What this category of data looks like
This category of data mainly comes from monitoring systems, ticket systems, contract documents, and customer communication records. Regularly produced documents include SLA clauses, monthly service reports, availability statistics reports, and customer review minutes. The data update cycle is monthly or quarterly. Core fields include service cycle range, SLA agreed metrics, actual service compliance status, abnormal event traceability records, and customer feedback content. Some fields are presented in numerical or text description formats.
What constraints do these characteristics impose on re-ranking and hybrid retrieval
The data sources for this category are diverse, including structured contract clauses, semi-structured ticket records, and unstructured customer review minutes. Hybrid retrieval must adapt to recall weights for different formatted data. The data update cycle is monthly or quarterly. A single retrieval must cover all documents for the full cycle, so the retrieval scope must clearly limit the service cycle interval. Core fields focus on SLA metrics, service compliance status, and abnormal events. The re-ranking link must prioritize content related to business terminology, to avoid irrelevant documents interfering with retrieval results. Document structures also vary greatly, so matching rules need to be adjusted to ensure the recall priority of core business fields.
How to configure the settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the core semantics of SLA clauses and service reports, filters low-relevance redundant content |
limit | Top 8–12 entries | Covers the core entries of monthly service reports, ensures business completeness of retrieval results |
searchMode | Hybrid Retrieval | Adapts to multi-format data recall for structured contracts, semi-structured tickets, and unstructured customer minutes |
usingReRank | Enabled | Prioritizes results related to SLA metrics and business terminology, improves business relevance of retrieval results |
| Re-rank model video memory release timing | Automatically unload after retrieval completes | Adapts to the low data update frequency of this category, reduces video memory occupation costs |
Three common mistakes
- Phenomenon: The re-rank model continues to occupy video memory after retrieval completes, and does not automatically release it. Cause: The trigger rule for automatically unloading the re-rank model after retrieval is not configured, resulting in long-term occupation of video memory resources.
- Phenomenon: After enabling hybrid retrieval optimization and result re-ranking, the response time exceeds 30 seconds. Cause: The value of the
limitparameter is not adjusted. Recalling too many low-relevance entries superimposes re-ranking calculation overhead, resulting in excessive latency. - Phenomenon: A request error is displayed after configuring a local re-rank model. Cause: The correct local model path is not specified, or the model version is incompatible with FastGPT v4.17.0, resulting in model call failure.
How to confirm the configuration is complete
- View the FastGPT retrieval configuration panel, confirm that
searchModeis set to hybrid retrieval mode, andusingReRankis enabled. - Export the retrieval log, and check that the number of returned entries matches the value range of the
limitparameter. - Test retrieval using keywords related to SLA clauses and service reports, confirm that the re-ranked results prioritize displaying core business-related content.
- Monitor server video memory usage, confirm that the re-rank model automatically releases occupied video memory resources after retrieval completes.
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.