Granular operations
No workspace. Parse, classify, extract, or ground individual documents and get back results immediately.- Provide a document via
POST /v1/uploads, a public URL, or a workspace file reference. - Call an operation:
POST /v1/parse - Get results: markdown, categories, extracted JSON, or target locations.
- Results include presigned URLs that expire when the job’s
payload_expires_atpasses.
- You want single-document intelligence without long-term storage.
- You’re analyzing a one-off PDF or spreadsheet.
- You’re integrating document intelligence into an existing pipeline.
Workspace workflows
Persistent storage + derived outputs + querying. Upload files into a workspace, trigger ingestion to build derivatives (parsed markdown, extracted tables, search indexes), then query the corpus.- Create a workspace with a chosen domain and access labels.
- Upload files:
POST /v1/workspaces/{workspace_id}/files - Ingest files to create derivatives:
POST /v1/workspaces/{workspace_id}/ingestions - Query with tools: search, file reading and QA, knowledge graph walk, intelligent search.
- You maintain a corpus of documents (engagement files, reference materials).
- You need to search or query across many files.
- You want to build a knowledge graph of relationships.
- You need to track file history and versions.
Key differences
Shared concepts
Both patterns share:- Authentication —
X-API-Keyheader - Job lifecycle — POST returns 200/202, poll with
GET /v1/jobs/{job_id} - Idempotency —
Idempotency-Keyheader for replay safety - Errors — typed error codes and messages