What this category of data looks like
Data for this category primarily comes from enterprise internal databases, data dictionaries, data masking tool operation logs, and access approval systems. Primary service users include data security engineers, data service teams, and compliance personnel. Daily maintained documents cover data masking plans, field mapping tables, masking verification reports, and approval records. Data updates per project batch or single task, with no fixed cycle. Data fields include sensitive field identifiers, masking rule codes, verification results, approval status, and other fields. No unified mandatory units exist. Values adjust based on project configuration.
What constraints do these characteristics impose on re-ranking and hybrid retrieval
Data for this category comes from scattered heterogeneous sources including databases, tool logs, and approval systems. Hybrid retrieval requires integrating valid data across different formats. Data updates per project batch. During retrieval, prioritize currently active project configuration data. This avoids calling expired masking rules. Daily document types are diverse, including plans, mapping tables, and verification reports. Re-ranking requires distinguishing information priority across different documents. Prioritize content directly related to sensitive field processing. Sensitive field identifier fields account for a high proportion. Retrieval must focus on keywords related to sensitive information processing. This avoids interference from irrelevant general operations and maintenance content.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
usingReRank | Enabled | This category requires prioritizing results related to sensitive fields and masking rules. Re-ranking improves result accuracy, filters irrelevant content, and is compatible with FastGPT v4.17.0’s re-ranking call logic. |
limit | Top 3-7 entries | The volume of operations and maintenance tickets and documents related to data masking is relatively concentrated. Excessive results increase screening costs for operations and maintenance duty personnel. This aligns with daily efficiency needs. |
similarity | 0.72-0.85 | This balances the recall rate and accuracy of sensitive information matching. It avoids false recall of non-sensitive general operations and maintenance content, and fits the retrieval goals of this category. |
searchMode | Hybrid Retrieval | Data for this category comes from heterogeneous sources including databases, tool logs, and approval systems. Hybrid retrieval integrates all valid data and covers complete business scenarios. |
Three Common Mistakes
- Symptom: The interface displays
Invalid URL (POST /v1/rerank). The re-ranking model call fails. Cause: The re-ranking model API access address was not configured correctly, or the oneAPI re-ranking model forwarding path was not mapped properly. - Symptom: Calling the re-ranking model returns a
401 unauthorizederror. Cause: The correct re-ranking model API key was not entered in FastGPT configuration, or the key has insufficient permissions. - Symptom: Hybrid retrieval returns large amounts of non-sensitive content. Screening efficiency is low. Cause: The re-ranking function was not enabled, or the similarity threshold does not meet business needs. This results in recall of irrelevant results.
How to Confirm Configuration Is Complete
- Access the scenario configuration page of FastGPT v4.17.0. Verify the
usingReRanktoggle is enabled. - Submit test tickets containing sensitive field keywords. Check if retrieval results prioritize relevant documents such as data masking plans and field mapping tables.
- Review the re-ranking model’s API configuration items. Confirm the access address and verification information are correctly entered with no format errors.
- Adjust values of relevant configuration items based on actual operations and maintenance scenarios. Verify results meet business screening needs.
The values given 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.