What data for this category looks like
Data sources for this category include DCS/APC systems, LIMS systems, tank farm metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Data updates follow three cycles: real-time, shift-based, and daily. Daily documents include operating procedures, production plans, material balance reports, distillate export quality reports, interlock ledgers, shift handover records, and change management documents. Fields involved include equipment vibration values, process temperature and pressure, crude oil physical property parameters, material flow rates, and other items with clear industry-specific units.
What constraints these characteristics impose on model access and configuration
Data sources for this category are scattered, and update frequencies vary significantly. Real-time process data and daily reports require different context processing logic. This creates clear requirements for the context window of model access. There are diverse document types with large structural differences. Configure splitting rules adapted to different document formats to avoid context fragmentation. Industry-specific fields and units require the model to prioritize semantic mapping of industry terms during access. This ensures the rigor of Q&A results. The business characteristic of multi-device coupling also requires the configuration link to support cross-system data association call constraints.
How to set the configuration
This configuration is formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets the context requirements for splicing multiple documents in the petroleum refining industry, covering complete associated information for multiple document types such as operating procedures and interlock ledgers |
maxTokens | 2000–3000 token | Matches the single-response length requirement for equipment inspection Q&A, avoiding exceeding interface return limits |
quoteMaxToken | 1500–2000 token | Ensures recalled industry document fragments provide sufficient detail for equipment fault troubleshooting |
temperature | 0.2–0.4 | Reduces random output probability, ensuring the rigor and accuracy of equipment maintenance suggestions |
AIPROXY_API_ENDPOINT | Fill in according to the official interface of the selected model service provider | Adapts to the call paths of different models, ensuring requests are correctly delivered to the model service |
AIPROXY_API_TOKEN | Fill in with the valid key generated by the service provider | Completes identity verification for model calls, ensuring interface access security |
Three common errors
- Model mapping configuration fails to take effect, and the interface displays that parameters are not synchronized. The cause is that the association binding between the model and the business knowledge base is not completed on the configuration page. Only the interface key is filled in, and the mapping rule for the corresponding knowledge base is not specified.
- An error is returned when calling the model. Exceptions occur in both commercial large model services and locally deployed open source model images. The cause is that the interface path configured in
AIPROXY_API_ENDPOINTdoes not match the actual deployment address of the model, orAIPROXY_API_TOKENhas missing characters or is expired. - After the business system is connected, chat records of different users are not isolated. The cause is that the user identification parameter is not passed during API calling, so the conversation context is not split by user dimension.
How to confirm the configuration is complete
- Call the model test interface, input industry terms related to equipment inspection, and check whether the returned results contain correct process parameters and equipment names.
- Upload an operating procedure document from the petroleum refining industry, test the document parsing and recall function, and confirm that the matching degree between the recalled document fragments and the question meets expectations.
- Pass different user identification parameters to initiate multi-round conversations, and check whether the conversation context is stored independently by user dimension.
- Check the interface return logs to confirm that
AIPROXY_API_TOKENverification is passed, and there are no permission error or parameter missing prompts.
The values provided are common starting points and should be measured against the reader's own samples.