What data looks like for this category
This category’s data primarily comes from DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring devices, energy metering systems, and raw material tank data. Daily documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift logs. Data update rhythms fall into three categories: real-time, per shift, and daily. Document fields cover process control parameters, alarm thresholds, test results, work permit information, and more. Units use common industrial measurement standards such as pressure, temperature, flow, and material concentration.
What constraints these characteristics impose during model access and configuration
Differentiate call frequencies for different data sources due to varying update rhythms, to avoid overusing real-time interfaces and excessive resource consumption. Adapt extraction logic for diverse document types and structural differences, to ensure key fields such as interlock parameters and alarm thresholds are fully identified. Clarify permissions and interface formats for each data source during configuration, as multi-system linked data sources prevent cross-system data synchronization errors. Adjust context-related parameters to cover critical time windows, since abnormal report drafting requires fast access to the latest alarm data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers shift-level process data and abnormal records, ensuring sufficient context information for the process |
maxTokens | 4000 token | Limits the generation length of a single abnormal report, avoiding exceeding the standard output length of chemical documents |
quoteMaxToken | 1500 token | Prioritizes extracting core document fragments such as process index cards and interlock records, ensuring accuracy of quoted content |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring that descriptions of process parameters and alarm disposal procedures comply with industry standards |
AIPROXY_API_ENDPOINT | Fill in the intranet address of the deployment node | Adapts to the network environment of internal multi-system linkage in enterprises, ensuring data transmission security |
AIPROXY_API_TOKEN | Bind a dedicated call key | Restricts unauthorized access, meeting data security requirements in industrial scenarios |
Three common configuration mistakes
- Phenomenon: Local testing produces normal results, but external release channels cannot call generated content, returning a 403 status code. Cause: The access whitelist for
AIPROXY_API_ENDPOINTis not configured correctly, and the corresponding port is not open for external calls on the intranet deployment node. - Phenomenon: After upgrading to FastGPT v4.17.0, model calls frequently time out and generated results are truncated. Cause: The
maxTokensparameter setting exceeds the default limit of the new system version, and the value range was not adjusted based on the standard length of chemical documents. - Phenomenon: Generated abnormal reports do not include key information from interlock bypass records. Cause: The
quoteMaxTokenparameter is set too small, failing to cover the field length of this type of document, leading to failure to recall core data.
How to confirm configuration is complete
- Check the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto ensure they match the actual parameters of the deployment environment. - Initiate a simulated call, input test text containing process index cards and alarm records, and verify that the generated results include core fields.
- Adjust the value range of the
temperatureparameter, compare generated content across different configurations, and confirm compliance with industry expression standards. - Review system logs to confirm there are no timeout or permission error messages during model calls.
The values provided are common starting points and should be measured against local samples.