What Data for This Category Looks Like
Data for image annotation projects primarily comes from image samples produced by annotation platforms, annotation specification documents, bounding box example materials, category dictionaries, and spot check quality inspection reports. Data updates are completed daily or per single batch. Single batch data includes fields such as images to be annotated, annotated bounding box information, category labels, and rework lists. There is no unified fixed unit; data is only counted by annotation task batch or number of images. Daily circulated documents mostly include annotation operation guides and quality inspection result summary files. Data centers on the position, category, and annotation quality of visual targets.
Constraints Imposed by These Characteristics on the Reranking and Hybrid Retrieval Link
Visual data and text annotation data for image annotation projects have notable differences. Hybrid retrieval must handle image metadata, annotation text, and quality inspection reports simultaneously, so it must balance text semantic matching and visual feature correlation. Daily or single-batch updated data creates constraints for index refresh frequency. Frequent full index updates that reduce retrieval efficiency must be avoided. Documents such as annotation specifications and rework lists have large structural differences. The recall scope of hybrid retrieval must cover multiple types of data sources. The reranking link must prioritize matching high-frequency fields related to annotation tasks, such as bounding box parameters and category labels. It must also filter low-relevance redundant content to ensure retrieval results align with the needs of operations duty and ticket Q&A scenarios.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | Multiple data sources for image annotation projects require precise sorting; reranking filters low-relevance recall results |
similarity | 0.65–0.75 | Adapts to semantic matching accuracy for annotation documents, avoids recalling irrelevant non-annotation content |
limit | Top 10 entries | Matches the single query result volume for operations duty and internal ticket Q&A, balances efficiency and coverage |
searchMode | hybrid | Supports hybrid retrieval of text and annotation metadata, adapts to multiple data source types |
| Reranking model timeout threshold | 30 seconds | Adapts to retrieval response requirements for annotation data, avoids long wait times blocking ticket processing workflows |
Three Common Misconfigurations
- Phenomenon: After enabling
usingReRankand completing the knowledge base index configuration, online recall tests do not return reranked results. Cause: The reranking model is not bound in the knowledge base retrieval configuration, or the reranking model's API key is not filled correctly. - Phenomenon: The reranking model call returns the
Invalid URL (POST /v1/rerank)error. Cause: The API address configuration for the reranking model is incorrect, and the corresponding endpoint path is not matched. - Phenomenon: After the reranking configuration is completed, retrieval results are not sorted by relevance from high to low. Cause: The
similaritythreshold is not set correctly, or the hybrid retrieval data source does not cover the core fields of the annotation project.
How to Confirm Successful Configuration
- Enter the knowledge base retrieval test page of FastGPT v4.17.0, input ticket-related questions for the annotation project, and check whether the returned results carry reranking sorting identifiers.
- Check the
usingReRankswitch status on the knowledge base configuration page, confirm it is in the enabled state. - Call the dedicated test interface for the reranking model, verify that the relevance sorting of the returned results meets expectations.
- View the system retrieval logs, confirm that the reranking model's call request has been sent normally and there are no error messages.
The values provided here are common starting points and should be evaluated 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.