What the Data for This Category Looks Like
Text annotation project data mainly comes from customer-provided corpora, internal annotation platforms, custom term lists, and quality inspection sampling results. Daily associated documents include annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Data updates follow a daily or per-batch rhythm. Core fields include annotated text, entity tags, semantic boundary descriptions, and quality inspection results. The most time-consuming links in the business process focus on tag boundary explanations, difficult sample arbitration, and consistency spot checks. Data anomalies directly lead to model training deviations, rework, or delivery delays. Compared with image annotation projects, this category places greater emphasis on content related to semantic boundaries, entity recognition, and intent classification.
Constraints Imposed on HTTP Interfaces and External Systems
The data characteristics of text annotation projects create multi-dimensional constraints for HTTP interface and external system configuration. Interfaces must support pulling sample packages in batches and synchronizing quality inspection reports, and validate formats for multi-source data. Interfaces must support dynamic adjustment of pull cycles to align with daily or batch update rhythms. Interfaces must return standardized tag explanation fields to help annotators understand rules, as core time-consuming links involve tag boundary explanations. Interfaces must provide detailed error feedback fields to quickly locate annotation deviation issues, due to the severe consequences of data anomalies. Interfaces must support multi-dimensional annotation parameters such as entities and intents, as semantic annotation fields have higher complexity. This differs from the pixel-level parameters used in image annotation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {API_KEY}, fixed prefix plus 64-bit random secret key | Complies with the interface authentication specification of FastGPT v4.17.0 to ensure data call security |
variables | Pass two custom variables: sample batch ID and quality inspection threshold | Adapts to the business requirements of batch sample processing and binding quality inspection rules for text annotation projects |
responseData | Return four core fields: annotated text, entity tags, semantic boundary descriptions, and quality inspection results | Matches the core data structure of text annotation projects and reduces subsequent data cleaning costs |
| Request timeout | 300–600 seconds | Text annotation sample processing usually includes multiple rounds of verification, requiring sufficient interface response time |
| Sample pull limit | First 200 entries | Pulling too many samples at one time increases network transmission load, adapting to the daily processing scale of annotation teams |
Three Common Misconfigurations
- Phenomenon: When calling the workflow interface, execution process logs for the MCP tool cannot be retrieved, and the returned result only includes the final annotation result. Cause: Log tracking configuration was not enabled in the request parameters, and the tool call link field was not specified for return via
responseData. - Phenomenon: When configuring a local large model interface, calls with identical parameters return results that differ significantly from those from the online API. Cause: Parameters were not adjusted for the semantic verification scenario of text annotation, and default configurations for general dialogue scenarios were used directly.
- Phenomenon: A timeout error occurs when calling the API to upload annotation samples. Cause: The size of the sample package uploaded at one time exceeds the interface limit, and the one-time transmission scale was not adjusted according to the sample pull limit configuration.
How to Confirm Proper Configuration
- Initiate a single sample pull request and verify that the returned fields include preset core content such as annotated text and entity tags.
- Call the authentication interface and confirm that the returned status code is 200 and there are no permission-related errors.
- Trigger an annotation quality inspection synchronization request and verify that the interface can correctly receive and return quality inspection results.
- View the interface logs and confirm that the
variablesandresponseDataconfigurations in the request parameters have taken effect correctly.
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.