What data in this category looks like
Core data for image annotation projects comes from image samples, category dictionaries, inspection results, and rework lists produced by annotation platforms. Daily associated documents include annotation specifications, bounding box examples, quality inspection reports, and more. Data updates follow a daily or single-batch annotation task cycle, with real-time updates tied to new annotation tasks and inspection results. Document structures typically include fields such as visual target category definitions, bounding box annotation rules, non-conforming item details, and rework requirements. Some fields are tied to attributes like image pixel counts and batch numbers, with no unified fixed units.
What constraints these characteristics impose on knowledge base retrieval and recall
Data and associated documents for image annotation projects mostly include visual annotation rules, concrete examples, and unstructured quality inspection details. This creates multiple constraints for knowledge base retrieval and recall. Data is updated daily per single-batch annotation tasks, so recall scope must support filtering by batch to avoid including expired annotation rules. A large number of visual terms and concrete requirements such as bounding box and occlusion judgment exist in documents. Retrieval must prioritize matching the context of annotation scenarios. At the same time, most fields in quality inspection reports and rework lists are unstructured details. Precise recall of unstructured text must be supported to ensure that operations on-duty and internal work order Q&A can quickly locate solutions to corresponding annotation issues.
How to set configurations
The following configurations are based on FastGPT v4.17.0, adapted for the knowledge base retrieval scenario of image annotation projects:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Match professional terms and rules in annotation scenarios, filter low-relevance general content |
limit | Top 8 entries | Adapt to the frequency of annotation task questions, avoid excessive results interfering with quick judgments by on-duty operations staff |
searchMode | Semantic search | Adapt to matching needs for visual terms and concrete examples, outperforms pure keyword retrieval |
usingReRank | Enabled | Reorder recall results by annotation scenario relevance, improve recall accuracy for quality inspection and rework-related questions |
datasetSearchUsingExtensionQuery | Enabled | Generate extended queries based on annotation specifications and bounding box examples, cover more retrieval needs in professional scenarios |
collectionIds | Bind dedicated knowledge base collections for annotation teams | Isolate annotation data across batches, avoid invalid recall across batches |
Three common misconfigurations
- Failure to support real-time switching of knowledge base collections during conversations. The symptom is that operations staff cannot switch to the knowledge base for the current batch of annotation data during conversations. The cause is that the logic for dynamically binding
collectionIdsis not configured, and only a fixed knowledge base collection is bound. - Poor recognition of tables and images in Word documents in the knowledge base. The symptom is that matching tables or image content in corresponding documents fails when retrieving bounding box examples. The cause is that multi-modal retrieval adaptation is not enabled, or image documents are not preprocessed and split.
- Error prompt indicating model is inaccessible when calling the model. The symptom is that the interface prompts that
Deep Search-r1:7bis inaccessible. The cause is that access permissions for the corresponding model are not configured, or the model parameters bound to the knowledge base are incorrect.
How to confirm the configuration is complete
- Run a retrieval targeting the current batch's annotation specifications, verify that recall results only include annotation-related content with no irrelevant general documents.
- Upload an annotation example document containing tables and images, then run a retrieval targeting bounding box determination rules, confirm that tables and image content in the document are correctly recognized and recalled.
- View the recommended questions list on the work order Q&A interface, confirm that all recommended questions come from common annotation scenario questions in the knowledge base.
- Trigger knowledge base retrieval, check whether returned results are sorted according to annotation scenario priorities, such as quality inspection and rework-related questions appearing in top positions.
The values provided 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.