What this category of data looks like
Data for this category covers multiple source types. DCS/SIS systems collect real-time process parameters. Online analyzers and LIMS systems output laboratory and quality inspection data. Equipment status, energy metering, and raw material storage tanks synchronize equipment operation and material storage information. Daily documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift reports. Data update frequencies include real-time, per shift, and daily. Most fields have clear units, such as temperature (℃), pressure (MPa), and material storage (t).
Constraints on the "model access and configuration" link from these characteristics
Multiple heterogeneous data sources and varied update frequencies require models to adapt to inputs with different timelines. Real-time process data and daily shift reports must not be processed using the same rules. Diverse document types cover structured indicators, trend chart text, and approval documents. Configuration must support retrieval and parsing across document types. Strong timeliness of real-time process parameters and interlock records requires the overall latency of model calls to match production rhythms. Most data fields have professional units. Configuration must support verification of unit consistency to avoid unit mismatch issues during calculations.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate combined context from multiple DCS trends, interlock records, and laboratory reports to avoid truncation of critical process information |
maxTokens | 2000–3000 token | Supplier audit and quality check responses need to cover multiple sets of indicator comparisons. Overlong output exceeds response requirements for production scenarios |
quoteMaxToken | 1500–2000 characters | Must fully reference key clauses from process index cards and interlock bypass records to avoid truncation of important compliance content |
temperature | 0.1–0.3 | Quality audits require stable result consistency to avoid indicator judgment deviations caused by random fluctuations |
AIPROXY_API_ENDPOINT | Fill in the corresponding address according to the deployment environment | Route model requests to suitable proxy nodes to ensure secure data transmission and controllable latency |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Implement identity verification for model calls to prevent unauthorized access |
Note: This page is based on the configuration specifications for FastGPT v4.17.0
Three common configuration mistakes
- Phenomenon: The total material quantity calculated by the model does not match actual tank data. Fields may be empty or have numerical deviations. Cause: Unit consistency verification for data is not configured. Different unit parameters are added directly, or time range matching between real-time data and historical shift reports is not handled correctly.
- Phenomenon: Model calls return identity verification related errors. Cause:
AIPROXY_API_TOKENis not filled in correctly, or the key permission is not configured to allow access to model interfaces related to supplier audits. - Phenomenon: The number of documents returned by retrieval is insufficient to cover key process indicators. Cause: The
maxContextparameter is not adjusted to fit multiple document combinations, or the query optimization link is not configured to expand the retrieval scope. This leads to ambiguous queries that fail to retrieve valid documents.
How to verify a correct configuration
- Upload a single process index card and DCS trend chart, trigger a quality audit request, and check whether the returned result fully references key parameters in the documents.
- Import a set of process data with different units, trigger a calculation request, and check whether the returned result marks a unified output unit.
- View the model call log to confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfiguration items match the deployment environment, with no identity verification errors. - Simulate an ambiguous query such as "Check recent raw material quality", and check whether the system expands the query scope through query optimization to retrieve shift reports and laboratory reports from the corresponding period.
Note: The values listed are common starting points. They should be evaluated against the samples used in the target deployment environment.