What the data for this category looks like
Core data for construction engineering quality and safety supervision comes from four sources: engineering quality and safety supervision systems, on-site construction real-name management platforms, supervision logs, and test reports. Daily office and service documents follow standardized structures: supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Data updates occur weekly. Fields include supervision subject, inspection points, rectification requirements, acceptance deadlines, and others. Adapt field formats and units according to the standards for each individual document type.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The weekly data update cycle requires limiting dialogue context to a recently valid range. This prevents expired data from interfering with judgments. The standardized document structure requires clear field extraction rules in prompt engineering. This ensures conversations accurately match fixed formats for documents such as supervision plans and rectification notices. Scattered data sources require isolating dialogue context by project. Conversations for different supervision projects must not cross-call data. Time-consuming tasks such as on-site document verification and rectification tracking require dialogue tools to quickly retrieve historical inspection records and closed-loop rectification data for the target project.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current supervision project | Distinguish dialogue contexts for different projects, avoid cross-project data confusion |
messages | Retain only the last 3 supervision-related conversations, truncate non-core content | Adapt to the weekly data update cycle, control context length to avoid overload |
variables | Inject the current project’s supervision plan ID and rectification ledger number | Accurately call exclusive documents and data for the corresponding project |
| Number of documents recalled per single request | Top 3–5 entries | Balance information completeness and response speed, adapt to retrieval volume for multi-source documents |
| Maximum context length | 8000–12000 characters | Accommodate total information from the last 3 conversations and 5 recalled documents |
| Dialogue history retention duration | 7 days | Match the weekly data update cycle, avoid retaining expired invalid data |
Three Common Misconfigurations
- A JSON request fails when dialogue responses include line breaks. This occurs because line breaks in dialogue content are not escaped. Raw content is directly spliced into the request body, which breaks the JSON format structure.
- An error triggers when uploading standardized documents such as supervision inspection records, but text input dialogue functions normally. This occurs because exclusive document parsing rules for this category are not configured. The system cannot recognize fixed field formats of documents such as hidden danger rectification notices.
- An
Unexpected end of JSON inputerror appears in dialogue. This occurs because themessagesparameter is not properly closed, or the request times out before fully receiving model response content. This issue is especially likely to occur when using lightweight open-source models due to response delays.
How to Confirm Configurations Are Properly Set Up
- Initiate a conversation that includes the specified supervision project number. Verify that returned content can extract supervision plan fields and recent inspection records for the corresponding project.
- Upload a hidden danger rectification notice document. Confirm that the system correctly parses and extracts core fields such as rectification requirements and rectification deadlines.
- Initiate 3 consecutive conversations across different projects. Verify that project contexts bound to
chatIddo not cross-contaminate. - Test sending 10 consecutive dialogue requests. Confirm that no JSON format errors or response timeout issues occur.
The values given 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.