What Data for This Category Looks Like
The data for this category originates from three official business systems: 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 processed documents include business license applications, vehicle technical files, driver qualification materials, safety production management systems, and road transport business licenses, among others. A single review file package includes five core field categories: main body qualifications, vehicle information, personnel qualifications, site conditions, and safety systems. There is no unified standardized template format, and some fields require cross-system associated verification to ensure authenticity.
What Constraints These Characteristics Impose on the Model Integration and Configuration Link
Since data originates from multiple official systems, parsing rules adapted to different data source formats must be configured to avoid deviations in cross-system field extraction. Daily updated business data requires the model integration link to support scheduled synchronization verification, preventing expired data from participating in review workflows. The mixed processing scenario for multiple document types requires configuration items to balance the length and field priority of different documents, ensuring full review coverage. At the same time, review results are directly linked to license approval and the legal operation of enterprises, so the rigor of model output must be strictly controlled to prevent incorrect judgments caused by improper configuration.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate the complete content of multiple associated review files to avoid truncation of key review information |
maxTokens | 2000–3000 token | Adapts to the standard output length of documents such as review opinions and verification conclusions |
quoteMaxToken | 4000 token | Retains sufficient original reference fragments to ensure traceability of review basis |
temperature | 0.1–0.3 | Reduces output randomness to ensure the rigor and consistency of review conclusions |
AIPROXY_API_ENDPOINT | Fill in the actual endpoint according to the deployed model service | Establishes a valid link for model calls, ensuring requests are correctly delivered to the target service |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding model service | Completes identity verification for model calls to prevent unauthorized access |
Three Common Configuration Errors
- Symptom: An "another model error" prompt is displayed when loading a model. Cause: The
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters are filled incorrectly, or the target model has not been granted corresponding access permissions. - Symptom: Only the default first model is used after deployment, and the custom configured model does not take effect. Cause: The configuration format requirements of FastGPT v4.17.0 are not correctly followed, or there is a deviation in the reading path of the configuration file.
- Symptom: Irrelevant redundant content appears in review results. Cause: The
temperatureparameter is set too high, causing the model output to deviate from rigorous review logic.
How to Verify Successful Configuration
- Execute a curl command to test the connectivity of
AIPROXY_API_ENDPOINT, confirming that the returned result conforms to the standard response format of the target model service. - Upload a standard road transport business license application material, checking whether the model can correctly extract core review fields such as vehicles, drivers, and site conditions.
- Adjust the
temperatureparameter to 0.1 and initiate a test conversation, confirming that the output content does not have subjective divergence and strictly focuses on review key points. - View system operation logs, confirming that there are no records of model call timeouts, parameter parsing failures, or permission errors.
The values provided are common starting points and should be validated against the target deployment’s sample data.
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.