Feedback: integrations-livekit

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/integrations/livekit
Category: integrations
Generated: 05/08/2025, 4:27:57 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:27:56 pm

Technical Documentation Analysis: AssemblyAI LiveKit Integration

Overall Assessment

This documentation provides a solid foundation but has several areas for improvement in clarity, completeness, and user experience. Here’s my detailed analysis with actionable recommendations:

1. Missing Information

Critical Gaps

• Prerequisites section: Missing specific Python version requirements, LiveKit version compatibility
• Error handling: No guidance on common errors, troubleshooting, or debugging
• Complete working example: The basic usage example is incomplete (missing imports, llm/tts configuration)
• Performance considerations: No information about latency, resource usage, or scaling
• Limitations: What doesn’t work, known issues, or constraints

Recommended Additions

# Add a complete, runnable example
import asyncio
from livekit import agents
from livekit.plugins import assemblyai, openai, elevenlabs, silero

async def entrypoint(ctx: agents.JobContext):
    session = agents.AgentSession(
        ctx=ctx,
        stt=assemblyai.STT(
            end_of_turn_confidence_threshold=0.7,
            min_end_of_turn_silence_when_confident=160,
            max_turn_silence=2400,
        ),
        llm=openai.LLM(model="gpt-4"),
        tts=elevenlabs.TTS(),
        vad=silero.VAD.load(),
        turn_detection="stt",
    )
    await session.astart()

if __name__ == "__main__":
    agents.run_app(entrypoint)

2. Unclear Explanations

Issues Identified

• Parameter units: Some parameters mention “ms” while others don’t specify units
• Technical jargon: “end of turn confidence score” needs clearer explanation
• Configuration relationship: How parameters interact with each other isn’t clear

Improvements Needed

### Turn Detection Parameters Explained

**end_of_turn_confidence_threshold** (float, 0.0-1.0)
- Controls how certain the model must be before ending a turn
- Higher values (0.8-0.9): More conservative, waits longer before ending turns
- Lower values (0.5-0.6): More aggressive, ends turns sooner
- **Use case**: Set higher for complex queries, lower for simple commands

**min_end_of_turn_silence_when_confident** (int, milliseconds)
- Minimum silence duration when confidence is high
- Range: 100-500ms recommended
- **Trade-off**: Lower = faster response, Higher = fewer interruptions

3. Better Examples

Current Issues

• Only one basic example
• No real-world scenarios
• Missing context for parameter choices

Recommended Examples

# Example 1: Customer Service Agent (Conservative)
stt = assemblyai.STT(
    end_of_turn_confidence_threshold=0.8,  # Wait for clear end
    min_end_of_turn_silence_when_confident=200,  # Give users time
    max_turn_silence=3000,  # Allow for longer pauses
)

# Example 2: Quick FAQ Bot (Aggressive)
stt = assemblyai.STT(
    end_of_turn_confidence_threshold=0.6,  # Respond quickly
    min_end_of_turn_silence_when_confident=100,  # Minimal wait
    max_turn_silence=1500,  # Short timeout
)

# Example 3: Data Entry Agent (Balanced)
stt = assemblyai.STT(
    end_of_turn_confidence_threshold=0.7,  # Default balance
    min_end_of_turn_silence_when_confident=160,  # Standard wait
    max_turn_silence=2400,  # Allow for lookup time
)

4. Improved Structure

Current Structure Issues

• Configuration section is too dense
• No logical flow from basic to advanced
• Missing navigation aids

Recommended Restructure

# LiveKit Integration

## Prerequisites
- Python 3.8+
- LiveKit Agents 0.8.0+
- Valid AssemblyAI API key

## Quick Start
### 1. Installation
### 2. API Key Setup
### 3. Basic Implementation
### 4. Testing Your Setup

## Configuration Guide
### Turn Detection (Recommended)
### VAD-based Detection (Alternative)
### Advanced Parameters

## Real-world Examples
### Customer Service Bot
### FAQ Assistant
### Data Collection Agent

## Troubleshooting
### Common Errors
### Performance Issues
### Debug Mode

## Best Practices
## API Reference

5. User Pain Points

Identified Issues

1. Setup confusion: Users don’t know if their setup is working
2. Parameter tuning: No guidance on how to adjust parameters
3. Integration complexity: Missing context about how this fits into larger applications
4. Error debugging: No help when things go wrong

Solutions

Add Setup Verification

# Add this section: "Verify Your Setup"
import asyncio
from livekit.plugins import assemblyai

async def test_connection():
    try:
        stt = assemblyai.STT()
        print("✅ AssemblyAI STT initialized successfully")
        return True
    except Exception as e:
        print(f"❌ Setup failed: {e}")
        return False

# Run: python test_setup.py

Add Troubleshooting Section

## Troubleshooting

### Common Errors

**"Invalid API key"**
- Verify your API key in the dashboard
- Check that ASSEMBLYAI_API_KEY is set correctly
- Ensure no extra spaces or characters

**"Connection timeout"**
- Check internet connection
- Verify firewall settings
- Try increasing timeout values

**Agent interrupts too frequently**
- Increase `end_of_turn_confidence_threshold` to 0.8+
- Increase `min_end_of_turn_silence_when_confident` to 200+ms

**Agent waits too long to respond**
- Decrease `end_of_turn_confidence_threshold` to 0.6 or lower
- Decrease `max_turn_silence` to 1500ms or lower

6. Additional Recommendations

Add Interactive Elements

• Parameter tuning calculator/guide
• Configuration generator based on use case
• Performance benchmarking tools

Improve Cross-references

• Link to specific LiveKit concepts
• Reference related AssemblyAI features
• Connect to broader voice agent patterns

Add Monitoring Section

## Monitoring and Analytics
- How to track turn detection accuracy
- Logging configuration for debugging
- Performance metrics to monitor

Priority Implementation Order

1. High Priority: Add complete working example, troubleshooting section
2. Medium Priority: Improve parameter explanations, add use-case examples
3. Low Priority: Restructure sections, add monitoring guidance

This analysis should help transform the documentation from a basic reference into a comprehensive, user-friendly guide that reduces friction and improves developer success rates.

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