What the Data for This Category Looks Like
The data sources for this category are the transit group’s internal scheduling platform, employee management system, vehicle repair ledger, and line operation archives. The update schedule is as follows: line schedules are updated daily at midnight, repair work orders are submitted and synced in real time, and employee attendance data is synced each evening. Document structures include structured tables and semi-structured work order texts. Fields covered include line number, shift time slot, employee ID, fault type, and processing progress. Units are defined as: line number uses "line", shift time slot uses "minutes", and employee ID uses "unit".
Constraints for Multi-turn Dialogue and Prompt Engineering
Daily updates to line schedules require multi-turn dialogue contexts to only call schedule data valid on the current day, to avoid referencing expired information. High-volume real-time submission of repair work orders requires setting a reasonable session validity period for dialogue contexts, to prevent redundant old data from interfering with current requests. Clear structured fields require prompts to first extract core identifiers such as line number and employee ID from the session, then match corresponding knowledge base content. The presence of semi-structured work order texts requires multi-turn dialogue to support automatic classification and field extraction for non-standard work order content, to ensure accuracy in subsequent processing.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|
Reference only; verify against the target FastGPT release and deployment documentation.
| chatId binding rule | Bind employee ID and session ID | Ensures multi-turn dialogue only associates historical requests from the current employee, prevents cross-employee session confusion |
| messages context truncation threshold | Retain the most recent 10 conversation messages | Adapts to the concise, fast interaction style of transit work order processing, filters out expired non-core conversation content |
| variables parameter injection | Automatically bring in the current day's line schedule ID | Ensures prompts always call the latest line data, no manual configuration updates required |
| Prompt template configuration | Add line number and employee ID extraction instructions | Matches the structured field characteristics of internal transit data, improves information matching accuracy |
Three Common Misconfigurations
- Symptom: The conversation log tagging function cannot associate the corresponding work order ID. Cause: No work order ID is injected into
variablesas a context parameter, causing tagging to fail to match specific business data. - Symptom: AI-generated replies are directly streamed to the page without being processed by a text splicing component. Cause: No output interception rules are configured in the workflow, and the output port of the AI dialogue node is directly bound.
- Symptom: The second AI dialogue node cannot read the output content of the first node. Cause: The result of the first dialogue is not passed to the second node as context via
messages, causing session context loss.
How to Verify Correct Configuration
- Initiate a test conversation that includes a line number and employee ID, check if
variablesautomatically imports the corresponding parameters. - Initiate multi-turn interaction, confirm that the conversation context only retains the most recent 10 messages with no expired historical content.
- Trigger the AI dialogue node in the workflow, check if the output is passed through the text splicing component before returning to the specified port.
- View the conversation log, confirm that the tagging function can associate business data and work order information corresponding to the current session.
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.