What this category of data looks like
Text annotation project data typically comes from customer corpora, annotation platforms, vocabularies, and quality inspection sampling results. Updates occur daily or per annotation batch. Daily associated documents include annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Core fields cover annotated text content, entity tags, intent classification results, quality inspection markers, and more. This category focuses on semantic boundaries, entity and intent recognition, and differs from image annotation projects. The most time-consuming steps are label boundary explanation, difficult sample arbitration, and consistency spot checks. Data anomalies may lead to model training bias, rework, or delivery delays.
What constraints these characteristics impose on forms and interactions
Text annotation project data has scattered sources. FastGPT form input nodes can receive batch data. Multi-channel batch import and real-time field verification logic are implemented by external business systems. Update frequency is daily or per batch, requiring adaptation to the interaction logic of batch submission and incremental updates from external business systems. Associated document types are diverse. FastGPT form and user selection nodes only transmit information such as annotation specifications and quality inspection rules to external business systems. Multi-dimensional verification logic and exclusive field entry matching are implemented by external business systems. Focusing on the business characteristics of semantic boundaries, exclusive interaction controls for entity marking and boundary calibration are provided by external business systems. FastGPT form and user selection nodes only transmit relevant configuration information. For difficult sample arbitration and consistency spot check links, external business systems need to configure pre-submission form verification rules to restrict submission of incomplete fields and prevent abnormal data from flowing into subsequent processes. FastGPT nodes only transmit field submission status.
How to configure the settings
This configuration applies to FastGPT v4.17.0
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
Form input node formInput | Test example: Select 3 fields from the text annotation project as required inputs; actual fields are determined per task | This node collects user input; missing required items require checking interaction status to prevent subsequent nodes from obtaining incomplete inputs |
User selection node userSelect | Test example: Set 2 business options, check corresponding branches one by one | This node handles user selection; querying and filtering records related to text annotation projects must be implemented by subsequent interfaces or external systems |
| Field transmission after submission | Test example: Prepare 10 text annotation project input samples, covering complete filling, missing required items, and cancellation operations | Compare input and node output to verify whether subsequent nodes receive corresponding fields; result thresholds are determined per actual task |
| External implementation item: Batch submission threshold (handled by external system) | Must be implemented separately by the external system; test example value: No more than 500 entries submitted per single request | Adapt to the batch update data test rhythm, verify the concurrency processing effect of the external system |
| External implementation item: Submission process timeout threshold (handled by external system) | Must be implemented separately by the external system; test example value: 60 seconds | Match the test response expectations of daily operations of annotation teams, verify the timeout processing logic of the external system |
| External implementation item: Form required field configuration | Must be implemented separately by the external system; required field rules are configured by the external business system | Cover core business fields to prevent invalid data from flowing into subsequent links; FastGPT nodes only transmit field submission status |
Three common mistakes
- Phenomenon: Subsequent processes are triggered without completing form required field filling, resulting in empty field errors. Cause: Pre-submission required item verification rules are not configured in the external business system.
- Phenomenon: The tag selection control provided by the external business system keeps refreshing, and preset tags cannot be stably selected. Verifiable action: Check the interface request frequency limit and local state management configuration of the external business system.
- Phenomenon: The external business system fails to properly parse the imported annotation sample package, prompting a content format error. Cause: The field structure of the external business system and the sample package are not aligned, resulting in mismatched data mapping.
How to confirm the configuration is complete
- Submit a test data entry containing all required fields, confirm that FastGPT nodes can normally transmit data to the external business system and complete the submission process.
- Attempt to submit test data without filling required fields, confirm that the external business system will pop up corresponding prompts and block submission.
- Submit test data within the batch submission test example value, confirm that the external business system process has no timeout or interruption issues.
- Switch the preset tag library, confirm that the tag selection control of the external business system only displays currently configured tag options.
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.