploring Other Plugins

Experiment with different plugins for STT, LLM, and TTS to optimize performance and cost. Consider using the

ElevenLabs TTS Plugin for voice agent

and the

Deepgram STT Plugin for voice agent

for enhanced capabilities.

Troubleshooting Common Issues

API Key and Authentication Errors

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

Audio Input/Output Problems

Check your microphone and speaker settings if you encounter issues with audio input or output.

Dependency and Version Conflicts

Ensure all dependencies are up-to-date and compatible with Python 3.11+.

Conclusion

Summary of What You've Built

You've built an AI Voice Agent capable of assisting in the recruitment process, providing information, and managing schedules. The

AI voice Agent Sessions

provide a robust framework for managing interactions.

Next Steps and Further Learning

Explore additional features and plugins to further enhance your agent's capabilities and efficiency. Consider integrating the

OpenAI LLM Plugin for voice agent

and

Silero Voice Activity Detection

to improve interaction quality.

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