What the data for this category looks like
Data sources include wood pulp and waste paper batch ledgers, DCS/QCS systems, online paper machine measurement devices, laboratory test reports, and energy and water treatment system logs. Daily documents include pulp preparation sheets, beating operation records, paper machine operation logs, online quality curves, sheet break and grade change ledgers, and laboratory quality inspection reports. Data update frequencies cover real-time (online quality data), per shift (beating and energy records), and per roll (finished paper quality inspection and sheet break records). Fields include pulp ratio, beating degree, grammage, moisture, thickness, paper defect type, and more. Common units are percentage, grams per square meter, seconds, meters, and others.
Constraints imposed on model integration and configuration
Multi-source heterogeneous data with widely varying update frequencies requires model integration to support dynamic context length adjustment. This avoids truncating batch-level pulp preparation sheets and real-time online quality data. Time-series online quality curves require the model to handle time-series analysis. Tool call configuration must match the data update time window. Mixed input of structured quality inspection reports and unstructured sheet break logs requires clear context token limits. This prevents key fields from being truncated. Finished paper data generated per roll has a large volume. Output token length must be set appropriately to carry complete roll-level quality inspection records.
How to set the configurations
These configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to bulk input of multiple documents such as pulp preparation sheets and paper machine operation records, avoiding truncation of key quality inspection fields |
maxTokens | 2000–3000 token | Matches the output length of per-roll finished paper quality inspection reports, while adapting to time-series analysis result output for online quality curves |
| Workflow/API tool selection | Auto | Supports automatic tool calls to supplement external information such as wood pulp market trends and industry quality standards |
temperature | 0.3–0.5 | Reduces random generation probability, ensures consistency of quality audit results, and meets the strict requirements of production scenarios |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Ensures the network path for model calls complies with regulations, and adapts to enterprise internal data isolation requirements |
AIPROXY_API_TOKEN | Use valid tokens calibrated through actual testing | Ensures security of model access identity verification, and prevents unauthorized access |
Three common configuration errors
- Phenomenon: After setting
toolChoiceto Auto, the model cannot trigger web search tools and only returns basic question-and-answer results. Cause: Tool call permissions are not enabled in the model integration configuration, or external data sources are not associated with the tool call scope. - Phenomenon: After importing data such as wood pulp batches and online quality curves, the vector indexing progress stalls. Cause: The heterogeneous characteristics of pulp and paper data are not adapted, and fixed-length chunking rules are used, leading to mismatched chunking between real-time data and batch data.
- Phenomenon: After connecting a local vision model, empty results are returned when recognizing curve charts in laboratory quality inspection reports. Cause: The
visionconfiguration item is not enabled, or chart images are not preprocessed for adaptation, preventing the model from parsing visual information.
How to confirm successful configuration
- Upload a combined document of a single pulp preparation sheet and online quality curve, verify that the analysis results returned by the model cover all core business fields in the document.
- Submit a query that requires external information, verify that the model can trigger tool calls according to the configured settings and return relevant results.
- View the system indexing logs, confirm that all imported pulp batches, quality inspection data and other data have completed indexing normally with no abnormal errors.
- Upload an image file of a laboratory quality inspection report, verify that the model can correctly recognize chart and numerical information in the image.
The values provided here are common starting points, and should be evaluated against local samples.