What the data for this category looks like
Data for this category primarily comes from event backends, content libraries, traffic data, and user feedback. Daily produced documents include event plans, resource slot schedule tables, event page copy, post-event review reports, and more. Fields cover event cycles, resource slot IDs, traffic conversion data, user feedback tags, and other relevant items. Data updates on a weekly basis, with temporary additional updates before and after holidays. The data structure follows the full workflow of a single special event, includes clear event cycle nodes and resource slot binding information, and has no redundant cross-event associated fields.
What constraints these characteristics impose on knowledge base retrieval and recall
This category’s data follows the full workflow of a single special event. Knowledge base retrieval must accurately match event cycle and resource slot binding information to avoid irrelevant cross-event content interfering with results. Data updates weekly, with temporary additions around holidays. The retrieval process must therefore support incremental synchronization mechanisms to avoid returning expired documents. Documents include multi-dimensional fields such as traffic data and user feedback tags. Recall logic must support multi-field associated matching, and filter historical documents from ended events to ensure result timeliness.
How to set up configurations
These configurations are based on FastGPT v4.17.0, and are adapted for the knowledge base retrieval and recall needs of special event operations.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the accuracy requirement for special event documents, avoids recalling irrelevant cross-event content |
limit | Top 8 entries | Controls the number of single recall results, adapts to the scenario where event operations teams quickly review documents related to a single event |
searchMode | Precise matching | Prioritizes matching core fields such as event cycle and resource slot, aligns with the structured characteristics of this category’s data |
usingReRank | Enabled | Rearranges recall results by event priority to ensure core event documents are displayed first |
datasetSearchUsingExtensionQuery | Enabled | Expands search terms based on event names to cover different document types such as event plans and schedules |
collectionIds | Bound knowledge base collections by event cycle | Limits the retrieval scope to the exclusive knowledge base of the current event to avoid cross-event interference |
Three common errors
- Phenomenon: Retrieval results do not display referenced knowledge base source information. Cause: The data source display configuration for retrieval results is not enabled, making it impossible to trace document sources.
- Phenomenon: No image indexing model option appears when creating a knowledge base in a locally deployed FastGPT v4.17.0 environment. Cause: The current deployment version does not load the model files required for this function, or the function is only available in specific authorization modes.
- Phenomenon: Garbled characters appear in results returned when configuring a thinking model for retrieval. Garbled characters disappear after replacing with a non-thinking model. Cause: Event operation documents contain a large number of special format contents such as event schedule IDs and resource slot identifiers. The coding parsing logic of the thinking model does not adapt to the field structure of this category of documents.
How to confirm the configuration is complete
- Enter the core keywords of the current event, check if retrieval results only include documents within the event cycle, with no irrelevant cross-event content.
- Trigger an incremental update task, confirm that only recent weekly event documents are synced, with no full knowledge base rebuild operations.
- After configuring
usingReRank, check if the result order prioritizes core event plans and schedule documents. - Test retrieval using a thinking model, confirm that the returned results have no garbled characters, and data source information is displayed normally.
The values listed here are common starting points, and should be evaluated against samples relevant to the specific use case.
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.