What the data for this category looks like
Data for spraying and coating scenarios comes from coating supplier TDS/SDS documents, spray booth and oven operating data, collection results from colorimeters and film thickness gauges, environmental monitoring records, and MES system data. Daily generated documents include coating process cards, coating batch lists, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Data updates occur per coating batch and per shift. Critical operating data is collected continuously. Document fields cover process parameters, material batches, test results, environmental indicators, and some fields have clear units.
What constraints these characteristics impose during model access and configuration
Data for this category is multi-source, dispersed, and updated frequently. This requires model access and configuration to support multi-document association and real-time data synchronization. Fields across multiple document types have clear units. During configuration, ensure the model can accurately identify the correspondence between parameters and units, to avoid generating content that does not comply with process specifications. Continuously collected operating data will quickly occupy the context window. Limit the total token count for a single input to prevent abnormal model responses. Exception analysis requires associating data from multiple links. Tool call configuration must support cross-document parameter matching, to avoid generating reports disconnected from actual production.
How to set the configuration
This document applies to FastGPT v4.17.0. The following configuration items are customized for the spraying and coating scenario:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Matches the total length of coating process cards and multiple test reports, avoids truncating critical parameters such as film thickness and oven temperature |
temperature | 0.1–0.3 | Ensures accuracy of process parameters and exception analysis, reduces randomness of generated content, and avoids non-compliant process recommendations |
| Workflow/API tool selection | auto | Supports automatic calling of associated data sources such as process cards and test reports, completes cross-document parameter matching and exception analysis |
maxContext | 15000–20000 characters | Accommodates paint mixing records and oven curve data for multiple coating batches, ensures completeness of context |
quoteMaxToken | 2000–3000 token | Limits the single-segment length of referenced documents, ensures referenced content focuses on critical process or exception information, and avoids redundant content interfering with the model |
AIPROXY_API_TOKEN | Exclusive key assigned by the platform | Ensures secure identity verification for model calls, prevents unauthorized access requests |
Three common configuration mistakes
- Symptom: A "model stream response is empty, please check if the model stream output is normal" prompt appears when calling the model. Cause:
maxContextis not configured, or its value exceeds the system upper limit, causing the context window to be filled with a large amount of coating data, preventing the model from generating valid responses. - Symptom: After migrating the knowledge base, all documents can be viewed on the management page, but the model prompts that the knowledge base is empty during conversation. Cause:
quoteMaxTokenis set too small, causing document fragments such as referenced process cards and paint mixing records to be truncated, preventing the system from loading valid knowledge base content. - Symptom: A 400 error is returned when calling the MCP tool, but the model works normally when the tool is not called. Cause:
toolChoiceis configured to force tool calls, but the data source association for the corresponding tool is not configured, resulting in invalid request parameters.
How to confirm the configuration is correct
- Upload a single coating process card and the corresponding batch's film thickness and color difference report, initiate a content generation request, and verify whether the model output includes the critical parameters from the documents.
- View the model call logs, confirm that no context overflow or parameter truncation prompts appear.
- Configure and call the associated MCP tool, verify whether the data returned by the tool matches actual production records.
- Generate multiple exception reports, compare output consistency across different configurations, and confirm that the parameter values meet the accuracy requirements of the scenario.
The values provided are common starting points and should be measured against the reader's own samples.