What the data for this category looks like
Railway operation ledger data comes from on-site operation terminals, dispatching command platforms, passenger ticketing systems, freight management systems, and line maintenance systems. Data update rhythms fall into three categories: dispatching-related data is updated in real time; equipment inspection records are updated after a single operation is completed; freight and ticketing ledgers are updated per shift or daily. Documents mainly use structured fields, including line number, train ID, operation node, completion duration, exception code and other fields. Units include kilometers, tons, hours, minutes and others. The length of a single ledger document varies with operation scale, and usually contains tens to hundreds of structured records.
What Constraints These Characteristics Impose on the Multi-turn Dialogue and Prompt Engineering Link
The large number of structured fields and fixed units require prompts to clearly specify field extraction rules and unit retention requirements, to prevent the model from outputting incorrect fields or units. Real-time data and static ledgers coexist, so multi-turn dialogue must pass historical context to ensure the model can follow the query scope from the previous round. The differences in update rhythms of different data require prompts to distinguish the logic of real-time calls and static calls, to avoid using expired data. Railway operation terminology is highly specialized, so prompts for multi-turn dialogue must include definitions and constraints of industry terms to improve the model's understanding accuracy. In addition, some ledgers involve sensitive information, so prompts must clearly specify filtering rules to avoid leakage of classified content.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the current user's railway operation job number as the unique identifier | Ensure each user can only view ledger data within their permissions, preventing cross-user data leakage |
messages | Retain the last 10 rounds of dialogue context, with no single round of context exceeding 2000 characters | Adapt to the structured content volume of railway ledgers, avoiding model understanding deviations caused by overly long context |
variables | Pre-set two global variables: line number and train number range | Quickly narrow the query scope, reduce invalid data recall, and improve multi-turn dialogue efficiency |
| Maximum context length | 8000–12000 token | Adapt to the content length of multi-field railway ledgers, avoiding truncation of key operation records |
| Question Optimization | Enabled | Optimize understanding of railway operation terminology, improving response accuracy for multi-turn follow-up questions |
| Single dialogue timeout | 600 seconds | Adapt to the recall and processing duration of railway ledger data, avoiding timeouts caused by excessive data volume |
Three Common Misconfigurations
- Symptom: Multi-turn dialogue fails to follow up on previous round's operation node queries, and the model returns irrelevant general ledger content. Cause: The
messagesparameter is not configured correctly, and the context from the previous round of dialogue is not passed, causing the model to lose historical operation information. - Symptom: After running a workflow, replies from all
<ai dialogue>nodes appear in the final chat conversation. Cause: No output hiding rules are configured for the nodes, and all node outputs are added to the dialogue display list by default. - Symptom: All users see identical ledger historical conversations after logging in, and cannot distinguish individual operation records. Cause: The
chatIdparameter is not bound correctly, and a globally unified session ID is used, resulting in shared session context.
How to Verify Successful Configuration
- Initiate dialogue using different railway operation job numbers, check the isolation of session data, and confirm that each user can only view ledger content within their permissions.
- Launch consecutive follow-up queries, check whether the model can follow the query conditions from the previous round, for example, querying subsequent operation nodes based on the previous round's train ID, to confirm that the
messagesparameter configuration takes effect. - Run a workflow containing multiple AI dialogue nodes, check that the specified hidden node outputs are not displayed, and the replies of other nodes are displayed normally.
- Trigger a query containing a large amount of structured ledger data, check that the dialogue completes the response within the set timeout period, and no timeout error occurs.
The values provided here are common starting points, and should be evaluated 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.