# 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
```bash
pip install automatic-goggles
```
## Quick Start
```python
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)
```python
# 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)
```python
# 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)
```json
{
"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
```json
{
"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
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