What Data for This Category Looks Like
This category of data primarily comes from image samples uploaded to annotation platforms, preset category dictionaries, spot check results, and rework lists from annotation teams. Daily produced documents include annotation specifications, bounding box examples, quality inspection reports, and rework lists. Data is updated daily or synchronized with each batch of annotation tasks. Data fields cover bounding box coordinates, category labels, confidence values for visual targets, as well as operation records from annotation and quality inspection links. The overall workflow focuses on position and category determination for visual targets, differing from the semantic processing logic of text annotation projects.
What Constraints Do These Data Characteristics Impose on Forms and Interactions
The data characteristics of this category impose multiple constraints on the forms and interactions workflow. Capabilities including multi-source data import, real-time verification, batch submission, caching logic, role permission configuration, and cross-form linked filling must be implemented by external business systems or subsequent nodes. Visual interactive input for fields related to visual targets such as bounding boxes and coordinates must be completed using external visualization components, differing from plain text forms. FastGPT’s formInput and userSelect nodes are only responsible for collecting user input information and passing it to subsequent nodes.
How to Configure
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Form Input Node formInput | Test example: Select 3 fields from the image annotation project as required inputs; actual fields are determined by the task | This node collects user input; missing required items require checking interaction status to prevent subsequent nodes from receiving incomplete input |
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 image annotation projects must be implemented by subsequent APIs or external systems |
| Field Passing After Submission | Test example: Prepare 10 input samples for image annotation projects, 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 by actual tasks |
| External Implementation: Maximum Data Per Submission | Must be implemented separately by external systems; to be tested at 100 items per request | Matches the conventional scale of each batch of annotation tasks, avoids single request timeouts, and is determined by external request carrying capacity |
| External Implementation: Form Cache Duration | Must be implemented separately by external systems; to be tested | Related caching logic must be implemented by external business systems or subsequent nodes, adapting to the data update rhythm of annotation tasks |
| External Implementation: Visual Input Component Size | Must be implemented separately by external systems; to be tested | Related component sizes must be configured via external visualization components, adapting to the display ratio of conventional annotation screens |
Three Common Configuration Errors
- Symptom: Empty result files returned after form submission. Troubleshooting steps: Check the connectivity status of the external file upload API, confirm that annotation data files have been correctly passed to subsequent nodes.
- Symptom: Request timeout triggered during batch submission. Troubleshooting steps: Check whether the single submission data volume meets the request carrying limit of the external system, adjust to the range to be tested.
- Symptom: Forms in the quality inspection link cannot automatically associate bounding box example data. Troubleshooting steps: Check whether the external system has configured cross-form association logic, confirm that original images from the annotation platform and bounding box data have been correctly associated.
How to Verify Proper Configuration
- Check whether external visualization components load normally, confirm that the bounding box input function works correctly.
- Submit a small amount of test data, verify whether the returned results include corresponding annotation fields, and confirm that external file upload and parsing logic works normally.
- Switch between different role accounts, check whether external role permission configurations take effect, confirm that operable fields align with role divisions.
- Submit test data exceeding the conventional batch scale, verify that the external request restriction logic triggers reasonable submission restriction prompts.
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.