What This Category of Data Looks Like
This category of data comes from engineering quality and safety supervision systems, construction site real-name management platforms, supervision logs, and test reports. The update frequency is weekly. Daily processed documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Each individual document contains fields such as project number, participating unit information, inspection item details, rectification requirements, and closed-loop status. Document fields vary across different projects, and there is no unified global template.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources and fixed update frequency require that multi-turn dialogue call logic be split by data source type. Prompt engineering must clearly specify the query scope of corresponding documents. Inconsistent document fields require that prompt engineering include field verification and adaptation logic, to ensure extracted information matches the document structure of the current project. The most time-consuming links — material inspection, rectification tracking, and acceptance verification — require that multi-turn dialogue be split by business link to avoid processing confusion from overly long context. The context time range must be limited to the past 7 days to prevent expired data from being included.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chatId | Generate a unique value based on project + inspection batch | Matches the batch management logic of engineering quality and safety supervision, ensuring coherent conversation context for the same batch |
messages | Limit single-turn context to document fragments from the past 7 days | Corresponds to the weekly data update frequency, avoiding calls to expired information |
variables | Pre-set project number and participating unit list as global variables | Adapts to inconsistent document fields, quickly matching exclusive documents for the current project |
| Maximum context length | 800–1200 characters | Controls single-turn dialogue information density, avoiding exceeding processing limits |
| Single dialogue return count | Top 3 entries | Focuses on core documents for the current link, reducing interference from invalid information |
| Prompt optimization | Follow actual measurement calibration | Adapts to document structure differences across projects, optimizing the guiding effect of prompt engineering |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: Calling the dialogue interface returns an unAuthChat error, and the apikey configuration is correct. Cause: The data source permission corresponding to the engineering quality and safety supervision system is not bound in the dialogue configuration, resulting in interface authentication failure.
- Phenomenon: Dialogue return content contains line breaks, triggering JSON parameter request failure. Cause: The dialogue return content is not escaped, and direct concatenation into request parameters causes format errors.
- Phenomenon: An error is triggered when uploading files such as supervision inspection records and supervision logs, and text input dialogue works normally. Cause: The parsing rules for the corresponding file formats are not configured, or the file size exceeds the current system's upload limit.
How to Confirm Successful Configuration
- Initiate a test dialogue, enter a specified business question, and verify whether core field information of the corresponding document can be correctly extracted.
- Call the dialogue interface, check whether the returned
messagesparameter contains valid document fragments from the past 7 days, with no expired data. - Upload a standard business document, confirm that core preset information can be normally parsed and extracted.
- Trigger the multi-turn dialogue process, covering the material inspection, rectification tracking, and acceptance verification links in sequence, and confirm that the context is coherent and the logic is clear.
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.