Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Core data for this category comes from highway maintenance management systems, bridge and tunnel monitoring systems, road administration patrol systems, and traffic flow monitoring data. The update frequency is daily. Daily business documents include maintenance plans, patrol logs, defect disposal orders, road-related construction permits, and road property damage compensation forms. Each document contains unique fields such as section number, defect type, disposal status, and permit number. There is no unified cross-system field standard, so business data from different sources must be linked via conversation context.
What constraints do these characteristics impose on the multi-turn dialogue and prompt word link?
Since data is updated daily, multi-turn dialogue must prioritize calling recent patrol logs and defect disposal data to avoid referencing expired information. Since business document types vary and fields are inconsistent, prompt words must clearly define information extraction rules for different scenarios including maintenance work orders, road-related construction, and road property compensation. Since data comes from multiple independent systems, multi-turn dialogue must guide users to provide unique identifiers such as section numbers and permit numbers to accurately integrate cross-system business data. The most time-consuming links in this category — patrol, work order scheduling, and road-related processing — require multi-turn dialogue to guide users to supplement necessary information step-by-step according to business processes, reducing ineffective interactions.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | First 30 conversation histories | Matches the average daily business interaction volume of this category, avoids redundant data occupying session resources |
variables bound fields | Section number, defect ID, permit number | Links cross-system business data, ensures multi-turn dialogue can accurately match corresponding records |
messages context rules | Retain conversation content from the past 24 hours | Matches the daily data update frequency, avoids referencing expired patrol and maintenance data |
chatId session identifier | Bind the unique number of the current job | Distinguishes session contexts for different sections and different businesses, prevents data confusion |
| Prompt output constraints | Output according to the field order of maintenance plans and patrol logs | Standardizes AI reply formats, matches the standard structure of business documents |
| Session timeout setting | 600 seconds | Adapts to the response time of cross-system data retrieval, prevents session interruptions mid-process |
Three common misconfigurations
- Phenomenon: Multi-turn dialogue fails to associate defect disposal data for the target section, and reply results do not match actual business conditions. Cause: No unique identifier fields such as section number are bound in
variables, so the context cannot match cross-system business records. - Phenomenon: Intermediate outputs of AI dialogue in the workflow are directly displayed on the frontend, without processing via a text splicing component. Cause: No dialogue output trigger node is configured, and an unprocessed AI reply module is called directly.
- Phenomenon: The annotation function for conversation logs cannot be associated with corresponding business documents, and annotation content has no actual business significance. Cause: Fields such as permit number and compensation form ID are not bound in the session, so annotations cannot match specific business data.
How to verify proper configuration
- Launch a multi-turn dialogue that includes a specified section number, check whether the historical conversation context for that section is correctly carried in
messages. - Submit a query related to road-related construction permits, verify whether the AI output includes required fields such as permit number and construction section.
- Test launching a new session after an interruption, confirm that
chatIdcorrectly associates previous session data, eliminating the need to re-enter basic information. - After configuring the workflow, trigger the AI dialogue node, confirm that intermediate outputs are not directly displayed, and must be output to the frontend only after processing via a text splicing component.
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.