What the Data for This Category Looks Like
Data sources for this category include the road transport administration system, the national public service platform for dynamic road transport vehicle information, and the vehicle and practitioner database. The update frequency is daily. Daily processed documents cover business license applications, vehicle technical files, driver qualification materials, safety production management systems, road transport business licenses, and other materials. These cover all file materials for the entire process of transport enterprise business qualification application, approval, and renewal. They serve the verification and review work of the transportation bureau approval department and road transport management institutions.
What Constraints These Characteristics Impose on the "Vector Models and Indexes" Link
The daily data update feature requires indexes to support incremental synchronization, to avoid resource consumption and time delays caused by full reconstruction. The mixed structure of multiple types of documents requires chunking strategies adapted to different document character lengths. For example, short driver qualification materials and long business license applications need unified processing logic. The need for multi-source data integration requires indexes to support unified mapping and retrieval of cross-system data. The strict verification standards in the approval process require retrieved results to have high semantic matching, to avoid irrelevant materials interfering with review judgments.
Configuration Recommendations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents such as business license applications and vehicle technical files, avoids overly fragmented or overly long chunks affecting retrieval accuracy |
| Overlap Length | 100–150 characters | Ensures semantic coherence between adjacent chunks, prevents loss of cross-chunk information, and adapts to cross-paragraph associated verification needs in file review |
maxToken | 4096 token | Adapts to the context window limits of general large models, while accommodating the content of file materials retrieved in a single batch |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Road transport business license review requires strict matching of material authenticity, to avoid recall of irrelevant documents with low similarity |
| Single Search Limit | Top 10 results | The approval process requires accurate verification of core materials; too many retrieved results will increase review burden |
| Incremental Index Switch | Enabled | Adapts to the daily data update frequency, avoiding significant resource consumption from full index reconstruction |
Three Common Mistakes
- A single file remains in the processing state for a long time during the indexing process. The cause is that the character length of the file exceeds the upper limit of the chunk configuration, or the file format is not supported by the preprocessing module, leading to chunk failure and stagnation.
- Knowledge base priority settings do not take effect, and retrieval still recalls across multiple knowledge bases. The cause is that the multi-knowledge base priority binding switch is not enabled correctly, or the priority configuration is not associated with the current retrieval scenario, causing the system to recall all knowledge base content according to default rules.
- A 60-second timeout or long training state occurs when switching indexes. The cause is that no reasonable batch processing threshold is set during incremental indexing, or the interface response delay of the data source system exceeds the preset timeout limit, causing the task to fail to complete on time.
How to Confirm the Configuration Is Correctly Set
- Upload a standard business license application material, check whether the index progress bar completes within a reasonable time, with no long-term stagnation.
- Configure two test knowledge bases, upload vehicle files and driver qualification materials respectively, set knowledge base priorities and initiate retrieval, confirm that only matching results from the specified knowledge base are recalled preferentially.
- Initiate retrieval targeting a vehicle identification number or practitioner qualification certificate number, verify that the similarity threshold of the retrieved results meets the preset range, and the number of results does not exceed the single search limit.
- Submit new test data, confirm that the index system only synchronizes the new files, without triggering full index reconstruction.
The values provided here are common starting points, and should be evaluated against the samples relevant to the actual use case.
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.