What the data for this category looks like
Data sources for this category include resin batches and property sheets, injection molding machine operating parameters, mold temperature controller data, MES production systems, first article inspection records, material change and machine cleaning records, and more. Data is generated and updated per production batch, each mold or material change, and after each shift inspection. Daily documents include injection molding process cards, raw material drying records, mold history logs, first article inspection forms, material change and machine cleaning records. Fields cover resin melting temperature, holding pressure, drying duration, mold usage count, and other metrics. Some documents must be linked to corresponding production batch numbers and timestamps.
What constraints these characteristics impose on the model access and configuration workflow
This category has high-frequency data updates and scattered sources. Model access configuration must support batch processing of multi-source heterogeneous data to avoid exceeding model context limits. Professional field units and parameter ranges require the model to accurately identify, so the configured model must support parsing industrial domain terminology. Batch association for production ledgers requires model outputs to be bound to corresponding production identifiers. Context association rules must be clearly defined during configuration to prevent cross-batch data confusion. Additionally, production data processed in a single operation is large, so token consumption per single request must be reasonably limited to avoid timeouts or call failures.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Private deployment large model access address | Adapt to the dedicated model call requirements for multi-source industrial data in this category |
AIPROXY_API_TOKEN | Access key generated by the privately deployed large model | Ensure permission security for model calls and prevent unauthorized access |
maxContext | 8000–12000 tokens | Adapt to the total data volume of a single batch production ledger to avoid context overflow |
maxTokens | 2000–3000 characters | Limit the output length of a single round of report generation to meet the requirements of production ledger summarization |
temperature | 0.2–0.4 | Reduce output randomness to ensure the accuracy of industrial parameter summarization |
quoteMaxToken | 1500 tokens | Limit token consumption for referencing source data, prioritize retaining core process and inspection data |
Three common configuration mistakes
- The text understanding model list in the FastGPT backend is empty, and custom models cannot be selected. The cause is that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured, or the connected custom model has not completed permission verification. - A timeout error occurs when generating a production ledger report. The cause is that the
maxContextvalue exceeds the maximum context limit of the currently called model, causing the request to fail to respond. - Redundant parameters from non-current production batches or formatting chaos appear in the summarization results. The cause is that the
quoteMaxTokenvalue is not limited, and the model introduces irrelevant historical data.
How to confirm the configuration is complete
- Check the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto ensure they match the privately deployed large model address and key. - Initiate a test call for a single batch production ledger, and verify that the summary content returned by the model includes core fields from injection molding process cards and raw material drying records.
- View the call logs to confirm that the token consumption of a single request does not exceed the model limit, and there are no timeout or error records.
- After adjusting the
temperatureparameter, compare the two output results to confirm that the accuracy of the summary content meets expectations.
The values provided are common starting points and should be measured against the reader's own samples.