What the Data for This Category Looks Like
Business data for urban public transit mainly comes from in-vehicle smart terminals, dispatch command systems, ticket settlement platforms, and station operation and maintenance systems. Data updates follow three rhythms: real-time, daily, and monthly. Real-time data includes current vehicle location, passenger count, and operation status, updated every 10 to 30 seconds. Daily data covers same-day ticket revenue, shift completion rate, and station inspection records, and is summarized by 24:00 on the same day. Monthly data includes line operation cost and passenger flow fluctuation statistics, and is generated by the 5th of the next month. Single data entries include fields such as line number, station name, vehicle identification code, work order number, and processing progress. Some fields have clear units, such as cruising range (km) and response duration (seconds).
Constraints Imposed on the Model Access and Configuration Link
High-frequency updates of real-time passenger flow and vehicle location require model call response latency to match the data refresh rhythm, avoiding returned outdated information. Dispersed multi-source business data requires configuring cross-system data call permissions during the access phase, ensuring dispatch, ticket, and operation data can be called uniformly. Long-text monthly summary operation and maintenance statistical reports require adjusting model context window parameters to adapt to batch data analysis requirements. Unit differences across different fields require adding format verification rules during configuration, preventing tool call failures caused by unit mismatches. Internal public transit work orders involve operation-sensitive information, so the access configuration requires adding a pre-verification step for data desensitization.
How to Set the Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of multi-source business data for public transit, avoiding loss of key dispatch and work order information due to context overflow |
maxTokens | 2000–3000 token | Matches the answer length requirements of internal office scenarios, ensuring that work order processing suggestions and passenger flow analysis results can be fully output |
quoteMaxToken | 1500 token | Used when quoting original database text fragments, limits the length of a single quoted data segment, aligning with the scale of single entries in public transit operation records and ticket reports |
toolChoice | auto | Supports automatic tool selection, adapting to dynamic requirements of multiple scenarios such as dispatch queries and work order processing |
temperature | 0.3–0.5 | Reduces answer randomness, ensuring the rigor of operation and maintenance suggestions and schedule adjustment plans |
AIPROXY_API_ENDPOINT | Fill in the API access address of the local or cloud service provider according to the deployment environment | Adapts to the model call link of locally deployed FastGPT, ensuring data transmission complies with internal network security requirements |
Three Common Configuration Mistakes
- Phenomenon: A 403 status code (no body) error is reported during testing after adding a model to locally deployed FastGPT. Cause:
AIPROXY_API_ENDPOINTor the corresponding model's key parameter is not configured correctly, leading to identity verification failure or blocked network link. - Phenomenon: The target model does not appear in the channel configuration list, and the access process cannot be completed. Cause: The access address and key of the corresponding model are not added in the FastGPT model management page, or the deployed version does not support the call protocol of the model.
- Phenomenon: After calling MYSQL data via Function CALL, the large model response does not quote original database text fragments. Cause: The
quoteMaxTokenparameter for limiting quoted length is not configured, or the original text reference switch after tool call is not enabled, preventing the model from extracting and displaying original database content.
How to Confirm the Configuration Is Correct
- Enter the FastGPT model test page, enter a query related to public transit dispatch, and check whether the returned result contains correct line and station information.
- Conduct multi-round conversation tests to confirm that context information is correctly retained and the response content conforms to conversation logic.
- Call the Function CALL tool to test database queries, and check whether the returned result contains original database text fragments.
- View the FastGPT log panel to confirm that the status code of the model call request is 200, with no error messages.
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.