What the data for this category looks like
This category of data primarily comes from content audit backends, user reporting channels, public regulatory policy documents, and internal audit sample libraries. Daily maintained documents include structured audit rule entries, violation disposal operation guides, user appeal review records, and monthly quality inspection reports. Data update frequency follows policy adjustments or daily synchronization. Document fields include rule number, applicable scenario, trigger threshold, disposal process, appeal time limit, and more. Sample library data includes violation sample tags, audit judgment results, and quality inspection scores.
What constraints these characteristics impose on the reranking and hybrid retrieval link
Structured fields of audit rules require retrieval to prioritize matching rule numbers and applicable scenarios, so hybrid retrieval must balance semantic similarity and precise field matching. Real-time updates of sample library data require the retrieval pipeline to support access to high-frequency refreshed datasets, avoiding expired samples that compromise judgment accuracy. A high proportion of unstructured text in appeal records and quality inspection reports requires reranking models to filter low-relevance results, while also controlling the retrieval scope to valid data within the current audit cycle to prevent cross-cycle rule interference with judgments. Sudden changes to rules require retrieval configurations to quickly switch to adapt to new rule retrieval dimensions, avoiding retrieval result failures caused by inflexible configurations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Result Reranking Switch | Enabled | This category has a high proportion of unstructured samples and rule text. Reranking filters low-relevance retrieval results and improves the accuracy of audit judgments |
| Similarity Threshold | 0.72–0.85 | Balances the precision and recall range of rule matching, avoiding missed judgments of violating content or false judgments of compliant content |
| Single Recall Upper Limit | Top 10 entries | Covers rule and sample data for common audit scenarios, while controlling the computational load of the reranking model |
| Search Mode | Hybrid Retrieval | Balances structured rule field matching and unstructured text semantic association, adapting to the retrieval needs of multiple types of data in this category |
| Reranking Call Timeout | 15 seconds | Avoids overall retrieval timeouts due to network latency, adapting to the real-time response requirements of content audit scenarios |
Three common misconfigurations
- The symptom is that knowledge base search results are not sorted by relevance, and returned results have inconsistent matching degrees with audit rules. The cause is that the result reranking function is not enabled, or no valid reranking model is bound.
- The symptom is that the retrieval request returns a 504 timeout status code, with a duration exceeding 15 seconds. The cause is that the number of recalled results per single request is too high, and the computational load of the reranking model exceeds its carrying capacity.
- The symptom is that retrieval results include expired old-version audit rules, and the rule update time displayed in the fields is earlier than the current policy effective time. The cause is that no retrieval condition for filtering by update time is set.
How to confirm the configuration is complete
- Enter the knowledge base configuration page of FastGPT v4.17.0, confirm that the result reranking switch is enabled.
- Submit a test query containing specific audit rule keywords, check whether the returned results are sorted to prioritize high-matching rule entries.
- View the API call logs in the system backend, confirm that the reranking model call requests are successful and no error messages are returned.
- Manually update the effective time of one audit rule, verify that the retrieval results prioritize recalling the rule content with the latest update time.
The values listed above are common starting points. Always test and adjust configurations based on your own specific samples and use cases.
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.