What data for this category looks like
The data sources involved in this category include special equipment safety supervision systems, inspection and testing institution systems, user account books, and operator certificate databases. Daily documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Each document contains fields such as equipment number, registration date, inspection cycle, maintenance unit, and operator qualification number. The update frequency is once per month. The data covers all stages of the entire equipment lifecycle, and fields and units must match the standardized formats used in supervision workflows.
Constraints on model access and configuration from these data characteristics
These data characteristics create multiple constraints for model access and configuration. Multi-source heterogeneous data sources require compatibility with the output formats of different systems during access, to avoid field mapping errors. The monthly update frequency requires the knowledge base synchronization cycle to match the data update rhythm, ensuring the timeliness of Q&A references. Diverse document types and structured fields require the model to accurately identify key information from different documents, and avoid confusing device attributes with policy clauses. High-risk supervision attributes require stability and accuracy in model calls, to avoid regulatory risks caused by incorrect replies due to improper configuration.
How to configure the parameters
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Documents related to special equipment often include multiple inspection reports and maintenance records, which have long length. Sufficient context must be covered to ensure coherent Q&A logic |
maxTokens | 2000–3000 token | Q&A for policy and regulatory basis requires clear compliant items as output, to avoid truncating key policy references or device data |
quoteMaxToken | 1500 token | Policy clauses and device document fragments must be accurately referenced. Control the length of referenced content to ensure information readability |
toolChoice | auto | Require the model to independently choose whether to call the document retrieval tool, to match policy references with actual device data |
temperature | 0.1–0.3 | Policy Q&A requires strict accuracy, to avoid generating vague replies that deviate from supervision basis |
AIPROXY_API_ENDPOINT | Terminal address calibrated via platform testing | Ensure stable call links, and adapt to the dedicated network environment of supervision scenarios |
Three common configuration errors
- Phenomenon: Global variables modified during a conversation cannot be displayed correctly in subsequent replies. Cause: Context retention rules are not properly configured, causing updated variables to not be included in the conversation context scope.
- Phenomenon: Model call duration exceeds 600 seconds. Cause: Reasonable values for
maxContextormaxTokensare not set, causing the model to process excessive redundant context data. - Phenomenon: The
temperatureparameter cannot be set. Cause: Advanced parameter editing mode is not enabled in the model call configuration interface of FastGPT v4.17.0, causing the temperature parameter to not be visible in the operation interface.
How to verify successful configuration
- Submit a test query that includes a specific equipment number and inspection cycle, check whether the model's returned content accurately references the corresponding document fragments and policy basis.
- Run a multi-turn conversation test, verify whether global variables are passed and updated correctly, and can be reflected normally in subsequent conversation replies.
- Trigger a timeout test, confirm that the model call duration falls within the constraint range set by
maxContextandmaxTokens. - Upload image files of paper documents related to equipment, verify whether the model can correctly recognize the text information in the images and associate it with corresponding policy clauses.
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.