What the Data for This Category Looks Like
Continuous basic chemical production core data sources include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring modules, energy metering systems, and raw material tank sensors. Data update frequencies include three categories: real-time, shift-based, and daily. These correspond to process parameters, shift reports, full daily reports, and similar content. Daily associated documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and more. Fields include standardized industrial parameters such as pressure (MPa), temperature (℃), material flow (t/h), and pollutant concentration (ppm). All entries include time stamps and equipment number identifiers.
What Constraints Do These Characteristics Impose on the Model Access and Configuration Link
Multi-source heterogeneous data formats for this category have significant differences. Time-series DCS data and structured laboratory reports require different parsing rules. Real-time updated process parameters require the model pipeline to support low-latency response. Sensitive documents such as interlock bypass records need permission filtering rules to prevent sensitive information leaks. Data with different update frequencies require differentiated synchronization cycle settings. This ensures the timeliness and accuracy of recalled data. Process content related to hazardous media must undergo compliance verification before model invocation. This prevents generation of analysis results that do not meet safety requirements.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0, and are adapted to the business data characteristics of continuous basic chemical production:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers core relevant content of on-shift DCS trends, laboratory reports, and process index cards, meets context requirements for quality traceability |
maxTokens | 4000–6000 token | Adapts to the standard generation length of quality problem traceability reports, avoids output truncation or redundancy |
quoteMaxToken | 2000–3000 token | Retains complete key fields of laboratory reports and interlock bypass records, ensures completeness of traceability basis |
temperature | 0.1–0.3 | Ensures rigor of quality analysis, reduces divergent expressions, complies with industrial scenario compliance requirements |
AIPROXY_API_ENDPOINT | Exclusive interface address for deployed models | Completes link docking for custom large models, supports access to third-party models |
AIPROXY_API_TOKEN | Valid key obtained from the model service provider | Completes model identity verification, ensures legitimacy of the call link |
Three Common Misconfigurations
- A 500 error occurs when importing interlock bypass records or DCS trend charts: The structured format of industrial time-series documents is not adapted, leading to chunk parsing failure and inability to generate document vectors.
- No response when sending a query after configuring a custom model:
AIPROXY_API_ENDPOINTis filled incorrectly orAIPROXY_API_TOKENis invalid, and valid connection of the model link is not completed. - Generated analysis results contain <think> tags: Model output cleaning rules are not configured, native thinking process content of third-party models is directly received, and no pre-filtering is performed.
How to Confirm Configuration Is Complete
- Fill in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, then send a model test request to verify that the interface returns normally without errors. - Import a single process index card or laboratory report, confirm that document parsing is normal, and complete content can be viewed in the recall list.
- Send a targeted quality problem traceability query, verify that the returned result contains key fields of corresponding process parameters, laboratory data, and work records.
- Adjust the
temperatureparameter to a reasonable range and send a test, confirm that the generated result has rigorous logic and no irrelevant divergent content.
The values provided are common starting points and should be measured against the reader's own samples.