What the Data for This Category Looks Like
This category’s data primarily originates from risk control systems, audit records, user behavior logs, and report evidence. Daily processed documents include account ban records, user appeal materials, review opinions, and user notifications. Data update cycles are divided into real-time and daily. A single data entry typically contains fields such as account unique identifier, violation type, appeal submission time, review status, and associated evidence path. Fields are adjusted based on platform governance rules, with no unified fixed field units.
Constraints Imposed on Reranking and Hybrid Retrieval
Hybrid retrieval must support content with varying formats and fields from multi-source heterogeneous sources, including risk control logs, audit documents, and appeal materials. The retrieval pipeline must support low-latency incremental index refreshes to accommodate real-time and daily data update cycles. Single data entries include differentiated fields such as account identifiers and violation types. The reranking stage must perform weighted sorting based on the semantic matching degree of account-level associated evidence and appeal grounds. Errors in this category directly impact user rights and interests. Reranking must prioritize matching core association points between appeal materials and violation evidence, to prevent irrelevant content from interfering with critical judgments.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Rerank Toggle | Enabled | This category requires filtering low-relevance recall results, and reranking improves the matching priority of appeal materials and violation evidence |
| Similarity Threshold | 0.70–0.88 | Adapts to text matching accuracy for account ban scenarios. This interval balances false ban risk and appeal pass rate, and is calibrated via testing on FastGPT v4.17.0 |
| Single Search Limit | Top 8–12 entries | Controls the number of results returned per retrieval round, meets the quick browsing needs of on-duty operations staff, and aligns with internal ticket processing rhythms |
| Search Mode | Hybrid Retrieval | Must support mixed recall of multi-source data including risk control system logs, audit records, and appeal materials. Hybrid retrieval mode balances exact matching and semantic recall |
| Rerank Return Count | Top 5–7 entries | Focuses on core associated content, avoids redundant information interfering with on-duty operations staff’s review judgments, and is calibrated via testing on FastGPT v4.17.0 |
Three Common Misconfigurations
- Starting the application after configuring an offline rerank model triggers an error, with the interface displaying
rerank error fastgpt | { message: { detail:. Cause: The model path is not correctly configured, or dependent packages are missing, preventing FastGPT from loading the offline rerank model files. - The result rerank model option cannot be selected when creating an application. Cause: The rerank model is not correctly registered in the global configuration file, or the registered model format does not meet FastGPT requirements.
- Hybrid retrieval returns results with extremely low relevance to appeal materials. Cause: No reasonable similarity threshold and rerank weight are set, causing low-relevance risk control logs to interfere with core matching results.
How to Verify Proper Configuration
- Navigate to the FastGPT application configuration page, confirm the rerank toggle is enabled and the search mode is set to hybrid retrieval.
- Upload a test appeal material and associated violation evidence, execute a retrieval, and check the sorting logic of returned results to confirm high-relevance content is displayed first.
- Check the global configuration file to confirm the required rerank model is correctly registered, and no model loading-related error logs exist.
- Simulate real-time data access, confirm the retrieval pipeline returns results within a reasonable time frame.
The values provided 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.