Resource ingestion

This feature is in beta. Behavior and availability may change as we expand support.

Use resource ingestion when you have clinical PDFs or related images (scans, screenshots, print-to-PDF notes) that should become structured logs in Olira. You label the target log type at upload time; Olira stores the binary, runs text extraction / OCR, then emits an event log that flows into patient state like any other submission. This path is for document binaries. For already-structured payloads, use live logging or historical JSONL backfill. For continuous sensor Parquet, use passive signal ingestion.

Supported materials

Kind	Content types
PDF	`application/pdf`
Images	`image/png`, `image/jpeg`, `image/jpg`, `image/webp`, `image/tiff`, `image/gif`

Max file size is 100 MB per document. You choose the clinical target — the platform does not infer log_type or document_type / note_type from the filename.

Target log types

`log_type`	Required labels	When to use
`unstructured_report`	`document_type`	Raw document blob for extraction (labs, imaging reports, discharge packets, etc.)
`clinical_note`	`note_type` + `source`	Provider-authored note that should land as a `clinical_note` log

document_type values (for unstructured_report): clinical_note, radiology_imaging, operative_procedure, cardiology_diagnostic, pathology_report, lab_report, genomic_report, discharge_summary, patient_intake, external_records_package. note_type values (for clinical_note): progress_note, history_and_physical, social_history, family_history, medical_history, surgical_history, psychosocial_history, financial_history, discharge_summary, consultation_note, other. Payload schemas: clinical note, unstructured report.

Pipeline

Upload binary → commit → textize / OCR → emit event log → patient state / views

Upload — request a presigned URL, PUT the file bytes (with matching Content-Type), then commit.
Textize — for born-digital PDFs (Word→PDF, EHR print-to-PDF), Olira prefers the embedded text layer when quality checks pass (chars/page, letter ratio, nonempty pages).
Vision OCR — scanned PDFs, sparse text layers, and images fall back to vision OCR.
Emit — Olira writes an unstructured_report or clinical_note event log and continues through the normal graph / extraction path.

There is no human confirmation on the live document path. Commit starts OCR immediately.

Two ways to send resources

Live upload (single document)

Best for ongoing document arrival from your backend. Requires sdk:event-log.

from datetime import datetime, timezone
from olira import OliraClient

client = OliraClient(api_key="YOUR_API_KEY")

handle = client.upload_document(
    patient_id="8a4fde23-0f1b-4c2a-9d7e-b36c1a5f0e82",
    path="progress-note.pdf",
    log_type="unstructured_report",
    document_type="clinical_note",
    timestamp=datetime(2026, 7, 15, 14, 30, tzinfo=timezone.utc),
    idempotency_key="emr-note-88421",
)
doc = handle.wait()  # or handle.poll()
print(doc.status, doc.event_log_id, doc.ocr_page_count)

using Olira;

using var client = new OliraClient(apiKey: "YOUR_API_KEY");

var handle = client.UploadDocument(
    patientId: "8a4fde23-0f1b-4c2a-9d7e-b36c1a5f0e82",
    path: "progress-note.pdf",
    logType: DocumentLogType.UnstructuredReport,
    documentType: "clinical_note",
    timestamp: new DateTimeOffset(2026, 7, 15, 14, 30, 0, TimeSpan.Zero),
    idempotencyKey: "emr-note-88421");
var doc = handle.Wait();  // or handle.Poll()
Console.WriteLine($"{doc.Status} {doc.EventLogId} {doc.OcrPageCount}");

For a clinical-note target:

handle = client.upload_document(
    patient_id="8a4fde23-0f1b-4c2a-9d7e-b36c1a5f0e82",
    path="h-and-p.pdf",
    log_type="clinical_note",
    note_type="history_and_physical",
    source="ehr_integration",
    timestamp=datetime(2026, 7, 15, 14, 30, tzinfo=timezone.utc),
    idempotency_key="emr-note-88422",
)

handle = client.UploadDocument(
    patientId: "8a4fde23-0f1b-4c2a-9d7e-b36c1a5f0e82",
    path: "h-and-p.pdf",
    logType: DocumentLogType.ClinicalNote,
    noteType: "history_and_physical",
    source: "ehr_integration",
    timestamp: new DateTimeOffset(2026, 7, 15, 14, 30, 0, TimeSpan.Zero),
    idempotencyKey: "emr-note-88422");

Under the hood the SDK calls:

POST /v1/documents:upload-url
Presigned PUT of the binary
POST /v1/documents/{id}:commit
GET /v1/documents/{id} while you poll

Method reference: upload_document, get_document.

Historical package (bulk PDFs + logs)

Best for onboarding backfills that mix JSONL-style patient/log rows with PDF binaries. Requires sdk:historical-ingest.

from olira import OliraClient
from olira.models import IngestDocument, IngestRecord, IngestLogSpec

client = OliraClient(api_key="YOUR_API_KEY")

job = client.create_ingestion_job(
    records=[
        IngestRecord.log(
            IngestLogSpec(
                event_type="symptom_report",
                patient_id="MRN-104",
                timestamp="2026-03-01T09:00:00Z",
                payload={"instrument": "esas_r", "symptoms": [{"name": "fatigue", "score": 4}]},
            )
        ),
    ],
    documents=[
        IngestDocument(
            path="discharge-summary.pdf",
            patient_id="MRN-104",
            log_type="unstructured_report",
            document_type="discharge_summary",
            timestamp="2026-03-01T10:00:00Z",
            idempotency_key="MRN-104:doc:discharge-1",
        ),
    ],
    idempotency_key="onboarding-docs-2026-q1",
    require_confirmation=True,
)

The SDK allocates upload URLs (POST /v1/ingestion/jobs:begin), PUTs each PDF, builds a manifest.jsonl that includes type=document rows, then creates the historical job. After you confirm, OCR and document log materialization run inside the historical workflow before view backfill. See create_ingestion_job and Backfilling historical data for confirmation, cancel, and progress polling.

Document statuses

Status	Meaning
`pending_upload`	Upload URL issued; binary not yet verified
`uploaded`	Object landed (intermediate)
`ocr_running`	Textize / OCR in progress
`ocr_complete`	Text extracted; event log emit in progress (non-terminal)
`log_emitted`	Event log written (`event_log_id` set) — success
`ocr_failed`	OCR or emit failed (`error` may explain)

Terminal statuses: log_emitted, ocr_failed.

What you need

API key with sdk:event-log (live upload) or sdk:historical-ingest (package jobs).
Patient already in your org (or created in the same historical package via patient rows).
A PDF or supported image file, plus the correct log_type labels.
A stable idempotency_key so retries do not create duplicate document resources. This is a separate key from the one on log_batch / log_fhir (log() has no key) — document uploads have their own idempotency scope.

After emit

Once status is log_emitted, treat the result like any other log: inspect it in Console observability, read it via state / log query APIs, and expect downstream extraction and view updates according to your org configuration. Re-using the same idempotency_key after a document already left pending_upload returns a conflict — pick a new key for a new file.

Get started

Send data

Read data

Outbound actions

Console

Resource ingestion

Supported materials

Target log types

Pipeline

Two ways to send resources

Live upload (single document)

Historical package (bulk PDFs + logs)

Document statuses

What you need

After emit

​Supported materials

​Target log types

​Pipeline

​Two ways to send resources

​Live upload (single document)

​Historical package (bulk PDFs + logs)

​Document statuses

​What you need

​After emit

Supported materials

Target log types

Pipeline

Two ways to send resources

Live upload (single document)

Historical package (bulk PDFs + logs)

Document statuses

What you need

After emit