What the data for this category looks like
The data for this category typically comes from event content libraries, event backend management systems, traffic statistics tools, and user feedback channels. Daily produced documents include event plans, resource slot scheduling tables, event page copy, and post-event review reports. Most fields include event ID, resource slot number, traffic metrics, and user feedback tags. Data updates follow a weekly cycle. Update frequency temporarily adjusts around holidays. Some temporary event documents have no fixed update cycle.
What constraints do these characteristics impose on knowledge base retrieval and recall
The data sources for this category are scattered. Document types are diverse. Update cycles are flexible. Knowledge base retrieval must accurately limit the associated event resource library to avoid mixing in irrelevant content. Long texts such as event plans and post-event review reports mix with short texts such as page copy. Retrieval logic must adapt to different text lengths. Documents for temporary events have no fixed update cycle. Rapid vector database refresh must be supported. Events have clear cycles and bind to resource slots. Retrieval must associate the time and resource slot tags of the corresponding event to narrow the recall scope.
How to set configurations
The following configurations are based on FastGPT v4.17.0, adapted to the business data characteristics of special event operations:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
collectionIds | Bind the dedicated dataset ID collection for the current event | This category has scattered data sources, so retrieval scope must be limited to avoid recalling irrelevant event or non-event data |
searchMode | Switch based on document type: use full-text retrieval for long documents, use keyword + semantic retrieval for short copy | Daily documents include long-text event plans and short page copy, so recall logic must adapt to different scenarios |
similarity | 0.75–0.85 | Balance retrieval recall completeness and result relevance, adapted for mixed-type business documents |
usingReRank | Enable, take the top 3–5 reranked results | Inquiries related to events require precise matching of event details, and reranking improves the relevance ranking of results |
datasetSearchUsingExtensionQuery | Enable, expand the thesaurus to cover dedicated common event terms | This category of documents contains dedicated event terminology, and expanded queries improve recall accuracy |
limit | Top 10–15 original recall results | Ensure sufficient event-related data is covered, while avoiding excessive results that cause context overflow |
Three common configuration mistakes
- Retrieval results do not include local event knowledge base content:
collectionIdsis not configured to limit the retrieval scope, resulting in recall of irrelevant external dataset content and failure to hit event-specific documents. - Imbalance between retrieval recall completeness and result accuracy:
similarityandusingReRankparameters are not set reasonably. Too high a value misses relevant event documents. Too low a value mixes in a large number of irrelevant results. Not enabling reranking leads to chaotic ranking and prolonged result processing time. - PDF documents in event materials cannot be correctly recalled: Correct parsing rules are not configured for PDF documents, resulting in some event PDF documents with complex layouts or special formats failing to be vectorized correctly.
How to confirm the configuration is correct
- Enter the FastGPT knowledge base management interface, check whether the
collectionIdsof the current event dataset are correctly bound to the retrieval configuration. - Enter event-specific terminology to verify that retrieval results only include documents related to the event, with no irrelevant content.
- Test different types of event documents such as event plans and page copy, to confirm that recall result relevance meets expectations after
searchModeis switched. - Check the retrieval log to confirm that
usingReRankanddatasetSearchUsingExtensionQueryare working normally with no error messages.
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.