This document was written for FastGPT v4.17.0.
What data for this use case looks like
Image annotation project data typically comes from image samples, category dictionaries, quality inspection reports, and rework lists produced by annotation platforms. Update frequency is daily or synced per annotation batch. Daily associated documents include annotation specifications, bounding box examples, random inspection result records, and similar materials. Data fields include pixel coordinates of target bounding boxes, annotation categories, rework markers, and other information centered around the position and category of visual targets. This data supports the annotation stage of computer vision model training.
What constraints these characteristics impose on multi-turn dialogue and prompt workflows
The data for image annotation projects centers on the position and category of visual targets, and is updated per batch or daily. Multi-turn dialogues must accurately match the category dictionary and rework list for the current annotation batch to avoid mixing data across batches. Visual data fields include non-text information such as pixel coordinates, so prompts must clearly specify data formats and retrieval scopes to ensure content returned by dialogues complies with annotation specifications. The frequent update rhythm requires dialogue contexts to load the latest quality inspection reports and rework records in real time, to ensure information timeliness during the review process.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current annotation batch | Distinguish dialogue contexts for different annotation batches to avoid mixing cross-batch data |
messages | Retain the most recent 20 dialogues and 3 latest annotation documents | Control context length to adapt to the batch update rhythm of annotation data |
variables | Bind the category dictionary and rework list ID for the current batch | Allow dialogues to automatically load exclusive data for the current project, reducing manual input |
| Maximum context length | 8000–12000 characters | Cover the content volume of single-batch annotation documents and multi-turn review dialogues |
| Dialogue history reset timing | Triggered after each annotation batch is completed | Adapt to the batch-based business rhythm of annotation projects, avoiding redundant data residue |
| Prompt template binding scope | Only bind annotation specifications for the current project | Ensure review dialogues always follow the annotation rules of the current project |
Three common configuration mistakes
- Phenomenon: Dialogue response time exceeds expectations, and the interface displays a loading delay prompt. Cause: The context loading scope is not restricted, introducing excessive cross-batch annotation data that does not belong to the current project.
- Phenomenon:
Unexpected end of JSON inputerror appears in dialogues, which occurs frequently when using the ChatGLM2 model. Cause: Thevariablesparameter is not correctly bound, causing the dialogue to attempt to load category dictionary or rework list data with incorrect formats. - Phenomenon: Dialogue results are only displayed in the sidebar and cannot be directly embedded in the main dialog window; each new dialogue requires re-binding project data. Cause: The current batch identifier is not bound via
chatId, so the system generates a new dialogue session by default and does not reuse the context of the current project.
How to confirm configurations are correctly set
- Initiate a test dialogue, enter the annotation category name for the current batch, and check whether the returned content includes the annotation specification requirements for that category.
- Switch to another annotation batch, and check whether the dialogue context automatically updates to the documents and data for that batch.
- Initiate multiple consecutive dialogues, confirm that each reply is based on the review content of the previous round, and no context loss occurs.
- Check the system logs to confirm that no format error-related prompt messages appear.
The values provided here 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.