What the data for this category looks like
Rail transit equipment declaration materials and file review data mainly comes from R&D design drawings, production process documents, third-party inspection reports, project acceptance files, and compliance qualification documents. Data updates follow the progress timeline of individual equipment projects. A single declaration batch corresponds to one data update cycle, which is typically 7–14 days. Document formats include PDF drawings with dimension annotations, Excel spreadsheets with performance parameters, and Word files with review nodes. Fields include equipment model, production batch, detection load, compliance clause number, and units involve professional engineering units such as millimeters, kilonewtons, and operating hours.
What constraints these characteristics impose on the knowledge base retrieval and recall link
The long document drawings, structured parameter tables, and compliance clause number fields of rail transit equipment impose multiple constraints on retrieval and recall. Long text drawings, after being chunked, are prone to semantic discontinuity, requiring more precise context association logic. The demand for precise field matching in structured parameter tables is higher than the adaptability of general fuzzy retrieval. Number-based retrieval for compliance clauses requires that recall results strictly match the numbers, with no irrelevant content returned. Data updated by project batch requires that the retrieval scope can be quickly filtered by project dimension to avoid interference from redundant data across batches.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
similarity | 0.72–0.80 | Matches the semantic precision of rail transit equipment professional terminology, prevents low-relevance general engineering documents from being included |
limit | Top 8 entries | Core review materials for a single declaration file usually do not exceed 10 copies, controlling the number of returned entries avoids context overflow |
searchMode | hybrid | Balances precise matching for structured parameter tables and semantic retrieval for long text drawings, covering data sources of different formats |
usingReRank | Enabled | Semantic deviation exists in recall results after chunking long documents, reranking filters low-relevance entries caused by chunk discontinuity |
datasetSearchUsingExtensionQuery | Enabled | Declaration materials often include abbreviated models, extended queries can supplement matching of full names and industry common aliases |
collectionIds | Bound to the current declaration project | Filters redundant data across projects, only recalls declaration and file materials from the current batch |
Three Common Misconfigurations
- Phenomenon: Retrieval results include document references not uploaded to the current project knowledge base, with no hidden entry available. Cause: Failed to bind the exclusive project collection via
collectionIds, recalling redundant file materials across batches. - Phenomenon: A single retrieval returns too many reference entries, and some results have insufficient semantic matching. Cause: The
similaritythreshold is set too low, or thelimitparameter value exceeds the reasonable range of current review materials. - Phenomenon: Irrelevant unstructured redundant entries cannot be filtered after hybrid retrieval, resulting in mixed results. Cause: The
usingReRankreranking function is not enabled, and no semantic sorting filter is applied to chunked long text results.
How to Confirm the Configuration Is Properly Set
- Check the source collection identifier of retrieval results, confirm that only the files and materials of the current declaration project are included.
- When adjusting the
similaritythreshold, verify that the semantic matching degree of returned results meets review requirements via test samples. - After enabling
usingReRank, compare the result order before and after reranking, confirm that highly relevant entries are displayed first. - Test the extended query function, verify that abbreviated models can match corresponding full-name documents.
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 2026-09-28.