What the data for this category looks like
The data for this category primarily comes from internal content libraries, event backends, traffic statistics tools, and user feedback channels. Daily maintained documents include event execution plans, resource slot schedules, event page copy, and post-event summary reports. Data updates follow a weekly cadence, with temporary additional updates around holidays. Document fields include event cycle, resource slot ID, traffic metrics, user feedback tags, and more. There is no unified standard unit, and values adjust based on the business attributes of individual documents.
What constraints do these characteristics impose on re-ranking and hybrid retrieval
Documents for special event campaigns are mostly short-cycle event-related content, with high data update frequency and temporary changes around holidays. Therefore, re-ranking and hybrid retrieval need to adapt to rapidly updated data sources. Unlike long-term personalized recommendations for content recommendation operations, retrieval for this category must match clear event cycles and resource slot requirements. Documents such as event plans and resource slot schedules have large field differences. During hybrid retrieval, priority must be given to matching precise fields such as event cycle and resource slot ID to avoid recalling irrelevant general operation content. At the same time, the recall results of event-related content directly relate to event implementation effects. The accuracy of recall and re-ranking must be strictly controlled to prevent low-relevance content from entering the context, avoid missing event audits or resource slot mismatches. Additionally, data sources are scattered. Hybrid retrieval needs to integrate content from multiple platforms including content libraries, event backends, and traffic data, which places higher requirements on the stability of the retrieval pipeline.
How to set the configurations
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
similarity | 0.65–0.75 | Match precise business fields such as event plans and resource slot schedules, filter low-relevance general operation documents |
limit | Top 8–12 results | Control the number of results returned per retrieval, adapt to the single retrieval needs of event-related documents |
usingReRank | Enabled | Optimize result ranking accuracy through re-ranking for multi-source heterogeneous event documents |
searchMode | Hybrid Retrieval | Integrate multi-source content from content libraries, event backends, and traffic data, cover full-process event documents |
| Re-ranking minimum relevance threshold | 0.55–0.60 | Calibrated based on actual testing, avoid situations where recall results are empty or irrelevant content is included |
Three common configuration mistakes
- Phenomenon: Retrieval results are empty after re-ranking is enabled, and no valid content is returned. Cause: The
similaritythreshold is set too high, filtering all recall results that fit the event business scenario. - Phenomenon: Re-ranked results are poorly sorted and fail to match event priority requirements. Cause: Re-ranking parameters are not adjusted based on the field characteristics of event documents such as resource slots and cycles, and only default weights are used.
- Phenomenon: Calls to the locally deployed BGE Reranker v2 m3 module throw errors, and the re-ranking link cannot be completed. Cause: The deployment path and port of the local model are not correctly configured in FastGPT, or the module version is incompatible with the current FastGPT version.
How to confirm the configuration is complete
- Import 10 typical event documents, perform retrieval tests, and check whether the returned results include core business documents such as event plans and resource slot schedules.
- View the FastGPT retrieval pipeline logs, confirm that the re-ranking model is called normally, and there are no timeout or error messages.
- Adjust the
similaritythreshold and the re-ranking minimum relevance threshold, compare result relevance across different configurations, and calibrate values that meet business needs. - Simulate temporary event data updates around holidays, test whether retrieval results can cover new event content in a timely manner.
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.