What this category of data looks like
Public transit ledger data comes from on-board smart terminals, public transit dispatch management platforms, and ticket settlement systems. Single operational records are uploaded to the platform in real time. Full aggregated ledgers are generated each day. Each single record includes fields such as shift number, vehicle ID, operational duration, passenger volume, station stop duration, and energy consumption data. Operational duration uses minutes as the unit. Passenger volume uses passenger trips as the unit. Energy consumption uses kilowatt-hours as the unit. Aggregated ledgers are stored and categorized by date and route. Each aggregated document contains statistical data for all shifts on that day.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Real-time operational data and daily aggregated data for public transit ledgers have clear structural differences. Multi-turn dialogue must differentiate between single real-time queries and cross-time-period aggregated queries. Prompts must clearly specify the data type. Ledgers include multiple segmented fields. Without clear limits, the model may mix up the statistical logic of different fields. Cross-time-period or cross-route queries require retaining previously specified query conditions. Multi-turn dialogue context must be bound to a session identifier. This avoids repeated input of route, time period, and other information. Real-time data update rhythm requires dialogue responses to match the data synchronization cycle. This prevents returning outdated old data.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current session | Ensure context association for multi-turn dialogue, avoid cross-session data confusion |
messages | Retain the first 6 dialogue records | Public transit ledger queries usually require reviewing previously specified shifts and time ranges; 6 records are sufficient to cover conventional query contexts |
variables | Preset route number and statistical cycle variables | Quickly call ledger data for corresponding time periods, reduce repeated input |
| Maximum context length | 8000–12000 token | Public transit ledgers contain batch data with multiple fields, enough to accommodate aggregated content for a single shift or multiple days |
| Query optimization | Enabled | Public transit queries often include vague expressions such as "last week's operational data"; optimization can clearly extract the corresponding time period's ledger |
| Single search limit | Top 10 matching records | Single time period records in public transit ledgers usually do not exceed 10, avoid redundant data interfering with generation |
Three Common Errors
- Phenomenon: After calling the text content extraction node in a workflow, multi-turn dialogue context is lost. Previous route or time period queries cannot be continued. Cause: The
chatIdparameter is not bound in the workflow. Each node call generates an independent session. - Phenomenon: Format error prompts are returned when calling the multimodal image dialogue function. Cause: The standard format of public transit ledger images is not specified. Only PNG and JPG format station schedule diagrams and operational report screenshots are supported.
- Phenomenon: The summary report generated by the prompt omits specified fields such as passenger volume or energy consumption data. Cause: Required ledger fields are not clearly listed in the prompt. The model ignores unmentioned statistical items.
How to Verify Correct Configuration
- Initiate a first query. Input "Query the operational ledger for Route 12 at 10 AM today". Initiate a second query: "Supplement the energy consumption data for this time period". Confirm the system continues the previous route and time period information without re-entry.
- Open the workflow editing page of FastGPT v4.17.0. Check the parameter configuration of the text content extraction node. Confirm the
chatIdandmessagesparameters are correctly bound. Confirm preset variables are associated with corresponding ledger fields. - Upload a PNG format screenshot of a public transit operational report. Call the multimodal dialogue function. Confirm the returned text extraction result matches the shift, passenger volume, and other content in the screenshot.
- Edit the prompt. Clearly require generating a summary report including shift number, operational duration, and passenger volume. Confirm the generated report includes all specified fields.
The values provided here 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.