What the data for this category looks like
This category’s data primarily comes from customer-submitted warranty work orders, system operation logs, project contract warranty clauses, and software version records. Daily produced documents include warranty work orders, defect confirmation sheets, repair records, and customer confirmation sheets. Fields include work order number, defect details, associated project identifier, warranty period, repair steps, and customer acceptance flag. Data is updated daily or triggered by each business work order, with no fixed batch update cycle.
What constraints these characteristics bring to the knowledge base retrieval and recall link
Data sources are scattered, and the length of individual content varies significantly. Long-text warranty clauses and short-text defect descriptions coexist, so retrieval matching logic must adapt to different lengths. Update frequency has no fixed cycle, so real-time or on-demand recall of the latest work orders and repair records must be supported to avoid using outdated warranty clauses or closed invalid records. Document fields include a customer acceptance flag, so retrieval must prioritize associated confirmed repair solutions to prevent referencing content that has not passed customer acceptance. Additionally, data is linked to project IDs, so retrieval must support filtering by project dimension to ensure recalled content matches the corresponding warranty project and avoid interference from invalid cross-project data.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Adapt to short-text matching for warranty work orders and defect descriptions, filter low-correlation redundant results |
limit | Top 6 entries | Cover associated repair records and clauses for a single warranty issue, avoid overly long context affecting model processing |
searchMode | hybrid | Combine semantic and keyword retrieval to match fixed clauses in warranty terms and specific defect descriptions in work orders |
usingReRank | Enabled | Perform secondary sorting on recall results to prioritize returning repair solutions with higher matching degree to the current defect |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand queries by adding warranty-related keywords to improve recall accuracy for contract clauses |
collectionIds | Knowledge base collections bound to the project | Only retrieve documents associated with the current warranty project to avoid recalling invalid cross-project data |
This configuration is based on the parameter logic of FastGPT v4.17.0
Three Common Configuration Mistakes
- The symptom is an API call returning a 422 status code, with no system operation guidance content output normally. The cause is incorrect configuration of
datasetSearchUsingExtensionQuery, leading to abnormal retrieval parameter format, orcollectionIdsbound to a non-existent knowledge base collection. - The symptom is an API call only returning model answers, without cited document details and source information. The cause is failure to enable the citation field return configuration for retrieval results, or failure to correctly specify the range of knowledge base collections to recall.
- The symptom is an AI-generated answer not associating content from the knowledge base, relying only on general training knowledge. The cause is the
similaritythreshold being set too high, filtering valid matching results, or thelimitvalue being too low to recall enough associated repair records and clauses.
How to Verify Successful Configuration
- Submit a typical warranty defect description, and check whether retrieval results include associated repair records and warranty clauses under the current project.
- Call the API interface to verify whether returned results include the model answer and corresponding cited document fragments and source identifiers.
- Simulate submitting a new warranty work order, and check whether the retrieval result can recall the work order content within a reasonable time.
- Adjust the
similaritythreshold to the middle value of the specified range, and verify whether low-correlation redundant documents are effectively filtered.
The values provided 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.