What the data for this category looks like
The core data for this category 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. Documents processed daily include operation license applications, vehicle technical files, driver qualification materials, safety production management systems, road transport operation licenses, and more. Most document structures include fixed business fields such as vehicle license plate number, qualification certificate number, business scope, site address, and others. The service subjects are the transportation bureau examination and approval department, road transport management institutions, and road transport enterprises.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
This category’s data characteristics create three constraints for the retrieval and recall link:
- The data is updated daily, so incremental indexing must be supported to avoid excessive system resource usage from full synchronization.
- The documents contain a large number of road transport industry-specific terms and fixed business fields. An accurate semantic matching logic must be adapted to prevent generic retrieval from failing to locate accurate approval materials.
- The approval process requires association of multiple types of documents, such as vehicle files and driver qualifications. Retrieval must support cross-document associated recall, while ensuring that recall results match the priority of the approval link to avoid irrelevant content interfering with verification work.
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 | Semantic similarity threshold for matching road transport industry-specific terms, balancing recall accuracy and coverage |
limit | Top 10 entries | Covers multiple types of associated materials required for operation license applications, while avoiding excessive context length that affects processing efficiency |
searchMode | Semantic retrieval | Adapts to unstructured content and industry-specific terms in business documents, aligning with the query habits of approval staff |
usingReRank | Enabled | Reorders recall results by approval priority, prioritizing core materials such as operation license applications and vehicle technical files |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands industry-specific query terms, such as expanding "vehicle files" to "motor vehicle technical files", to improve recall coverage |
collectionIds | Bound to the road transport operation licensing exclusive knowledge base collection | Limits the retrieval scope to avoid mixing irrelevant documents from other categories |
Three Common Mistakes
- A 503 error is prompted when uploading a road transport operation license application file. The cause is that the incremental synchronization trigger rule is not configured, and the full index task occupies too many system resources, leading to overload.
- Semantic retrieval fails to locate specific keywords in vehicle technical files. The cause is that
datasetSearchUsingExtensionQueryis not enabled, and no industry term expansion is performed on the query terms, resulting in the generic semantic model failing to match business-specific expressions. - The number of retrieval results is insufficient to cover the materials required for approval. The cause is that the
limitparameter is set too low, leaving insufficient context space to accommodate multiple types of associated documents.
How to Confirm the Configuration is Correct
- Upload a standard vehicle technical file document, enter the FastGPT knowledge base management interface to check the indexing progress, and confirm that the indexing status is successful.
- Enter the query "What materials need to be submitted for a road transport operation license application", and check whether the retrieval results include core document blocks such as operation license applications and safety production management systems.
- After enabling
usingReRank, compare the sorting of retrieval results before and after enabling to confirm that core approval materials are displayed first. - Simulate a high-frequency query scenario to verify that the recall results under the
similaritythreshold have no redundant irrelevant content, and meet the needs of approval verification.
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.