What the data for this category looks like
Urban public transit service inquiry data mainly originates from urban public transit operation management platforms. It includes two categories: static data and real-time data. Static data covers route numbers, stop sequences, first and last service times, fare standards, service complaint channels, and more. Its update cycle is every quarter. Real-time data covers current shift location, estimated arrival time, real-time passenger flow, and more. It refreshes once per minute. Data is stored in structured table format. Fields include unique route code, stop name, timestamp, fare unit (yuan), and no redundant statistical fields.
What constraints these characteristics impose on the "multi-turn dialogues and prompt engineering" link
The quarterly update cycle of static data requires that prompts in multi-turn dialogues prioritize calling the latest static configurations. This avoids returning outdated route or fare information. The 1-minute refresh frequency of real-time data requires re-pulling the latest real-time interface data for each dialogue turn involving shift queries. Do not rely on cached historical data. Structured data fields require prompts to guide users to provide clear route numbers or stop names. This reduces matching errors caused by vague queries. Multi-turn dialogues must retain route or stop information mentioned by users in previous turns. This avoids repeated questions and improves inquiry efficiency. The diversity of data fields requires that context length not be excessive. This prevents confusion of core inquiry content.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|
Reference only; verify against the target FastGPT release and deployment documentation.
| chatId | Bind user session identifiers, set validity period to 30 minutes | Most user public transit inquiries are single sessions; 30 minutes covers most continuous inquiry scenarios, avoids confusion of session context |
| variables | Preset three types of variable templates: route, stop, real-time shift | Core public transit inquiries revolve around these three scenarios; preset templates reduce repeated prompt engineering writing, improve dialogue efficiency |
| Streaming return segment interval | 1–2 seconds per segment | When users receive real-time public transit information, this range aligns with visual perception habits, avoids overly fast or slow return rhythm |
| Dialogue log storage | Enable full recording, export format as CSV | Public transit inquiries may involve service vouchers or complaints; full logs retain complete session records |
Three common mistakes
- Phenomenon: Streaming output uses a fixed 4-second interval, unable to adjust to 1–2 seconds. Cause: Did not specify the streaming return segment interval parameter in session configuration, directly used the platform's default interval setting.
- Phenomenon: The icon of the guest login dialogue window cannot be customized, still displays the default style. Cause: Did not upload a qualified custom icon file in session configuration, did not complete the icon replacement process.
- Phenomenon: Content confusion and logical disconnection occur when splicing two AI dialogue outputs. Cause: Did not bind session context via
chatId, directly spliced unrelated dialogue fragments, leading to conflicting context logic.
How to confirm configurations are set correctly
- Initiate a dialogue that includes a real-time shift query, observe the return segment rhythm to confirm it meets the preset interval requirements.
- Enter the guest login dialogue configuration page, check if the uploaded custom icon is correctly displayed on the window.
- After initiating a multi-turn dialogue, call the dialogue log interface to confirm that the complete historical record of the current session can be obtained, including all turns of questions and replies.
- Initiate two independent multi-turn dialogues, obtain their respective
messagescontexts, confirm that no logical conflicts occur after splicing.
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.