Build an Astrology AI Voice Agent

Create an AI Voice Agent for astrology with VideoSDK. Follow this step-by-step guide to build, test, and deploy your agent.

Introduction to AI Voice Agents in AI Voice Agent for Astrology

In the rapidly evolving field of artificial intelligence, voice agents have become a cornerstone of user interaction. These agents, often referred to as voice bots or virtual assistants, are designed to understand and respond to human speech. They leverage advanced technologies such as speech-to-text (STT), natural language processing (NLP), and text-to-speech (TTS) to facilitate seamless communication.

What is an AI Voice Agent?

An AI Voice Agent is a software application capable of interpreting human speech and generating appropriate responses. These agents are powered by machine learning models that can comprehend language nuances, making them invaluable for applications requiring human-like interaction.

Why are they important for the AI Voice Agent for Astrology industry?

In the realm of astrology, AI Voice Agents can provide users with personalized astrological readings, daily horoscopes, and insights into zodiac compatibility. They offer a unique, interactive way for users to engage with astrological content, making the experience more engaging and accessible.

Core Components of a Voice Agent

The primary components of an AI Voice Agent include:
  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Model (LLM): Processes and understands the text.
  • Text-to-Speech (TTS): Converts the processed text back into speech.

What You'll Build in This Tutorial

In this tutorial, you will build an AI Voice Agent specifically designed for astrology. This agent will be able to provide astrological readings and answer questions related to zodiac signs and planetary movements.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of our AI Voice Agent involves a series of steps that transform user speech into meaningful responses. The process begins with capturing audio input, which is then converted to text using STT. The text is processed by an LLM to generate a response, which is finally converted back to speech via TTS.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • CascadingPipeline: Manages the flow of audio processing, integrating STT, LLM, and TTS. For more details, refer to the

    Cascading pipeline in AI voice Agents

    .
  • VAD & TurnDetector: These components help the agent determine when to listen and respond. Learn more about the

    Turn detector for AI voice Agents

    .

Setting Up the Development Environment

Prerequisites

Before starting, ensure you have Python 3.11+ installed and a VideoSDK account. Register at app.videosdk.live to obtain your API keys.

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
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 keys:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Let's start by presenting the complete, runnable code for our 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 an AI Voice Agent specializing in astrology. Your persona is that of a knowledgeable and friendly astrologer who provides insightful astrological readings and guidance. Your capabilities include answering questions about zodiac signs, providing daily horoscopes, explaining astrological concepts, and offering personalized astrological insights based on user input. You can also suggest compatible zodiac signs for relationships and provide information about planetary movements and their potential impacts. However, you are not a certified astrologer, and your insights should be considered for entertainment purposes only. Always include a disclaimer that users should consult a professional astrologer for personalized advice. You must respect user privacy and not store any personal data. You should also refrain from making any predictions about health, financial investments, or other sensitive topics."
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 start, you need a meeting ID. Use the following curl command to generate one:
1curl -X POST "https://api.videosdk.live/v1/rooms" \
2-H "Authorization: Bearer YOUR_API_TOKEN" \
3-H "Content-Type: application/json" \
4-d '{"name":"Astrology Session"}'
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is the heart of your voice agent. It defines how the agent interacts with users:
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 inherits from the Agent class and uses predefined instructions to guide interactions.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is crucial for processing audio data. It integrates various plugins to handle STT, LLM, and TTS:
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 plays a specific role:

Step 4.4: Managing the Session and Startup Logic

The start_session function manages the agent's lifecycle:
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4    pipeline = CascadingPipeline(...)
5    session = AgentSession(
6        agent=agent,
7        pipeline=pipeline,
8        conversation_flow=conversation_flow
9    )
10
11    try:
12        await context.connect()
13        await session.start()
14        await asyncio.Event().wait()
15    finally:
16        await session.close()
17        await context.shutdown()
18
The make_context function prepares the environment for the session:
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
The main entry point runs 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

Execute the script using Python:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you will see a playground link in the console. Use this link to interact with your AI Voice Agent in a web interface.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's capabilities using function_tool. This feature lets you integrate custom logic and tools into your agent's workflow.

Exploring Other Plugins

While this tutorial uses specific plugins, VideoSDK supports various STT, LLM, and TTS options. Consider exploring alternatives to suit your needs.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly set in the .env file. Double-check for any typos or missing keys.

Audio Input/Output Problems

Verify that your microphone and speakers are correctly configured and accessible by the application.

Dependency and Version Conflicts

Ensure all dependencies are up-to-date and compatible with your Python version. Use pip list to review installed packages.

Conclusion

Summary of What You've Built

In this tutorial, you constructed an AI Voice Agent capable of providing astrological insights. You learned how to set up the environment, build the agent, and test it using VideoSDK.

Next Steps and Further Learning

To enhance your agent, consider adding more complex logic or integrating additional data sources. Explore the VideoSDK documentation for more advanced features and customization options, starting with the

Voice Agent Quick Start Guide

for foundational knowledge.

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