What data for this category looks like
Image annotation project data primarily comes from image samples, category dictionaries, inspection results, and rework lists produced by annotation platforms. Daily maintained documents include four types: annotation specifications, bounding box examples, quality inspection reports, and rework lists. Fields cover target categories, bounding box coordinates, inspection judgment results, rework requirements, and more. Data updates follow a daily or single-batch annotation task schedule, synced with annotation progress. Data focuses on the position and category of visual targets. Unlike semantic content processing for text annotations, the core focus is on spatial localization and category recognition of entities within images.
What constraints these characteristics impose on the reranking and hybrid retrieval link
Image annotation data mixes visual features and text metadata. This requires hybrid retrieval to support both vector-embedded visual features and text fields, avoiding the limitations of single retrieval modes. Data updated daily or in single batches needs to adapt to retrieval latency constraints for high-frequency index refreshes, preventing inconsistent retrieval results caused by index updates. Fields in documents such as annotation specifications and rework lists have large differences. Targeted retrieval matching weight configuration is needed to ensure key information such as bounding box coordinates and rework requirements is prioritized for matching. Structured data such as bounding box coordinates of visual targets must be retrieved combined with text descriptions, preventing omission of key annotation information related to position. Inspection results in quality inspection reports are linked to annotation quality. Retrieval must prioritize matching high-reliability historical data to reduce rework risks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Rerank Toggle | Enabled | Image annotation data mixes visual and text features. Reranking filters low-relevance recall results, improving the accuracy of annotation-related retrieval |
| Similarity Threshold | 0.72–0.85 | Adapts to the similarity calculation range for mixed visual features and text metadata, preventing mismatching of bounding box coordinates with irrelevant content |
| Maximum Return Count per Request | Top 10–15 entries | Matches the sample size range of single-batch annotation tasks, controlling processing time for retrieval results |
| API Timeout Duration | 300–600 seconds | Adapts to the loading and inference time of reranking models, preventing retrieval failures caused by timeouts |
| Retrieval Mode | Hybrid Retrieval | Supports visual embeddings from image samples and text metadata from annotation documents, enabling hybrid retrieval of cross-type content |
| Rerank Model Version | Compatible version for FastGPT v4.17.0 | Prevents reranking request errors caused by version mismatches |
Three Common Misconfigurations
- Symptom: After enabling the rerank toggle, no reranked results appear in online recall tests. Cause: The rerank association configuration was not enabled during the knowledge base indexing step, or the deployment path of the rerank model was not correctly bound.
- Symptom: Local rerank model requests return errors. Cause: The configured rerank model version does not meet FastGPT compatibility requirements, or insufficient memory quota was allocated during model loading.
- Symptom: Calling the Ollama rerank model displays Invalid URL (POST /v1/rerank). Cause: The API endpoint path of the rerank model was not correctly configured, or third-party API forwarding rules did not cover the rerank interface.
How to Confirm Configuration is Complete
- Navigate to the knowledge base retrieval test page, enter query terms related to annotation specifications or rework lists, and check if returned results prioritize highly relevant bounding box examples or quality inspection reports in their ranking.
- Check the connection status of the rerank model, confirm that the interface shows the model status as running normally, with no connection timeout or authentication failure prompts.
- Adjust the range of the similarity threshold, verify that the number of matching retrieval results changes as expected with the threshold adjustment.
- View retrieval logs, confirm that rerank interface call requests and returned data include mixed matching results of visual features and text fields.
The values provided are common starting points, and should be evaluated against samples used in the actual deployment.
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.