Automatic Goggles

A Python package for extracting structured fields from call transcripts with confidence scores using DSPy and OpenAI's language models.

Features

• Extract structured fields from conversation transcripts
• Get confidence scores for extracted data using log probabilities
• Optional reasoning explanations - Control performance and costs with the include_reasoning flag
• Support for multiple field types (currently supports string fields)
• Easy integration with OpenAI API
• Similar functionality to RetellAI post-call processing

Installation

pip install automatic-goggles

Quick Start

from transtype import TranscriptProcessor

# Initialize the processor with your OpenAI API key
processor = TranscriptProcessor(api_key="your-openai-api-key")

# Define your input data
data = {
    "messages": [
        {
            "role": "assistant",
            "content": "Hi, this is Marcus, I'm a customer service representative with TechFlow Solutions in Downtown Seattle."
        },
        {
            "role": "user", 
            "content": "I need to discuss my account billing issues."
        }
    ],
    "fields": [
        {
            "field_name": "representative_name",
            "field_type": "string",
            "format_example": "Sarah Chen"
        }
    ]
}

# Process the transcript
result = processor.process(data)
print(result)

Reasoning Flag

You can control whether to include reasoning explanations in the output using the include_reasoning parameter. This affects both performance and API costs:

With Reasoning (Default)

# Default behavior - includes detailed reasoning
processor = TranscriptProcessor(api_key="your-openai-api-key", include_reasoning=True)
# OR simply:
processor = TranscriptProcessor(api_key="your-openai-api-key")

result = processor.process(data)
# Output includes field_reason with explanation

Without Reasoning (Faster & Cost-Effective)

# Faster processing, lower API costs
processor = TranscriptProcessor(api_key="your-openai-api-key", include_reasoning=False)

result = processor.process(data)
# Output has field_reason set to null

Benefits of disabling reasoning:

• ⚡ Faster processing - Fewer tokens generated
• 💰 Lower costs - Reduced OpenAI API token usage
• 🎯 Focused output - Just the extracted values and confidence scores

When to use each mode:

• With reasoning: When you need explanations for debugging, quality assurance, or transparency
• Without reasoning: For production systems where you only need the extracted values

Output Format

With Reasoning (Default)

{
    "fields": [
        {
            "field_name": "representative_name",
            "field_value": "Marcus",
            "field_confidence": 0.95,
            "field_reason": "Representative introduced himself as 'Marcus' at the beginning of the conversation"
        }
    ]
}

Without Reasoning

{
    "fields": [
        {
            "field_name": "representative_name",
            "field_value": "Marcus", 
            "field_confidence": 0.95,
            "field_reason": null
        }
    ]
}

Requirements

• Python 3.8+
• OpenAI API key

License

MIT License