What this category of data looks like
Data sources for this category include monitoring systems, ticketing systems, contract documents, and customer communication records. Update frequency is monthly or quarterly. Daily output documents include SLA clauses, monthly service reports, availability statistics reports, and customer review minutes. Data fields cover service cycle range, SLA compliance rate indicators, abnormal event classification, customer feedback records, and more. Data volume adjusts with the number of serviced customers and statistical dimensions. There is no fixed uniform single data length.
What constraints do these characteristics impose on knowledge base retrieval and recall
Data characteristics of this category impose multiple constraints on knowledge base retrieval and recall. First, monthly or quarterly update rhythm eliminates the need for high-frequency real-time index synchronization. Vector databases can be batch refreshed according to the update cycle, reducing operation and maintenance costs. Second, multi-source heterogeneous data sources require support for importing documents in different formats such as contract texts, ticket records, and monitoring logs. Retrieval must adapt to semantic matching logic for multiple fields. In addition, the data includes professional terms such as SLA clauses and service indicators. Recall must prioritize matching semantics related to service reports and SLA compliance, to avoid irrelevant content interfering with results. Finally, data volume changes with the number of serviced customers. Retrieval parameters must be properly configured to balance recall accuracy and response speed.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches professional terms related to SLA, filters low-correlation non-service content |
limit | Top 3–5 entries | Controls the number of recall results, adapts to the summary statistics requirements of monthly service reports, avoids excessive redundant data |
searchMode | semantic | Prioritizes matching semantically related SLA clauses and service records, suitable for professional term retrieval scenarios |
usingReRank | Enabled | Performs secondary sorting on initial recall results, improves the sorting priority of content related to SLA |
datasetSearchUsingExtensionQuery | Enabled | Generates extended semantics based on original queries, covers synonymous expressions related to SLA, improves recall coverage |
collectionIds | Specified by target knowledge base collections | Limits retrieval scope to only knowledge base collections related to SLA and service reports, avoids interference from irrelevant data |
Three common mistakes
- Phenomenon: Retrieval returns results completely unrelated to knowledge base references, and the answer does not hit content related to SLA or service reports in the knowledge base. Cause: The
similaritythreshold is not configured, or the threshold is set too low, causing low-correlation content to be recalled first. - Phenomenon: Knowledge base retrieval takes more than 30 seconds, and service report generation cannot be completed on time. Cause: The
limitparameter is not restricted, and too much redundant data is recalled, increasing vector matching and sorting time. - Phenomenon: Exported knowledge base dataset lacks the
contentfield, or indexes are incorrectly associated with content. Cause: The index mapping rules of the knowledge base collection are not properly configured, causing fields to be incorrectly associated during data export.
How to confirm configurations are set correctly
- Run a test retrieval, enter a query related to SLA clauses or service reports, and check whether the returned results include corresponding content in the knowledge base.
- View retrieval latency, and adjust parameter values to meet requirements based on actual service report generation time constraints.
- Check the reference sources of recall results, confirm that all results come from the configured target knowledge base collections.
- Export the test knowledge base dataset, check whether the
indexandcontentfields are correctly associated.
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.