What this category of data looks like
Data for urban public transit service inquiries and replies originates from three primary sources: public transit operation scheduling systems, line operation ledgers, and citizen convenience service platforms. Real-time data, including current route location and estimated arrival time, updates at minute-level intervals. Static data such as line fares and station facility details updates on a quarterly cycle. This data includes both structured fields and unstructured documents. Structured fields include route numbers, station names, first and last service time windows, with fares denominated in yuan. Unstructured content includes route adjustment announcements, lost and found notices, and accessible service descriptions.
Constraints on model access and configuration
Minute-level real-time route data updates require limiting model response durations. This ensures returned information aligns with current operational status. Quarterly updates for static line and fare data require knowledge base synchronization configurations to match this cycle. This prevents knowledge base content from becoming outdated. Structured fields have clear units. Model-generated replies must follow unit rules strictly to avoid matching errors. Short unstructured content like service announcements and lost and found notices requires context configurations optimized for short text processing. This avoids unnecessary long-context resource usage.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the short-text dominant context needs of transit inquiries, balancing information completeness and call costs |
maxTokens | 2000–3000 token | Ensures reply content covers complete outputs for typical inquiries such as route queries and fare explanations |
quoteMaxToken | 1500 token | Limits the length of referenced knowledge base content, avoiding introduction of redundant unrelated transit operation data |
temperature | 0.3–0.5 | Reduces randomness, ensuring accuracy of factual replies for route and fare information |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Adapts to FastGPT v4.17.0 proxy interface specifications, ensuring proper routing of model requests |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Completes identity verification for model calls, ensuring secure interface access |
Three common configuration errors
- Phenomenon: Model calls return a 60000ms timeout error, with the interface returning status code 504. Cause: Timeout configurations are not adjusted for the latency requirements of real-time transit data inquiries. The default timeout duration is insufficient to cover request processing for real-time route queries.
- Phenomenon: The requestid of model requests cannot be obtained via logs or the interface, making it impossible to locate the cause of abnormal inquiry replies. Cause: FastGPT request log tracking configuration is not enabled, and return and recording of requestid are not activated.
- Phenomenon: Batch execution nodes complete the process during online debugging, but fail to finish execution via API calls. Cause: Concurrency thresholds for batch nodes are not correctly configured during API calls, or context parameters consistent with those used during online debugging are not passed. This leads to interruption of batch task execution.
How to confirm configurations are properly set
- Submit a typical public transit inquiry, such as "What are the first and last service times for Route XX". Check if the reply content matches the latest data in the knowledge base.
- View model call logs to confirm there are no format errors in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations. - Test inquiries that require longer processing times, such as transfer queries across multiple routes. Verify that timeout configurations meet business requirements.
- Check the call results of batch execution nodes. Confirm that API call parameters match the configurations used during online debugging.
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.