> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sqwish.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Review teacher labels

> Label real queries, inspect disagreements, and select the rows that become training data.

First check whether labelling is available and which teachers are offered:

```bash theme={null}
curl --fail-with-body https://console.sqwish.ai/v1/labeling/teachers \
  -H "Authorization: Bearer $D1_API_KEY"
```

Use `available`, `defaults.teacher_models` and `limits` to plan the job. A catalogue of teacher names does not mean the service is enabled.

## 1. Upload unlabelled queries

Save UTF-8 JSONL, one context per line. You may put decisions on each row, or provide shared decisions when starting the job.

```bash theme={null}
cat > queries.jsonl <<'JSONL'
{"context":"Please refund the duplicate charge."}
{"context":"Where is my parcel?"}
JSONL

curl --fail-with-body 'https://console.sqwish.ai/v1/labeling/sources?name=Refund-review' \
  -H "Authorization: Bearer $D1_API_KEY" \
  -H 'Content-Type: application/x-ndjson' \
  -H 'Idempotency-Key: refund-source-v1' --data-binary @queries.jsonl
```

Keep the returned source `id`. A source can hold up to 2,000 rows and 16 MiB. The input receipt records the uploaded bytes and hash. Teacher labelling sends selected queries to the configured teacher model provider; choose data appropriate for that workflow.

## 2. Start a job

Set `D1_SOURCE_ID` to that returned ID. Select teacher IDs from the catalogue; this example uses its current default.

```bash theme={null}
curl --fail-with-body https://console.sqwish.ai/v1/labeling/jobs \
  -H "Authorization: Bearer $D1_API_KEY" -H 'Content-Type: application/json' \
  -H 'Idempotency-Key: refund-labels-v1' \
  -d "{\"source_id\":\"$D1_SOURCE_ID\",\"teacher_models\":[\"kimi-k3\"],\"decisions\":[{
    \"id\":\"refund\",\"kind\":\"binary\",\"question\":\"Is a refund requested?\"}]}"
```

Poll `/v1/labeling/jobs/{job_id}`. Jobs progress through `queued` and `running` to `review_ready`, or end `failed` or `cancelled`. Cancellation may pass through `cancelling`. Review readiness means labels are available, not that they are correct.

## 3. Review and finalize

Read `/v1/labeling/jobs/{job_id}/results` page by page. Each result has its source index, context, decisions, teacher labels, `valid` and `agreement`. Valid means the teachers returned labels in the expected shape. Agreement means they chose the same labels. Neither substitutes for your review.

For valid rows, `targets` averages the teachers' one-hot votes. Inspect disagreements and systematic mistakes against your labelling rules. Invalid rows cannot be finalized.

Set `D1_LABEL_JOB_ID` to your job's ID. After reviewing and accepting rows 0 and 1:

```bash theme={null}
curl --fail-with-body "https://console.sqwish.ai/v1/labeling/jobs/$D1_LABEL_JOB_ID/finalize" \
  -H "Authorization: Bearer $D1_API_KEY" -H 'Content-Type: application/json' \
  -H 'Idempotency-Key: refund-reviewed-v1' -d '{"row_indices":[0,1]}'
```

Use the original row indices, not page positions. At least two selected rows and two distinct contexts are required for a dataset. Finalization returns that dataset and changes the job to `finalized`. A different later selection conflicts; create another job when the reviewed selection must change.

The resulting dataset keeps teacher and source lineage. Inspect it as you would any [dataset](/guides/datasets) before tuning or training.