What this category of data looks like
The data for text annotation projects primarily comes from client-provided corpora, annotation platform interaction records, specialized vocabularies, and quality inspection sampling results. Data is updated daily or synchronized per annotation batch. Daily associated documents include annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Common data fields include annotation content, annotation tags, annotator identifiers, quality inspection results, and interaction timestamps. Units are primarily characters, tag categories, and person-times. Core focus areas are annotation and quality inspection process data related to semantic boundaries, entity recognition, and intent classification.
What Constraints These Characteristics Impose on Conversation Logs and Auditing
Text annotation project data is multi-source and updated per batch. This requires the conversation logs and auditing link to support multi-source data identifier association, to prevent mixing of annotation data from different batches or sources. Daily work requires tracking interaction processes such as annotation specification calls, tag boundary explanations, and difficult sample arbitration. Therefore, logs must fully retain conversation content and operation timestamps from annotators and quality inspection personnel. Data fields focus on content related to semantics, entities, and intent. The auditing link must support quick filtering by annotation batch and time range, while retaining full link records of tag modifications. This supports consistent sampling and rework traceability.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Log storage retention period | 7–30 days | Annotation batches are typically delivered weekly or monthly, requiring coverage of audit needs for at least one full annotation cycle |
chatId association scope | Single annotation batch | Text annotation project data is updated per batch, so limiting the log association scope avoids cross-batch data mixing |
responseChatItemId logging switch | Enabled | Full retention of interaction details between annotators and quality inspectors is required to support traceback for difficult sample arbitration |
detail log sampling granularity | Full recording of interaction content | Interaction details during the annotation process are critical for troubleshooting and auditing. Full recording fully covers operations such as tag modifications and specification calls |
| Single audit query duration | 600 seconds | Prevents long queries from occupying excessive cluster resources, meeting daily query latency requirements for annotation projects |
| Audit filtering dimension limit | Top 1000 entries | Daily sampling only needs to cover recent batches. Limiting the filtering upper bound reduces query load |
Three Common Misconfigurations
- Symptom: After executing a conversation record deletion operation, associated audit logs are also deleted. Cause: Independent binding rules between
chatIdand logs are not configured. Deleting the conversation identifier also clears associated audit data. - Symptom: When querying token consumption for a specified application, it is not possible to filter out exclusive data for text annotation projects. Cause: Log sources are not associated by annotation batch, and the statistical dimension does not cover multi-source data such as client corpora and annotation platforms.
- Symptom: After setting the chat record limit to 1 entry, it is still possible to retrieve more than 1 entry of historical interaction content. Cause: The
chatIdassociation scope is not limited to a single annotation batch, and historical cross-batch data is not correctly filtered.
How to Verify Correct Configuration
- Execute a log query for single-batch annotation data, confirming that filtered results only include interaction records for the corresponding batch.
- Trigger a single conversation interaction between an annotator and a quality inspector, checking whether audit logs fully retain
detail-level interaction content. - Adjust the log retention period configuration, verifying that the system automatically cleans up expired logs in accordance with the set rules.
- Execute a batch deletion operation, confirming that audit logs are not deleted synchronously with conversation records.
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.