What data looks like for this category
Data sources for this category include formula systems, raw material batch ledgers, internal mixer operation logs, Mooney viscometers, rheometers, vulcanizer parameter records, and laboratory finished product performance reports. Update frequency aligns with each raw material batch, each mixed rubber batch, and each vulcanized product per mold cycle.
Daily documents include formula sheets (with feeding ratios and sequence), weighing records, mixing curves, rubber compound quick test reports, vulcanization process cards, mold ledgers, and finished product performance test sheets. Most fields include batch numbers, weight, temperature, time, and similar metrics. Common units are kilograms, degrees Celsius, and minutes.
Constraints during model access and configuration
The high-frequency, small-batch update rhythm requires the model to quickly process single or small-batch audit tasks, avoiding processing delays caused by overly long context accumulation. Multi-source heterogeneous data formats require configuration to adapt to structured device logs and semi-structured process documents, with clear priorities for field extraction.
Fixed units and field rules require the model to identify industrial parameters with precision, avoiding unit or numerical deviations. Additionally, single audit data volumes are small but time-sensitive, so context length must be limited to ensure processing efficiency and avoid resource waste.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of spliced documents such as mixing curves, vulcanization process cards, and weighing records, to avoid truncating key process parameters |
maxTokens | 2000 token | Matches the audit output length of a single inspection record, to ensure full presentation of deviation analysis and rectification suggestions |
temperature | 0.1–0.3 | Reduces random deviations, ensures consistency in process parameter audits, and meets the precision requirements of industrial production |
| Workflow/API tool selection | "auto" | Lets the model autonomously choose whether to call tools to process structured process data, adapting to audit needs for multi-source documents |
AIPROXY_API_ENDPOINT | http://{local model IP}:{port}/v1 | Connects to locally deployed industrial-specific large models or proxy services for general large models, adapting to enterprise internal deployment scenarios |
AIPROXY_API_TOKEN | Fill in the key provided by the model service | Completes identity verification for the proxy service, ensuring requests are legal and valid |
Three common configuration mistakes
- The model list fails to update after modifying config.json, and the configuration in the /app/data/config directory as viewed inside the container is not synchronized. Cause: The FastGPT container was not restarted to load the new configuration, or the configuration file path was entered incorrectly.
- The model stream response is empty, and the audit result contains no valid content. Cause: The
AIPROXY_API_ENDPOINTconfiguration is incorrect, or the model service did not return streaming data normally, failing to adapt to the format requirements of industrial data. - The audit result shows parameter unit deviations, such as identifying "degrees Celsius" as "degrees Fahrenheit". Cause: The
temperatureparameter is set too high, leading to insufficient precision in the model's judgment of industrial parameters, and failure to match the field characteristics of this category.
How to confirm successful configuration
- Log in to the FastGPT v4.17.0 backend, go to the model management page, and check if the configured model appears in the available list.
- Upload a single batch of mixing records and vulcanization process card, initiate an audit test, and check if complete audit results and parameter deviation analysis are returned.
- Enter the FastGPT container, view the configuration file in the /app/data/config directory, and confirm that the modified parameters have been synchronized and updated.
- Test the streaming response function, check if segmented results are returned during the audit process, with no empty responses or prolonged lag.
The configuration values listed above are common starting points. Always validate against operational samples specific to the deployment.