Documentation Index
Fetch the complete documentation index at: https://docs.teli.ai/llms.txt
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Overview
Extraction fields allow you to automatically extract structured data from SMS and voice conversations. You can now provide custom descriptions to improve extraction accuracy, or rely on smart defaults for common fields.
Pass a simple array of field names. The system automatically generates intelligent descriptions for common fields:
{
"extraction_fields": ["credit_score", "interest_rate", "loan_balance"]
}
credit_score → “Extract ONLY the numeric credit score (e.g., 750). Do not include phrases like ‘my score is’. Just the number.”interest_rate → “Extract the interest rate as a percentage (e.g., 6.5%, 5.25). Include the % symbol if mentioned.”loan_balance → “Extract the loan balance or amount as a number (e.g., 250000, $250k).”scheduled_time / appointment_date → “Extract the date and/or time mentioned (e.g., ‘tomorrow at 3pm’).”phone_number → “Extract the phone number in format XXX-XXX-XXXX.”email → “Extract the email address exactly as provided.”
For fields without smart defaults, a generic description is generated.
For custom extraction logic, provide objects with name, description, and type:
{
"extraction_fields": [
{
"name": "credit_score",
"description": "Extract ONLY the numeric credit score. Do not include phrases like 'my score is' or 'around'. Just the number.",
"type": "string"
},
{
"name": "is_interested",
"description": "Return true if the customer shows interest, false otherwise.",
"type": "boolean"
},
{
"name": "budget",
"description": "Extract the customer's budget as a number without currency symbols.",
"type": "number"
}
]
}
{
"extraction_fields": [
"credit_score",
{
"name": "custom_field",
"description": "Extract the customer's specific requirement...",
"type": "string"
},
"interest_rate"
]
}
Field Types
The data type for extraction. Options:
string (default) - Text valuesnumber - Numeric values onlyboolean - true/false values
Best Practices for Descriptions
Be Specific
❌ Bad: “Extract the credit score”
✅ Good: “Extract ONLY the numeric credit score (e.g., 750, 680). Do not include phrases like ‘my score is’ or ‘around’. Just the number.”
Provide Examples
❌ Bad: “Extract the date”
✅ Good: “Extract the appointment date in MM/DD/YYYY format (e.g., 03/15/2026)“
❌ Bad: “Extract the phone number”
✅ Good: “Extract the phone number in format XXX-XXX-XXXX or (XXX) XXX-XXXX”
Handle Edge Cases
❌ Bad: “Extract if interested”
✅ Good: “Return true if the customer shows interest or curiosity. Return false if they say ‘not interested’, ‘no thanks’, or similar negative responses.”
POST /v1/agents
{
"agent_type": "voice",
"agent_name": "Mortgage Qualifier",
"starting_message": "Hi! I'm calling about your mortgage inquiry.",
"prompt": "You are a mortgage qualification agent...",
"voice_id": "11labs-Adrian",
"organization_id": "org_xxx",
"user_id": "user_xxx",
"extraction_fields": [
{
"name": "credit_score",
"description": "Extract ONLY the numeric credit score. Just the number, no phrases.",
"type": "string"
},
{
"name": "loan_amount",
"description": "Extract the desired loan amount as a number (e.g., 250000, 350k).",
"type": "string"
},
"interest_rate",
"scheduled_callback_time"
]
}
GET /v1/sms/extractions/{thread_id}
GET /v1/voice/calls/{call_id}/extractions
{
"success": true,
"extracted_fields": {
"credit_score": "750",
"interest_rate": "6.5%",
"loan_amount": "$250,000",
"scheduled_callback_time": "tomorrow at 3pm"
}
}
{
"extraction_fields": ["credit_score", "interest_rate"]
}
{
"extraction_fields": [
{
"name": "credit_score",
"description": "Extract ONLY the numeric credit score...",
"type": "string"
},
"interest_rate"
]
}
