What the data for this category looks like
Core data for the basic chemical continuous production category comes from DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring modules, energy metering systems, and raw material tank data interfaces. Data update cycles cover three levels: real-time, shift-based, and daily. Daily associated documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift operation logs. Data fields correspond to process control parameters, metered values, equipment status, and work approval information, with no additional custom fields not required for business purposes.
What constraints these characteristics impose on the model access and configuration workflow
Real-time data requirements for this category mean the model access link must support high-frequency data pulling, so configuration must adapt to short-cycle context updates. Structured differences across multiple types of process documents require model configuration to support document splitting and association for different formats. Strict interlock control and hazardous medium properties require that tender and quotation content output by the model strictly matches process parameters, with no deviations. Decentralized data sources require the configuration link to clearly define the call priority of each data module, to avoid irrelevant data interfering with generation results. At the same time, compliance requirements for tender and quotation documents require that all data used for model calls comes from officially authorized system interfaces.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Covers the total content volume of real-time DCS data, shift logs, and process index cards from the past 7 days, ensuring generated content can reference the latest process parameters |
maxTokens | 4000–6000 token | Adapts to the full output length of a single tender document or quotation sheet, preventing content truncation |
quoteMaxToken | 1500–2500 token | Limits the output length of quotation details, ensuring core parameters are clearly displayed |
temperature | 0.1–0.3 | Matches the rigor requirements of basic chemical scenarios, reducing randomness in generated content and preventing deviations in process parameters |
| Workflow/API tool selection | auto | Automatically calls associated process data interfaces, ensuring generated content can reference real-time raw material tank and energy metering data |
AIPROXY_API_ENDPOINT | Fill in the actual deployed model service address, such as http://localhost:11434/v1 | Connects to large model services, adapting to privacy data protection requirements for basic chemical scenarios |
AIPROXY_API_TOKEN | Fill in the key applied for in actual practice | Verifies access permissions for the model service, ensuring the configuration is legal and valid |
This document applies to FastGPT v4.17.0.
Three common misconfigurations
- An uncaught exception is displayed in the interface, and there is no response when calling a local model. The cause is that
AIPROXY_API_ENDPOINTis not correctly configured to point to the local model service address, and the validity ofAIPROXY_API_TOKENis not verified. - The
<think>tag content still appears in the generated tender document. The cause is thattemperatureis not set to a low range suitable for rigorous scenarios, and the model's native thought output configuration is not disabled. - The generated quotation data does not match the actual measured values of the raw material tanks. The cause is that
maxContextis not configured to include real-time tank data, causing the model to call outdated historical data.
How to confirm the configuration is complete
- Call the built-in model test interface of FastGPT, check that the returned response status code is 200, and there are no uncaught exception logs.
- Generate a test snippet of a tender document, check that the output content does not contain
<think>tags. - Import a set of actual process index cards and tank metering data, check that the generated quotation data matches the imported metered values.
- View the model access configuration panel, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave been correctly filled in.
The values provided here are common starting points, and should be evaluated against the reader's own samples.