brainkb_ingest_upload

org.brainkb/brainkb · Verify this server

Ingest a file you staged with `POST /upload` into a named graph. This is the route for a large local file: your HTTP client streams the bytes straight to this server over HTTPS, then you name the resulting upload_id here. The server reads its own staged copy and posts it to the ingest API internally, so the RDF never passes through a model's context — nothing to transcribe, no context-window ceiling, and no reason to split the document (splitting breaks blank-node identity and silently detaches triples, permanently). Stage a file with any HTTP client — the point is that the LIBRARY reads the file, so the bytes never pass through a model: import requests, hashlib, pathlib f = pathlib.Path("review.ttl") r = requests.post( "https://mcp.brainkb.org/upload", params={"filename": f.name, "sha256": hashlib.sha256(f.read_bytes()).hexdigest()}, headers={"Authorization": f"Bearer {TOKEN}"}, data=f.open("rb"), # streamed — never loaded into memory ) print(r.json()) # -> {"upload_id": "up_...", "state": "staged"} It returns an upload_id and the sha256 the server computed — compare it with your own before ingesting. Returns a job_id; poll brainkb_job_status, then reconcile brainkb_delta(job_id) against the triple count you expected. The staged copy is deleted once the ingest API has accepted the bytes.