What data for this category looks like
Text annotation project data primarily comes from customer corpora, annotation platforms, specialized vocabularies, and quality inspection sampling results. Daily associated documents include annotation specifications, sample packages, quality inspection reports, and issue feedback forms. Data updates occur either daily or per annotation batch. Common data fields include sample text, annotation tags, quality inspection results, and processing duration. Individual samples are measured in characters, while bulk sample packages are counted by item. This category focuses on semantic boundary, entity, and intent recognition, distinct from image annotation projects which require visual feature annotation.
Constraints imposed by these characteristics on scheduled tasks and automation
Text annotation projects have scattered data sources and inconsistent update rhythms. This requires scheduled tasks to adapt to synchronization timing for multi-source data. Bulk sample packages vary widely in size. Processing too many samples at once can trigger timeouts or memory errors, so task execution scale must be limited via sharding. Label boundary clarification and difficult sample arbitration during annotation account for a large share of total time. Automated workflows must include validation logic to reduce delays from manual intervention. Data anomalies directly lead to model training bias, rework, or delivery delays. Scheduled tasks must therefore include failure retry and alert mechanisms to ensure process stability.
How to configure settings
This configuration is designed for the scheduled task module of FastGPT v4.17.0.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Task trigger cycle | 15–60 minutes | Aligns with daily or batch-based data update rhythms, avoids frequent triggering that consumes resources |
| Single-processing sample count | 200–500 items | Balances processing efficiency and memory usage, prevents timeout errors caused by bulk loading |
| Failure retry attempts | 2–3 times | Covers non-persistent anomalies such as temporary annotation platform fluctuations and network jitter |
| Alert trigger threshold | 3 consecutive task failures | Notifies operations personnel in a timely manner to resolve issues, prevents disruption to annotation progress |
| Task timeout duration | 1800 seconds | Matches the average processing time per batch of samples, prevents tasks from hanging indefinitely |
| Bulk data shard size | 800–1200 characters | Matches the typical length of individual text annotation samples, optimizes sharding processing efficiency |
Three common misconfigurations
- Symptom: The first 10+ items of a bulk annotation run normally, but subsequent items produce no results or return connection timeout errors. Cause: No single-processing sample count limit is configured. Loading too many samples at once exceeds system resource thresholds.
- Symptom: Scheduled tasks only support trigger cycles of 1 hour or longer, and shorter intervals cannot be set. Cause: The custom cycle configuration switch is not enabled. The default setting only exposes fixed hourly trigger options.
- Symptom: Fields such as question-and-answer reasoning time and annotation progress in operational reports are empty or not updated. Cause: The question-and-answer log collection switch for annotation projects is not enabled, and trigger conditions for report generation are not configured.
How to verify correct configuration
- A scheduled task is manually triggered, system task logs are reviewed, and processing records and result outputs for the corresponding annotation batch are confirmed to exist.
- A task failure scenario is simulated, the preset alert notification is verified to trigger, and the alert link is confirmed to be operational.
- The operational report module is accessed, and statistical data including annotation sample count, processing duration, and quality inspection pass rate is confirmed to have been generated.
- A test sample package is uploaded, task execution is waited to complete, and consistency between processing results and original annotation data is verified.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.