What this category of data looks like
This category of data primarily comes from the road transport administration system, the national public service platform for dynamic information of road transport vehicles, and the vehicle and practitioner database. The update frequency is daily. Daily documents include road transport business license application materials, vehicle technical files, driver qualification materials, work safety management systems, valid road transport business licenses, and more. Most document structures combine structured approval forms and unstructured regulatory texts. Fields cover business entity information, vehicle parameters, practitioner qualifications, licensing scope and validity period, and there are no additional custom statistical fields.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Multiple data sources require the retrieval link to support cross-dataset associated recall, to avoid fragmented information segments. The daily update frequency requires configuring incremental synchronization logic, to ensure retrieval results cover the latest vehicle, personnel and licensing status data. The coexistence of structured approval forms and unstructured regulatory texts requires retrieval modes to balance precise field matching and semantic associated recall. Fields cover strongly constrained information such as qualifications and validity periods, which requires limiting associated field ranges during retrieval, to avoid recalling content unrelated to licensing approval. At the same time, approval results directly relate to the legality of enterprise operations, which requires the matching accuracy between recalled regulatory basis and application materials to meet approval verification requirements.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic relevance of road transport-related policies. The range is calibrated based on actual testing, to ensure recalled policy clauses closely align with user questions |
limit | Top 8 entries | Covers multiple types of approval basis such as vehicles, personnel and licensing scope, to avoid recalling too many entries that exceed context limits |
searchMode | hybrid | Combines precise matching of policy clauses and semantic recall of related regulatory texts, adapting to mixed data sources of structured and unstructured documents |
usingReRank | Enabled | Reorders recalled candidate results, filters low-relevance policy segments, and improves the accuracy of approval basis |
datasetSearchUsingExtensionQuery | Enabled | Expands synonymous expressions of user questions, covering industry term variants related to road transport business licensing, such as "operating permit" and "practitioner qualification certificate" |
collectionIds | Bind to the dedicated dataset for road transport business licensing | Limits the retrieval scope to only the approval documents and regulatory basis of this category, to avoid recalling unrelated system data |
Three common mistakes
- Training errors occur when importing knowledge base data exported from older versions. The cause is failing to adjust the field structure according to the CSV template format of the current FastGPT version. There are differences in required fields between older and newer versions.
- The upper limit of retrieval results cannot be adjusted. The cause is incorrectly configuring the
limitparameter, or confusing the retrieval upper limit with the context window upper limit, and failing to set a reasonable value in the corresponding configuration item. - The reranking function fails during large model calls, but works normally in knowledge base search tests. The cause is failing to enable the
usingReRankparameter in the retrieval configuration for large model calls, only enabling the reranking function in a separate knowledge base test session.
How to confirm the configuration is complete
- Verify the dataset binding relationship, confirm that only document collections related to road transport business licensing are included, with no additional unrelated datasets.
- Test retrieval for different types of policy questions, such as "What materials are required for business license applications", and check that retrieval results cover corresponding document types.
- Verify whether the reranking function takes effect, compare the sorting differences of retrieval results when
usingReRankis enabled and disabled. - Check the incremental synchronization configuration, confirm that the daily automatic update task for dynamic data such as vehicles and personnel is running normally.
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.