Build an AI Voice Agent for Banking

Step-by-step guide to building an AI Voice Agent for banking using VideoSDK. Includes code examples and testing instructions.

Introduction to AI Voice Agents in the Banking Industry

AI Voice Agents are sophisticated software systems designed to interact with users through voice commands and responses. These agents leverage technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Language Models (LLM) to understand and process human language, providing seamless and intuitive user experiences.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a digital assistant that can understand and respond to spoken language. It uses a combination of STT to convert spoken words into text, LLMs to process and understand the text, and TTS to convert the response back into speech. This technology enables real-time interaction between humans and machines, making it a valuable tool in various industries.

Why are they important for the banking industry?

In the banking industry, AI Voice Agents can significantly enhance customer service by providing 24/7 support, handling routine inquiries, and guiding users through complex processes without human intervention. They can assist with tasks like checking account balances, explaining loan options, and providing information on banking products, thereby improving efficiency and customer satisfaction.

Core Components of a

Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Language Model): Processes and understands the text to generate appropriate responses.
  • TTS (Text-to-Speech): Converts text responses back into speech.
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 will learn how to build an AI

Voice Agent

 tailored for the banking industry using the VideoSDK framework. This agent will be capable of handling common banking inquiries and providing helpful information to users.

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 data flow begins with the user's speech, which is captured and converted into text by the STT module. The text is then processed by the LLM to understand the user's intent and generate a response. Finally, the TTS module converts the response text back into speech, completing the interaction cycle.
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, which you can create at the VideoSDK website.

Step 1: Create a Virtual Environment

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

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk-agents videosdk-plugins-openai videosdk-plugins-elevenlabs videosdk-plugins-deepgram videosdk-plugins-silero videosdk-plugins-turn-detector
2

Step 3: Configure API Keys in a .env file

Create a .env file in your project directory and add your API keys:
1VIDEOSDK_API_KEY=your_videosdk_api_key
2OPENAI_API_KEY=your_openai_api_key
3ELEVENLABS_API_KEY=your_elevenlabs_api_key
4DEEPGRAM_API_KEY=your_deepgram_api_key
5

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

Here is the complete code for building 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 friendly AI Voice Agent designed specifically for the banking industry. Your primary role is to assist customers with their banking needs by providing accurate information and guidance. You can answer questions related to account balances, recent transactions, loan inquiries, and branch locations. Additionally, you can help users navigate through banking services and provide information on banking products such as savings accounts, credit cards, and loans. However, you are not authorized to perform any transactions or access personal banking information. Always remind users to contact their bank directly for sensitive transactions or if they need to discuss personal account details. Ensure that all interactions are secure and respect user privacy. You must include a disclaimer that you are not a financial advisor and that users should consult with a professional for financial advice."
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 generate a meeting ID, use the following curl command:
1curl -X POST \
2  https://api.videosdk.live/v1/meetings \
3  -H "Authorization: Bearer YOUR_VIDEOSDK_API_KEY" \
4  -H "Content-Type: application/json"
5
This command will return a meeting ID that you can use to join a session.

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where you define the behavior of your AI Voice Agent. It extends the base Agent class and includes methods for handling session entry and exit:
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 uses the agent_instructions to guide its interactions with users.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is the core of the agent's functionality, integrating all necessary plugins:
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 in the pipeline plays a specific role in processing audio and generating responses.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the

AI voice Agent Sessions

 and manages its lifecycle:
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4
5    pipeline = CascadingPipeline(
6        stt=DeepgramSTT(model="nova-2", language="en"),
7        llm=OpenAILLM(model="gpt-4o"),
8        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
9        vad=SileroVAD(threshold=0.35),
10        turn_detector=TurnDetector(threshold=0.8)
11    )
12
13    session = AgentSession(
14        agent=agent,
15        pipeline=pipeline,
16        conversation_flow=conversation_flow
17    )
18
19    try:
20        await context.connect()
21        await session.start()
22        await asyncio.Event().wait()
23    finally:
24        await session.close()
25        await context.shutdown()
26
The make_context function sets up the environment for the agent:
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
Finally, the if __name__ == "__main__": block starts the agent:
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 AI Voice Agent, execute the following command in your terminal:
1python main.py
2
This will start the agent and provide a link to the VideoSDK playground in the console.

Step 5.2: Interacting with the Agent in the Playground

Visit the playground link to interact with your agent. You can test its capabilities by asking banking-related questions and observing its 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, known as function_tool. This enables you to add specialized capabilities tailored to your needs.

Exploring Other Plugins

While this tutorial uses specific plugins for STT, LLM, and TTS, you can explore other options available in the VideoSDK framework to suit your requirements.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file. Double-check for typos and verify your account settings if you encounter authentication errors.

Audio Input/Output Problems

If you experience issues with audio quality or functionality, verify your microphone and speaker settings. Ensure that your system permissions allow audio access.

Dependency and Version Conflicts

Ensure all required packages are installed with compatible versions. Use a virtual environment to manage dependencies and avoid conflicts.

Conclusion

Summary of What You've Built

In this tutorial, you have successfully built an AI Voice Agent for the banking industry using the VideoSDK framework. This agent can handle common banking inquiries and provide valuable information to users.

Next Steps and Further Learning

To further enhance your agent, consider exploring additional plugins and custom tools. Continue learning about the VideoSDK framework to unlock more advanced features and capabilities.

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