What the Data for This Category Looks Like
Core data for data cleaning and quality inspection comes from four types of sources: original datasets, ETL logs, annotation platforms, and sampling results. Update frequency is daily or synced per batch task.
Daily output documents include cleaning rules, quality inspection sampling tables, abnormal data lists, and delivery reports.
- Cleaning rules contain fields for rule identification, effective period, and processing logic.
- Abnormal data lists contain fields for unique data identification, abnormal type, and processing status.
- Quality inspection sampling tables contain fields for sampling batch, sampling rate, and pass rate.
All data is stored in association with a single batch of cleaning and quality inspection tasks.
What Constraints Do These Characteristics Impose on the Citation Sources and Traceability Link
Data sources include multiple types of heterogeneous logs and reports. The traceability link must associate both original data and processing process records. This prevents displaying only information from a single stage.
The daily or per-batch update rhythm requires traceability to support incremental association and full retrospective queries. This fits different batches of cleaning tasks.
Structural differences across multiple document types require traceability to match corresponding fields by document type. For example, extract processing logic from cleaning rules. Extract repair records from abnormal data lists. A unified field mapping logic cannot be used.
The risk of data unavailability or distorted indicators means the traceability link must fully display the full data flow link information.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 600–1000 token | Matches the average token length of cleaning rules and abnormal data lists, avoids context overflow |
responseData | Enabled | Used to return specific content of original datasets and processing logs associated with traceability |
detail | Full | Used to display specific fields of abnormal data, processing steps, and quality inspection sampling results |
| Single traceability data source limit | Top 4 | Adapts to scenarios where data sources include up to four types, such as ETL logs and annotation platforms |
| Traceability update frequency | Daily sync | Matches the daily or per-batch update rhythm of daily data |
Three Common Misconfigurations
- Symptom: Returned results show no traceability information, with corresponding fields empty. Cause: The
responseDataconfiguration item is not enabled, and the data source for cleaning rules or abnormal data lists is not associated. - Symptom: Response content exceeds the scope of knowledge base question-answer pairs, with generated content not from the original text. Cause: The
detailconfiguration is not set to full, or content is not limited to only citing cleaning and quality inspection-related documents, causing the model to call irrelevant data. - Symptom: The service fails to respond for a long time or becomes stuck after startup. Cause: The value of
quoteMaxTokenis too large, causing context loading timeout, or the number of single-traceability data sources is not limited, loading too much redundant data.
How to Verify Correct Configuration
- Enter the knowledge base configuration page of FastGPT v4.17.0. Check the data source list for traceability association. Confirm it includes four types of sources: original datasets, ETL logs, annotation platforms, and sampling results.
- Initiate a test query. Check if the returned results include specific field information of corresponding documents, such as the processing logic of cleaning rules or repair records of abnormal data.
- Check system operation logs. Confirm there are no errors such as context loading timeout, data source loading failure, or field matching errors.
- Adjust the value of
quoteMaxToken. Verify that the length of returned traceability content changes synchronously with the configuration, as expected.
The values provided here 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.