What the data for this category looks like
The core data for this category comes from the content review backend, user reporting portals, public regulatory policy libraries, and internal review sample libraries. Daily maintained documents include review rule entries, violation handling guidelines, user appeal records, and quality inspection review reports. Fields cover rule IDs, trigger thresholds, violation types, appeal status, quality inspection scores, and more. Data is updated either daily or immediately when regulatory policies change. Individual document lengths vary widely, from rule entries of tens of characters to complete appeal case files of thousands of characters.
Constraints Imposed on Reranking and Hybrid Retrieval
Data for this category is scattered across multiple systems and document libraries. Hybrid retrieval must integrate heterogeneous data sources including backend review rules, appeal case files, and policy documents. It must ensure fields from different sources can be uniformly matched. Document lengths vary widely, from short rule entries of tens of characters to full appeal case files of thousands of characters. Reranking models must adapt to text features of different lengths. Data update frequency adjusts immediately with policy changes. Retrieval pipelines must support fast pulling of the latest rules and sample data to avoid returning outdated content. Additionally, difficult sample judgment and appeal review links have high requirements for retrieval accuracy. Irrelevant results must be filtered to focus on core rules and associated samples.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
usingReRank | Enabled | This category requires precise matching of review rules and appeal samples. Reranking filters low-relevance results and improves review efficiency |
similarity | 0.72–0.85 | Semantic matching between review rules and sample text must balance precision and recall. This interval supports hybrid retrieval for both short rules and long case files |
limit | Top 10 entries | For operations duty and ticket Q&A scenarios, single retrieval result count must be kept within a reasonable range to avoid information overload |
searchMode | hybrid | Must integrate vector retrieval and keyword retrieval to cover both rule keyword matching and semantically associated sample retrieval |
| Reranking model matching threshold | 0.68–0.75 | For the short text features of review rules, this threshold effectively filters irrelevant samples and focuses on core associated content |
Three Common Mistakes
- Deploying the bge-rerank-base image then calling the reranking model returns a 401 Unauthorized error. The cause is failure to correctly configure model access keys or the image's permission verification parameters. FastGPT cannot properly call the reranking model.
- When the reranking function is not enabled, retrieval results are sorted only by vector similarity. Semantically irrelevant rule entries cannot be filtered. Large volumes of invalid content must be manually filtered.
- When hybrid retrieval is configured for pure vector mode, keyword fields in review rules cannot be matched. Commonly triggered general violation rules cannot be accurately recalled. Ticket response efficiency is impacted.
How to Verify Configurations Are Correctly Set
- Navigate to the retrieval configuration page in FastGPT. Confirm the
usingReRankswitch is enabled. - Submit a test ticket containing violation type keywords. Verify returned results include the latest review rule entries and associated appeal samples.
- Check the number of returned retrieval results. Confirm it matches the value set for the
limitconfiguration. - Compare retrieval results with and without the reranking function enabled. Confirm the proportion of low-relevance results decreases after reranking is enabled.
The values provided are common starting points. They should be evaluated against the samples relevant to the deployment.
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.