What This Category of Data Looks Like
Image annotation projects draw daily data from image samples, annotation platform operation logs, category dictionaries, and spot check results. Data is updated daily or per batch. Daily documentation includes annotation specifications, bounding box examples, quality inspection reports, and rework lists. Annotation specifications cover category definitions and bounding box drawing rules. Bounding box examples include sample IDs and coordinate parameters. Quality inspection reports track spot check pass rates and rework rates. Rework lists document problem points and rectification requirements. Most data is structured visually linked content, with no generic plain text content.
Constraints Imposed by Data Characteristics on Knowledge Base Retrieval and Recall
The data characteristics of image annotation projects impose clear constraints on the retrieval and recall link. First, frequently updated data sources require the retrieval pipeline to support fast incremental synchronization. This prevents data lag from disrupting annotation and quality inspection workflows. Second, data includes structured business fields such as bounding box coordinates and sample IDs. Retrieval configurations must support precise matching of specific fields, and filter irrelevant cross-project content. Third, professional annotation rules and visual bounding box examples require recall results to align closely with specific scenarios such as bounding box definition and occluded sample judgment. This improves retrieval targeting and reduces rework risk.
Configuration Settings
This configuration applies to FastGPT v4.17.0. Specific parameter settings are listed below:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches professional documentation such as annotation specifications and bounding box examples. This range filters low-relevance results and avoids invalid recall disrupting annotation workflows |
limit | Top 3–5 results | Image annotation documentation focuses on specific scenarios per entry. A small number of results covers the core needs of annotation and quality inspection |
searchMode | vector | Adapts to structured documents with coordinates and visual descriptions. Vector retrieval accurately links visually related annotation rules |
usingReRank | Enabled | For specific scenarios such as occluded sample judgment and bounding box definition, re-ranking increases the ranking weight of highly relevant documents |
datasetSearchUsingExtensionQuery | Enabled | Supplements extended queries for annotation terminology, and matches professional term associations in category dictionaries and rework lists |
collectionIds | Bind dedicated knowledge base collections for annotation teams | Isolates annotation data across different projects, and prevents irrelevant cross-project content from interfering with retrieval |
Three Common Configuration Errors
- Symptom: API call workflows return empty values. Cause: No dedicated annotation project knowledge base collection is bound to
collectionIds, or a FastGPT version below v4.8.10 is used. - Symptom:
quote type erroroccurs during retrieval. Cause: The passed retrieval variable does not match the format requirements of structured fields in the document. For example, the numerical type of bounding box coordinates is not passed correctly. - Symptom: Retrieval results do not include annotation-related content obtained via external searches. Cause: External search results are not synchronized to the dedicated knowledge base collection, or
datasetSearchUsingExtensionQueryis not enabled to associate external query content.
How to Confirm Successful Configuration
- Check if
collectionIdsis bound to the dedicated knowledge base collection for the annotation project. Confirm that no irrelevant cross-project data is included in the retrieval scope. - Initiate a test retrieval. Input "occluded sample bounding box determination rules". Verify that returned results include documentation related to annotation specifications or bounding box examples.
- After enabling
usingReRank, compare result rankings before and after re-ranking. Confirm that highly relevant content for occluded sample processing ranks higher. - Pass a variable with coordinate format to initiate retrieval. Confirm no
quote type erroroccurs, and returned results match the business scenario corresponding to the variable.
The values provided are common starting points, and should be evaluated against samples corresponding to the specific use case.
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.