This document is written for FastGPT v4.17.0.
What this category of data looks like
This category of data primarily originates from raw datasets, ETL operation logs, output results from annotation platforms, and sampling quality inspection results. Daily associated documents include cleaning rule configuration files, quality inspection sampling tables, abnormal data lists, and final delivery reports. Data is updated daily or per batch task cycle. Fields cover raw data identifiers, cleaning operation records, anomaly type tags, sampling verification results, and other related items. There are no unified fixed units; values adjust based on data source types and task requirements.
What constraints these characteristics impose on the citation sources and traceability link
The multi-source nature of raw datasets requires traceability links to associate multiple types of data source identifiers, to avoid missing traceability information. The daily or per-batch update rhythm requires traceability links to adapt to switching between incremental updates and full verification, to ensure consistent data source versions for each review. The requirement to associate cleaning rules and sampling table documents requires traceability links to bind configuration versions for corresponding tasks, to prevent traceability deviations caused by version mismatches. The requirement to associate abnormal data lists requires marking the original source and full processing flow of abnormal data in the traceability link, to meet the traceability requirements of security and compliance.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Adapts to the average length of cleaning and quality inspection documents, avoids truncating critical traceability information |
responseData | Includes raw data identifiers, processing logs | Covers core traceability fields for this category of data, meets compliance review requirements |
detail | Enable full-link details | Associates multi-source data such as ETL logs and sampling results, fully displays data processing workflows |
| Matching Threshold | 0.75–0.85 | Adapts to feature matching accuracy for abnormal data identification, reduces false positives or false negatives |
| Single Traceability Query Limit | Top 10 entries | Controls the scale of data sets returned by traceability, avoids excessive resource usage |
Three common misconfigurations
- Symptom: A traceability request returns a normal status code, but no content field is returned. Cause: The
responseDataparameter is not configured correctly, and the required fields for raw data and processing logs are not specified. - Symptom: Displayed citation sources include redundant non-essential log fragments, and the format is disorganized. Cause: The display level of the
detailparameter is not limited, and cleaning rule details beyond the scope of compliance review are incorrectly loaded. - Symptom: Garbled characters appear in traceability results. Cause: The encoding formats of multi-source data are not adapted, and the unified character set verification mechanism is not enabled in the configuration.
How to confirm correct configuration
- Manually trigger a batch cleaning task, check whether the traceability log includes raw dataset IDs, ETL operation records, and sampling results.
- Call the traceability interface, verify whether the returned fields include the
responseDataassociated content specified in the configuration. - Adjust the value of the
quoteMaxTokenparameter, verify whether traceability information is truncated or fully retained. - Check whether the associated links in the abnormal data list can jump to the storage location of the corresponding original data source.
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.