Feedback: guides-lemur-transcript-citations

Documentation Feedback

Original URL: https://www.assemblyai.com/docs/guides/lemur-transcript-citations
Category: guides
Generated: 05/08/2025, 4:39:31 pm

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Claude Sonnet 4 Feedback

Generated: 05/08/2025, 4:39:30 pm

Technical Documentation Analysis & Recommendations

Critical Issues

1. Incomplete Code Examples

Problem: All three main examples contain empty URL arrays with TODO comments, making the code non-functional.

transcripts = transcribe([
    '', # TODO ADD URLS
])

Solution:

• Provide complete, working examples with sample audio URLs or local file paths
• Include a troubleshooting section for common transcription issues
• Add validation code to check if URLs are accessible

2. Missing Prerequisites & Setup

Problem: The documentation jumps into code without proper environment setup. Solution: Add a comprehensive setup section:

## Prerequisites
- Python 3.7+ installed
- AssemblyAI API key ([get yours here](link))
- OpenAI API key ([setup guide](link))
- Minimum 500MB available memory for processing

## Environment Setup
1. Create a virtual environment:
   ```bash
   python -m venv transcript-citations
   source transcript-citations/bin/activate  # On Windows: transcript-citations\Scripts\activate

2. Install dependencies: [existing pip install command]
3. Set environment variables:

   export ASSEMBLYAI_API_KEY="your_key_here"
   export OPENAI_API_KEY="your_key_here"

### **Structure & Navigation Issues**

#### 3. **Poor Information Architecture**
**Problem**: The document lacks clear section hierarchy and navigation aids.
**Solution**: Restructure with:
```markdown
## Table of Contents
- [Quick Start](#quick-start)
- [Core Concepts](#core-concepts)
- [Implementation Guide](#implementation-guide)
- [Use Cases & Examples](#use-cases--examples)
- [Troubleshooting](#troubleshooting)
- [API Reference](#api-reference)

## Quick Start (5-minute setup)
[Minimal working example here]

## Core Concepts
### What are Transcript Citations?
### How Embeddings Work
### LeMUR Integration

4. Missing Error Handling

Problem: No error handling or troubleshooting guidance provided. Solution: Add comprehensive error handling:

import assemblyai as aai
import logging

def transcribe_with_error_handling(urls):
    try:
        transcriber = aai.Transcriber()
        result = transcriber.transcribe_group(urls)

        if result.status == aai.TranscriptStatus.error:
            raise Exception(f"Transcription failed: {result.error}")

        return result
    except aai.exceptions.APIError as e:
        logging.error(f"API Error: {e}")
        raise
    except Exception as e:
        logging.error(f"Unexpected error: {e}")
        raise

Technical Clarity Issues

5. Inadequate API Explanations

Problem: Functions like find_relevant_matches() are complex but poorly explained. Solution: Add detailed docstrings and explanations:

def find_relevant_matches(embedded_blocks, new_block_text, k=3):
    """
    Find the most semantically similar transcript segments to a given text.

    Args:
        embedded_blocks (dict): Dictionary mapping (timestamp, transcript_id, text)
                               tuples to their embedding vectors
        new_block_text (str): Text to find matches for (e.g., LeMUR response)
        k (int): Number of top matches to return (default: 3)

    Returns:
        list: List of dictionaries containing:
            - timestamp: Start time in milliseconds
            - transcript_id: Unique identifier for the transcript
            - text: Original transcript text
            - confidence: Similarity score (0-1, higher is more similar)

    Example:
        >>> matches = find_relevant_matches(embeddings, "calcium and cell junctions", k=2)
        >>> print(f"Found {len(matches)} matches with confidence {matches[0]['confidence']:.3f}")
    """

6. Missing Performance Guidance

Problem: No guidance on costs, processing time, or optimization. Solution: Add performance section:

## Performance & Cost Considerations

### Embedding Costs
- OpenAI text-embedding-ada-002: $0.0001 per 1K tokens
- 1 hour of audio ≈ $0.0015 in embedding costs
- Use `granularity='paragraph'` for lower costs, `'sentence'` for higher precision

### Processing Time
- Transcription: ~10-30% of audio length
- Embedding generation: ~2-5 seconds per paragraph
- Citation matching: ~100ms per query

### Optimization Tips
- Cache embeddings to avoid regeneration
- Use paragraph granularity for general use cases
- Batch multiple queries for better performance

User Experience Issues

7. No Validation or Testing Guidance

Problem: Users have no way to verify their implementation works correctly. Solution: Add testing section:

## Testing Your Implementation

### Validate Transcription
```python
def test_transcription():
    # Test with a short sample file
    sample_url = "https://example.com/sample-audio.mp3"  # 30-second sample
    transcript = transcribe([sample_url])[0]

    assert transcript.status == aai.TranscriptStatus.completed
    assert len(transcript.text) > 0
    print(f"✅ Transcription successful: {len(transcript.text)} characters")

Validate Citations

[Include test cases with expected outputs]

#### 8. **Insufficient Context for Beginners**
**Problem**: The guide assumes deep familiarity with embeddings and ML concepts.
**Solution**: Add conceptual explanations:
```markdown
## Understanding the Technology

### What are Embeddings? (Simple Explanation)
Embeddings convert text into numbers that represent meaning. Similar concepts get similar numbers, allowing us to find related content mathematically.

**Example**:
- "The cat sat on the mat" → [0.2, 0.8, 0.1, ...]
- "A feline rested on the rug" → [0.3, 0.7, 0.2, ...] (similar numbers!)

### Why Use Citations?
LLMs can hallucinate or misremember details. Citations provide:
- **Verification**: Check if the AI's answer is supported by the source
- **Context**: See the full conversation around a point
- **Timestamps**: Jump directly to relevant audio sections

Missing Content

9. No Advanced Configuration

Add sections for:

• Custom embedding models
• Adjusting similarity thresholds
• Handling large transcripts (chunking strategies)
• Integration with other AssemblyAI features

10. No Production Deployment Guidance

Include:

• Rate limiting considerations
• Caching strategies
• Security best practices for API keys
• Scaling for multiple concurrent requests

Quick Wins

1. Add a complete minimal example at the top that works out-of-the-box
2. Include expected output samples for each code block
3. Add parameter explanations for all function calls
4. Provide troubleshooting steps for common issues (authentication, file formats, etc.)
5. Include links to related documentation (LeMUR API reference, embedding models, etc.)

This documentation has good technical depth but needs significant improvements in accessibility, completeness, and user guidance to be truly effective.

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