What this category of data looks like
Data for petroleum refining scenarios comes from DCS/APC control systems, LIMS laboratory information management systems, tank farm metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Daily generated documents include operating procedures, production plans, material balance sheets, distillate export quality reports, interlock logs, shift handover records, and change management documents. Data update frequencies cover three categories: real-time, shift-based, and daily. Fields include raw material properties, unit load, product fractions, incident alert codes, disposal records, and more. Units involve industrial standard units including temperature, pressure, flow rate, and material proportion.
What constraints these characteristics impose on the model integration and configuration phase
Multi-source heterogeneous data sources in petroleum refining scenarios require the model integration step to support interface formats of different industrial systems to avoid adaptation errors. Differentiated update frequencies (real-time, shift-based, daily) require configuration of corresponding data synchronization cycle parameters to ensure timeliness of documents and reports. Processing needs for multiple types of professional documents require configuring sufficient context windows to accommodate long text content. Standardized professional fields and industrial units require association with preset business knowledge bases to ensure generated content complies with industry standards. Business logic of multi-unit coupling requires configuring priority rules for tool calls to ensure generated content matches full-process collaboration requirements.
How to set the configurations
This document is based on FastGPT v4.17.0 configuration guidelines.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to processing needs for long petroleum refining documents such as material balance sheets and shift handover records, avoiding truncation of critical business information |
maxTokens | 2000–3000 token | Limits output length for single-round incident report drafting, ensuring content focuses on specific disposal plans |
quoteMaxToken | 1500 token | Restricts the length of referenced source data, ensuring relevance between generated content and original business data |
| Workflow/API tool selection | auto | Automatically selects tool calls, adapting to multi-unit coupling process document drafting scenarios and automatically associating business data from multiple systems |
temperature | 0.2–0.4 | Reduces randomness of generated content, ensuring standardization and consistency of process documents and incident reports |
AIPROXY_API_ENDPOINT | Fill in the actual address based on the deployment environment | Adapts to interface addresses for local deployment or commercial edition channels, ensuring normal model connection |
Three common configuration mistakes
- 404 status code (no body) error occurs. Cause:
AIPROXY_API_ENDPOINTis configured incorrectly, does not point to the correct model interface address, or the interface does not have open access permissions. - Only rearranged model options are displayed for channel models after commercial edition deployment. Cause: Access credentials for the language model are not configured correctly, or permission settings for the corresponding model are not enabled, causing the system to fail to recognize available language model types.
- Local deployment of ollama and Docker edition FastGPT cannot establish a connection. Cause: The ollama interface address is not configured as an address accessible to Docker containers, or network permissions for the local port are not opened, causing connection timeout or failure.
How to confirm the configuration is complete
- Call the test interface to generate a simulated incident report, check whether the output content includes petroleum refining-specific fields such as unit load and incident code.
- View the channel model list, confirm that all configured language models are displayed in the optional list, with no missing or abnormal status.
- Test the data synchronization function, verify whether business data pulled from DCS, LIMS and other systems can be properly associated with model-generated documents.
- View system logs, confirm that there are no error messages such as model call timeout or interface connection failure, which meet the configured timeout threshold requirements.
The values provided are common starting points and should be measured against the reader's own samples.