Document processing

Document processing is the combination of vision and language: a vision-language model reads the page, a classifier or detector finds its structure, an embedding model indexes it.

• Page images as image_url parts to a model advertising vision, with the extraction instructions in the prompt and, where the model advertises it, a json_schema response format for structured output.
• A classifier run on the same pages through POST /v1/tasks with task: "classification"; a detector through detection; embeddings through embedding.
• A long batch of pages as a job on POST /v1/jobs, with the pages uploaded once through POST /v1/uploads and named by upload_id.

• Each step bills in its own unit: tokens for the language step (image count and dimensions change the charge), per_1000_classifications for the classifier, tokens for the embedding.

• Structured text from the language step; labels with scores from the classifier; vectors from the embedding; each with its own usage record and request id, so the Console shows the cost of each step.