What Data for This Category Looks Like
Daily documents for this category include annotation specifications, bounding box examples, quality inspection reports, and rework lists. Data sources include image samples, annotation platforms, category dictionaries, and spot check results. Updates occur daily or per batch. Typical users are annotation teams, computer vision algorithm teams, and quality inspectors. Core processing targets the position and category information of visual targets, distinguishing this category from text annotation projects. The most time-consuming steps are target bounding box definition, occluded sample judgment, and rework communication during quality inspection. Processing errors can lead to recognition errors, increased rework rates, or customer rejection.
Constraints Imposed on Context and Token
Bulk data imports and multi-document reference needs for image annotation projects require the context to store multiple batches of annotation specifications, historical bounding box examples, and similar content. Therefore, the total context token count must be limited to avoid overflow. Daily documents such as category dictionaries and quality inspection reports are updated daily or per batch. The context must support dynamic loading of the latest version of reference content, while controlling the token length of single referenced content to prevent interference with core annotation logic. The token usage of rework lists and historical annotation results is much higher than that of text data. Context blocks must be split and a reasonable reference limit set to avoid LLM response abnormalities caused by context overload.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Maximum total context token count | 8000–12000 token | Adapts to the total token usage of multi-batch documents and visual data for image annotation projects, avoiding context overflow |
| Maximum token count per single reference | 1500–2000 token | Matches the length of single documents such as bounding box examples and quality inspection reports, preventing overload from a single referenced content |
| Context refresh trigger | Per project batch | Matches the daily or per-batch data update frequency, ensuring referenced annotation specifications and category dictionaries are the latest version |
| Maximum context reference count | Top 5 entries | Limits the number of loaded historical rework lists and spot check results, reducing total context token consumption |
| Context chunk overlap length | 200–300 token | Ensures continuity of visual reference content, avoiding loss of critical annotation rules after chunk splitting |
Three Common Misconfigurations
- Symptom: LLM returns empty content, and the interface displays
LLM model response empty. Cause: The total context token exceeds the set maximum limit, preventing the LLM from loading sufficient annotation reference content and failing to generate a valid response. - Symptom: The referenced annotation specification in the context does not match the current batch. Cause: Context refresh is not triggered per project batch, and old versions of category dictionaries or annotation rules are still loaded.
- Symptom: Referenced content for a single bounding box example is truncated. Cause: The token count of a single reference exceeds the set limit, causing visual reference content to be truncated early and losing critical bounding box annotation information.
How to Verify Correct Configuration
- Import a batch of test image samples and corresponding annotation specifications, and check if the context-loaded document list includes the latest version of the category dictionary.
- Trigger the LLM to generate annotation suggestions, and verify that the response content references relevant reference content such as bounding box examples and historical quality inspection results.
- Switch test data from different batches, and confirm that the context content updates along with the batch.
- Check system logs to confirm that no context token overflow error prompts appear.
The values given 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.