What data for this category looks like
Image annotation project data primarily comes from image samples uploaded to annotation platforms, category dictionaries, annotation process records, and sampling inspection results. Daily produced documents include annotation specifications, bounding box examples, quality inspection reports, and rework lists. Data updates follow a daily or per-batch annotation task cycle, synced with annotation progress. Data fields cover image identifiers, bounding box coordinates, category labels, quality inspection results, rework descriptions, and more. Common units include pixels, category numbers, and task batch numbers. These fields are strongly correlated with visual target position and category attributes, differing from the text content processing logic of text annotation projects.
Constraints imposed by these characteristics on conversation logs and auditing
Visual attribute data for image annotation projects requires conversation logs to synchronously record visual context such as bounding box coordinates and category labels. The daily or per-batch update rhythm requires audit logs to be archived by task batch or date, enabling full-process traceback by batch. Multi-stakeholder interaction scenarios involving annotation, quality inspection, and rework require logs to associate all interaction traces from annotators, quality inspectors, and algorithm engineers, preventing context breaks. The presence of special fields such as bounding boxes and category labels requires audit logs to accurately track the change trajectory of corresponding fields, preventing confusion between visual attribute and text attribute records. When a workflow embeds a text content extraction node to process annotation metadata, logs must simultaneously retain visual sample association information and extraction results to ensure complete traceback.
How to configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Number of carried chat records | First 6 entries | Annotation task interactions mostly revolve around annotation and quality inspection communication for a single batch of samples. 6 entries cover core context while avoiding redundancy |
detail log switch | Enabled | Visual details such as bounding box coordinates and category changes must be retained. detail mode fully records all fields returned by APIs |
chatId binding rule | Bound to annotation task batches | Annotation tasks progress in batches. Binding chatId to batches allows quick traceback of all interaction logs for the same batch |
responseChatItemId associated node | Bound to text extraction nodes in the workflow | This node often processes annotation metadata extraction. Association allows locating abnormal extraction links |
| Log timeout threshold | 600 seconds | Annotation task interactions may include metadata transmission for large images. 600 seconds covers conventional transmission and processing durations, and can be adjusted based on actual measurements |
Three common errors
- Phenomenon: A code execution node within a workflow fails validation and cannot start when supplied with historical records. Cause:
chatIdand workflow node binding rules are not configured correctly, leading to conflicting context association logic for historical records. - Phenomenon: After setting 6 carried chat records for a text content extraction node, empty results are occasionally returned, and an error occurs after a 10-second freeze. Cause: The log timeout threshold is not adjusted. Metadata transmission and processing for large images exceed default durations, triggering a timeout error.
- Phenomenon: Visual field information such as bounding box coordinates does not appear in audit logs. Cause: The
detaillog switch is not enabled. Only basic text interactions are recorded, and key visual data is omitted.
How to confirm correct configuration
- Navigate to the workflow debugging page of FastGPT v4.17.0, trigger the text content extraction node to run, and check if the log panel includes the
chatIdidentifier for the annotation task batch. - Trigger a complete annotation and quality inspection interaction process, and check if audit logs retain visual-related fields such as bounding box coordinates and category labels.
- Simulate a scenario where a code execution node input includes historical records, confirm that the validation logic passes normally with no error prompts.
- Adjust the number of carried chat records to a value other than 6, verify if the number of context entries in the logs changes synchronously to match the configured expectation.
The values provided are common starting points, and should be validated against applicable deployment 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.