What the Data for This Category Looks Like
Data related to bidding and quotations for petroleum refining comes from DCS/APC control systems, LIMS laboratory information management systems, tank farm metering systems, crude oil evaluation reports, equipment management systems, and energy management systems. Daily supporting documents include operating procedures, production plans, material balance sheets, distillate export quality reports, interlock ledgers, shift handover records, and change management documents. Data update frequencies cover real-time, per-shift, and daily. Fields include crude oil property parameters, unit load indicators, product quality standards, and more. Access must support multi-source heterogeneous data with non-uniform formats.
Constraints on Model Access and Configuration
Multi-source heterogeneous data sources require the model access layer to support unified parsing and format adaptation across systems, to avoid field mismatches or data loss. Data sources with different update frequencies require layered context loading rules. Real-time data must be loaded first to ensure quotation timeliness. Batch data from per-shift and daily sources must support bulk import. The process parameters involved in bid documents and quotations are numerous and professional, requiring the model context to cover complete business document fragments, while limiting interference from irrelevant data to avoid generated content deviating from the petroleum refining business scenario. Additionally, the rigor of data requires that model calls ensure permission security to prevent leakage of sensitive process data.
Configuration Settings
This configuration is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the complete context of multi-source documents for petroleum refining operations, avoiding truncation of process data |
maxTokens | 2000–3000 token | Adapts to the conventional output length of bid documents and quotations, meeting the requirements for complete output of professional content |
quoteMaxToken | 500–800 token | Limits the length of referenced process data fragments, ensuring the accuracy of referenced content |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring the rigor and professionalism of bid documents and quotations |
| Workflow/API tool selection | auto | Supports automatic tool calls to process multi-source heterogeneous data, adapting to the call logic of the qwen3 series models |
AIPROXY_API_ENDPOINT | Enterprise-specific proxy address | Ensures data transmission security and controllability of call permissions |
AIPROXY_API_TOKEN | Enterprise-specific access token | Verifies call permissions, preventing unauthorized access to sensitive business data |
Three Common Mistakes
- An interface returns a 401 Unauthorized error, caused by incorrect
AIPROXY_API_TOKENconfiguration, failure to fill in a valid exclusive token, or an expired token. - Generated bid documents lack key process parameters, caused by an excessively small
maxContextsetting that cannot load complete crude oil evaluation or unit operation data. - Detailed logs cannot be viewed after a call failure, caused by not enabling the retention configuration for API call logs, making it impossible to locate specific abnormal links in OneAPI calls.
How to Confirm Proper Configuration
- Test the interface corresponding to
AIPROXY_API_ENDPOINT, verify the validity ofAIPROXY_API_TOKEN, and check for normal return status codes. - Import a complete petroleum refining process document, confirm that the
maxContextsetting fully loads document content without truncation or loss. - Generate a quotation fragment containing process parameters, check that
maxTokensandquoteMaxTokensettings meet output length requirements with no premature truncation. - Adjust the
temperatureparameter, confirm that the rigor of generated content aligns with professional requirements for petroleum refining operations, with no overly divergent content.
The values provided are common starting points and should be evaluated against the reader's own samples.