What data for this category looks like
This category of data typically comes from highway maintenance management systems, bridge and tunnel monitoring systems, road patrol systems, and traffic flow monitoring data. Daily generated documents include maintenance plans, patrol logs, defect disposal orders, road construction permits, and road property damage compensation forms. Data is updated daily. Fields include section identifiers, defect types, disposal records, permit numbers, compensation-related information, and attributes such as the specific location and status of associated road facilities.
What constraints these characteristics impose on the "model access and configuration" workflow
Data sources for this category are scattered. Document formats output by different systems vary. When accessing models, it is necessary to adapt to the input formats of multi-source heterogeneous data. The high-frequency daily updated data requires the model to quickly process that day’s patrol logs and defect records to avoid processing delays. Documents contain segmented fields such as section identifiers and defect types. When calling the model, it is necessary to accurately match the business logic associated with the fields to avoid errors in maintenance work order assignment or compliance judgment. Documents related to road construction and road property damage involve business compliance. Model configuration must align with business scenarios to prevent non-compliant judgment results.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of multiple documents such as daily updated patrol logs and defect disposal orders, avoiding truncation of critical business information |
maxTokens | 2000–3000 token | Controls the length of single-round responses to meet content output requirements for maintenance work order assignment and road construction compliance judgment |
quoteMaxToken | 1500 token | Ensures that referenced business document content such as patrol logs and defect records does not exceed context limits, ensuring complete referenced data |
temperature | 0.3–0.5 | Balances the accuracy of generated content and business compliance, avoiding ambiguous or non-compliant results |
toolChoice | auto | Adapts to multi-source heterogeneous data processing, automatically calling tools to complete data integration and analysis |
AIPROXY_API_ENDPOINT | Fill in the official address provided by the platform | Ensures connectivity and stability of model calls, adapting to multi-system access requirements |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Ensures successful identity verification for model calls, protecting call permissions |
Three common mistakes
- An error
tls: failed to verify certificate: x509: certificate signed by unknown authorityoccurs when calling Spark or Tongwen models. Cause: Correct API endpoint certificate verification rules are not configured, or the used self-signed certificate is not imported into the system trust list. - Locally split vector documents cannot be called normally after being uploaded to the server. Cause: The vector model embedding dimensions used locally and on the server are not aligned, or the document format does not meet the model input requirements of the server.
- After configuring model redirection rules, the actual called model does not match the expected one. Cause: The mapping relationship of the redirection rules is configured incorrectly, and the target model is not specified correctly.
How to confirm configurations are correct
- Initiate a model call test. Confirm that the returned results contain valid content for maintenance work order assignment and road construction compliance judgment, with no truncation or format abnormalities.
- Verify the model redirection rules. Initiate a specified model call, confirm that the actual called model matches the configured redirection target.
- Upload locally split vector documents. Initiate a retrieval on the server, confirm that the corresponding documents can be retrieved normally, with no errors related to format or dimension mismatch.
- View the API call logs. Confirm that no errors of the type
tls: failed to verify certificateare present, ensuring that certificate verification is completed.
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.