What Data Looks Like for This Category
This category of data comes from DCS/APC control systems, LIMS laboratory management systems, tank farm metering systems, crude oil evaluation reports, equipment management systems, and energy management systems. Daily documents include operating procedures, production plans, material balance reports, distillate outlet quality reports, interlock ledgers, shift handover records, and change management documents. Data update frequencies cover real-time, per shift, and daily. Most document fields include process parameters, material quantities, quality indicators, and units such as temperature, pressure, metric tons, and cubic meters.
What Constraints These Characteristics Impose on Model Integration and Configuration
Multi-source heterogeneous data updates in real-time, per shift, and daily. Model integration must adapt to document sharding and index updates at different frequencies. This avoids index lag or resource waste. Diverse document types include professional content such as interlock logic and material balance formulas. Sufficient context length must be configured to cover long-text reviews. This prevents truncation of critical process logic. Multiple units are coupled through logistics and energy networks. The model must be able to associate cross-unit document data. Reasonable adjustments must be made to tool call scope limits. Industrial system data from different sources has varying formats. Format compatibility and permission configuration must be ensured during integration. This guarantees accurate data reading.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-document review needs of petroleum refining, covers cross-document associated content for coupled multiple units, and complies with FastGPT v4.17.0 context configuration specifications |
maxTokens | 4000–6000 token | Balances single-round review output length and response speed, fits content lengths for scenarios like process parameter verification and material balance checking |
quoteMaxToken | 1500–2000 token | Retains sufficient original text snippets for cross-verification of core content such as interlock conditions and quality indicators |
| Workflow/API tool selection | auto | Automatically adapts tool call logic for multi-document associated reviews, covers scenarios like cross-unit data verification and change management document association |
temperature | 0.1–0.3 | Reduces randomness in model output, ensures consistency of professional review results, and meets the rigor requirements of industrial scenarios |
AIPROXY_API_ENDPOINT | Fill in the dedicated node according to the deployment environment | Adapts to access restrictions of the enterprise internal network, ensuring security and stability of data transmission |
AIPROXY_API_TOKEN | Assign dedicated keys according to permissions | Controls access permissions for model integration, preventing unauthorized calls and data leaks |
Three Common Mistakes
- Symptom: Model service frequently interrupts automatically, logs show connection timeout or 401 status code. Cause:
AIPROXY_API_TOKENhas not been updated according to permissions, orAIPROXY_API_ENDPOINTpoints to a decommissioned proxy node. - Symptom: After adding reordering configuration, the order of retrieval results does not change. Cause:
quoteMaxTokenis set too small, only retaining a small number of original text snippets, so the reordering model cannot obtain sufficient context information to adjust the order. - Symptom: The locally deployed content extraction module cannot run normally, while the official online version works properly. Cause:
maxContextconfiguration does not match the document length, ortoolChoiceis not set to auto, causing the extraction logic to not trigger.
How to Confirm Configuration is Complete
- Initiate a cross-review of a single long document, such as a complete unit operating procedure, check whether the output covers cross-document associated content, and no critical text truncation occurs.
- After configuring the reordering model, input a query containing multiple sets of quality indicators, verify whether the order of retrieval results adjusts according to the query logic.
- View the model call logs, confirm that there are no format errors in the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations, and no frequent connection errors. - Test the content extraction function, input a document fragment containing process parameters, confirm that the extracted fields match the original text, with no missing or incorrect content.
The values provided here are common starting points, and should be evaluated against the reader's own samples.