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. Extraction: The pipeline processes each section and extracts data according to your schema
4. Validation: Extracted data is validated against your schema to ensure constraints and requirements are met
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

Streaming Mode (stream=true):

When stream=true, the endpoint returns an SSE stream instead of a job ID. Results are streamed as subtrees complete, enabling real-time progress visibility in the frontend.

• Response: SSE stream (text/event-stream) with events: start, subtree_result, subtree_error, agentic_array_progress
• Job records are still created and results persisted (retrievable via GET /jobs/{job_id})
• Always durable: Uses Temporal workflow—job continues running even if client disconnects
• Automatic retries: Temporal handles activity failures and retries automatically
• Use case: Interactive frontend UI where users want real-time extraction progress

SSE Event Types:

1. start: Emitted once with job_id, total_subtrees, and start_time
2. subtree_result: Emitted per successful subtree with extracted_result, attributions, usage_metadata
3. subtree_error: Emitted per failed subtree with error_message and error_type

See STREAMING-DESIGN.md for full protocol specification.

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 GET /jobs/{job_id} 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. total_fields: Total number of schema fields to process

Getting Results:

Once a job is completed, use GET /jobs/{job_id}/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
    "additional_instructions": "Extract information only about company X. Do not include information about company Y."
  }
}

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:

Document and schema validation occurs before job creation for both streaming and non-streaming paths:

• 400: Bad Request (invalid document_id or schema_id format)
• 404: Not Found (document or schema not found)
• 422: Unprocessable Entity (invalid schema definition)
• 429: Too Many Requests (user has exceeded concurrency limit of 5 active jobs)
• 500: Internal Server Error (processing error)

{
  "config": {
    "model": "gemini-2.5-flash",
    "enable_thinking": true
  }
}