Data Overview for This Category
Core data sources for the basic chemical continuous production category include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring modules, energy metering systems, and raw material tank management modules. Data update frequencies are divided into three categories: real-time, shift-based, and daily, corresponding to process parameters, equipment vibration values, shift reports, and other content. Daily documents cover operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift reports. Fields include temperature, pressure, flow rate, vibration amplitude, and others, with corresponding units such as degrees Celsius, pascals, cubic meters per hour, millimeters per second, and more.
Constraints Imposed by These Characteristics on Model Access and Configuration
This category has high real-time data requirements, and some process parameters must respond within seconds. Model access must therefore adapt to low-latency call links. The update frequencies of multiple data types vary widely, so a hybrid strategy of incremental synchronization and full update must be configured to avoid redundant requests. Document types include structured parameter tables, semi-structured trend charts, and compliance records. The model must support parsing and association of multi-format text. Documents related to strong interlock control must strictly limit the context scope to prevent generation of disposal suggestions that do not comply with safety specifications.
Configuration Settings
This page is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets the context carrying requirements for long documents such as DCS trends and process index cards |
maxTokens | 2000–3000 token | Meets the requirement for detailed responses in maintenance Q&A, and avoids truncating critical disposal steps |
quoteMaxToken | 1500 token | Prioritizes referencing compliance document fragments such as interlock bypass records and special work permits |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools to handle equipment inspection data queries |
temperature | 0.2–0.4 | Reduces random output, ensuring maintenance recommendations comply with the rigor of operating procedures |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Connects to locally deployed compatible model services |
AIPROXY_API_TOKEN | Fill in according to the key generated by the service | Completes identity verification for the model service |
Three Common Misconfigurations
- The
AIPROXY_API_TOKENfield displays as empty in the interface, causing model call failures. This occurs when the OneAPI key is not correctly imported, or when special characters in the key format are not escaped. - A format incompatibility error is returned during model calls. This happens when the Qwen model deployed via Xinference is not adapted to FastGPT's model call protocol, leading to mismatched request parameters.
- Retrieval results only return a small number of non-critical documents. This is caused by not setting a reasonable
quoteMaxTokenvalue, or having an overly narrow context length limit that cannot load core data such as complete process index cards and interlock records.
How to Verify Successful Configuration
- Initiate a simulated equipment inspection query. Confirm returned results include operating procedures, interlock disposal steps, and other content matching this category, to verify correct context loading.
- View the model call log to confirm that the request parameters for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not missing, and the return status code is 200. - Test a multi-turn question and answer scenario. Confirm the model can associate equipment status data from historical conversations, to verify correct context transfer.
- Upload a single DCS trend document. After retrieval, confirm returned reference fragments include correct timestamps and parameter values, to verify text parsing adaptability.
The values provided here are common starting points and should be measured against local samples.