What data looks like for this category
Core data for image annotation projects comes from image samples, category dictionaries, quality inspection reports, and rework lists generated by annotation platforms. Daily associated documents include annotation specifications, bounding box example instructions, and more. Data updates follow daily or per-task cycles. Fields include visual target position coordinates, category labels, spot check judgment results, and others. There are no unified fixed units; values change based on annotation task categories and sample sizes. Processing focuses on visual target bounding and occlusion judgment, which differs from the pure text processing logic of text annotation projects.
What constraints do these data characteristics impose on the vector model and indexing workflow
The data characteristics of image annotation projects impose multiple constraints on the vector model and indexing workflow. First, the data includes text content such as annotation specifications and interface documents, as well as structured annotation metadata with coordinates and categories. It must support vector encoding for both text and structured metadata. Second, data updates occur daily or per task cycle. In FastGPT v4.17.0, the incremental indexing configuration logic has been adapted for high-frequency update scenarios. Correct update trigger rules must be configured. Third, retrieval results directly affect annotation efficiency and rework rates. Recall accuracy and timeliness must be ensured to avoid errors in target bounding and occlusion judgment caused by retrieval bias.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of annotation specifications and interface documents, avoiding semantic fragmentation within chunks |
| Overlap Length | 100–200 characters | Preserves contextual connections between chunks, preventing loss of instructions related to bounding and occlusion judgment due to splitting |
| Similarity Threshold | 0.75–0.85 | Balances retrieval recall rate and precision, meeting the annotation task's need for accurate matching of examples and specifications |
| Single Search Limit | Top 5 results | Controls the scale of retrieval results, avoiding redundant information interfering with annotators' quick reference |
maxToken | 4096 | Adapts to the input length limit of general embedding models, covering the total encoding length of single-chunk text and associated metadata |
defaultToken | Fill in according to actual key length | Used to configure the default authentication token for embedding model calls, adapting to the authentication rules of transit services |
Three common misconfigurations
- Symptom: After adding the text-embedding-v3 model and configuring the API key, the interface prompts that no available channel is found. Cause: The access permission for the embedding model is not enabled in the
defaultgroup, or the transit configuration does not correctly map the model call channel. - Symptom: After enabling reranking functionality, retrieval response time exceeds expectations. Cause: The chunk length setting exceeds the adaptive range, increasing the vector encoding time for single-chunk text, or the single search limit value is too high, expanding the candidate set size.
- Symptom: A large number of document contents unrelated to the current annotation task appear in retrieval results. Cause: The similarity threshold is set too low, failing to filter low-match results, or index weights are not configured for the exclusive fields of annotation specifications and examples.
How to confirm correct configuration
- Navigate to the vector model configuration page, confirm that the target embedding model has been added and the key configuration completed, and verify that access permissions for the corresponding group are enabled.
- Initiate a test query for annotation specifications or bounding box examples, check the relevance of retrieval results, and adjust configuration items to optimize matching effects as needed.
- Import a batch of test annotation documents and metadata, check the index update status, and confirm that the incremental update function operates normally per task cycle.
- Verify the number of results returned by retrieval, confirm that it matches the configured single search limit value, and validate that the retrieval logic triggers correctly.
The values listed above 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.