Start Extraction Job

Start a new structured extraction job.

Extracts structured data from a PDF document using AI models based on a JSON Schema. The document_id and schema_id must be valid UUIDs of previously uploaded documents and created schemas.

How It Works: 1. Document Processing: The system analyzes the PDF document and splits it into manageable sections 2. Schema Analysis: Your JSON schema is parsed and validated for supported features 3. AI Extraction: The AI model processes each section and extracts data according to your schema 4. Validation: Extracted data is validated against your schema to ensure accuracy 5. Results: Structured data is returned in the format defined by your schema

Supported Models: 1. gemini-2.5-flash: Fast, cost-effective model for most use cases (default) 2. gemini-2.5-pro: More powerful model for complex extractions

Configuration Options:

Basic Settings: 1. model: AI model to use for extraction 2. max_num_concurrent_requests: Number of parallel processing requests (1-20) 3. validate_response_schema: Whether to validate extracted data against schema

Advanced Settings: 1. per_key_attribution: Enable detailed reasoning and confidence scores for each field 2. temperature: Control randomness in AI responses (0.0-2.0) 3. seed: Set random seed for reproducible results 4. enable_thinking: Show AI reasoning process 5. n_max_retries: Maximum retry attempts for failed requests

Job Lifecycle: 1. pending: Job is queued and waiting to start 2. processing: AI is actively extracting data from the document 3. completed: Extraction finished successfully with results 4. failed: Extraction failed due to an error 5. cancelled: Job was cancelled before completion

Monitoring Progress: Use the /jobs/ endpoint to check job status and progress: 1. completion_percentage: Overall progress (0-100) 2. current_step: Current processing stage 3. fields_processed: Number of schema fields completed 4. estimated_remaining_time: Time until completion

Getting Results: Once a job is completed, use /jobs//results to retrieve: 1. results: Extracted data conforming to your schema 2. metadata: Processing information, costs, and performance metrics 3. attributions: (if enabled) Reasoning and confidence scores for each field

Example Request:

{
  "document_id": "123e4567-e89b-12d3-a456-426614174000",
  "schema_id": "987fcdeb-51a2-43d1-b456-426614174000",
  "config": {
    "model": "gemini-2.5-flash",
    "per_key_attribution": true,
    "temperature": 0.1,
    "enable_thinking": true
  }
}

Example Response:

{
  "job_id": "456e7890-e89b-12d3-a456-426614174000",
  "status": "pending",
  "created_at": "2025-01-11T18:35:00Z",
  "estimated_completion": "2025-01-11T18:40:00Z"
}

Error Handling: Status 400: Bad Request (invalid document_id or schema_id format) Status 404: Not Found (document or schema not found) Status 422: Unprocessable Entity (invalid schema definition) Status 500: Internal Server Error (processing error)