Introduction to AI Voice Agents in Dialogue History Management
AI Voice Agents are sophisticated software systems designed to interpret and respond to human speech. These agents utilize technologies such as Speech-to-Text (STT), Text-to-Speech (TTS), and Language Models (LLM) to provide interactive and intelligent responses. In dialogue history management, AI Voice Agents play a crucial role by offering capabilities like retrieving past conversations, summarizing dialogue, and providing insights based on previous interactions. This tutorial will guide you through building an AI
Voice Agent
using the VideoSDK framework, focusing on dialogue history management.Architecture and Core Concepts
Before diving into the implementation, it is essential to understand the high-level architecture of an AI
Voice Agent
. The data flow begins with user speech, which is processed through a series of components, ultimately resulting in an agent response.
Understanding Key Concepts in the VideoSDK Framework
- Agent: Represents the core class of your AI
Voice Agent
. - CascadingPipeline: Manages the audio processing flow from STT to LLM to TTS. Learn more about the
Cascading pipeline in AI voice Agents
. - VAD & TurnDetector: Ensure the agent knows when to listen and speak.
Setting Up the Development Environment
To start building your AI
Voice Agent
, ensure you have Python 3.11+ and a VideoSDK account. Follow these steps to set up your environment:Step 1: Create a Virtual Environment
1python -m venv venv
2source venv/bin/activate # On Windows use `venv\\Scripts\\activate`
3Step 2: Install Required Packages
1pip install videosdk
2pip install python-dotenv
3Step 3: Configure API Keys
Create a
.env file in your project directory and add your VideoSDK API key:1VIDEOSDK_API_KEY=your_api_key_here
2Building the AI Voice Agent: A Step-by-Step Guide
Below is the complete, runnable code for the AI Voice Agent. We will break this down in subsequent sections.
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 dialogue history management expert AI Voice Agent. Your persona is that of a knowledgeable and organized assistant who helps users manage and navigate their conversation history effectively. Your primary capabilities include retrieving past conversation details, summarizing dialogue history, and providing insights based on previous interactions. You can also assist users in organizing their dialogue history for better accessibility and understanding. However, you must adhere to the following constraints: you cannot store or access any personal data without explicit user consent, you are not allowed to make assumptions beyond the provided dialogue history, and you must always prioritize user privacy and data security. Additionally, you should inform users that your insights are based solely on the dialogue history and not on any external data sources."
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=[Silero Voice Activity Detection](https://docs.videosdk.live/ai_agents/plugins/silero-vad)(threshold=0.35),
32 turn_detector=[Turn detector for AI voice Agents](https://docs.videosdk.live/ai_agents/plugins/turn-detector)(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()
63Step 4.1: Generating a VideoSDK Meeting ID
To create a meeting ID, use the following
curl command:1curl -X POST "https://api.videosdk.live/v1/rooms" \
2-H "Authorization: YOUR_API_KEY" \
3-H "Content-Type: application/json" \
4-d '{"region":"us"}'
5Step 4.2: Creating the Custom Agent Class
The
MyVoiceAgent class is the core of our voice agent. It inherits from the Agent class and initializes with specific instructions for dialogue history management. The on_enter and on_exit methods define the agent's behavior when a session starts and ends.Step 4.3: Defining the Core Pipeline
The
CascadingPipeline is crucial for processing audio data. It integrates various plugins:- DeepgramSTT: Converts speech to text.
- OpenAILLM: Processes text and generates responses. Explore the
OpenAI LLM Plugin for voice agent
. - ElevenLabsTTS: Converts text back to speech.
- SileroVAD: Detects voice activity.
- TurnDetector: Manages conversational turns.
Step 4.4: Managing the Session and Startup Logic
The
start_session function manages the agent's lifecycle, creating an AgentSession with the defined pipeline and conversation flow. The make_context function sets up the room options, and the main block starts the job.Running and Testing the Agent
Step 5.1: Running the Python Script
Execute the script by running:
1python main.py
2Step 5.2: Interacting with the Agent in the Playground
Once the script is running, you will see a playground link in the console. Open this link in your browser to interact with the agent.
Advanced Features and Customizations
Extending Functionality with Custom Tools
You can extend the agent's capabilities by integrating custom tools. This involves defining new functions that the agent can call during interactions.
Exploring Other Plugins
Consider exploring other STT, LLM, and TTS plugins to enhance your agent's performance and capabilities.
Troubleshooting Common Issues
API Key and Authentication Errors
Ensure your API key is correctly set in the
.env file and that you have the necessary permissions.Audio Input/Output Problems
Check your microphone and speaker settings. Ensure they are properly configured and accessible by the application.
Dependency and Version Conflicts
Ensure all dependencies are compatible with Python 3.11+ and are installed in your virtual environment.
Conclusion
In this tutorial, you have built a functional AI Voice Agent capable of managing dialogue history using VideoSDK. As next steps, consider exploring additional plugins and customizations to enhance your agent's capabilities.
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