What data for this category includes
Data for this category originates primarily from formula management systems, raw material batch records, internal mixers, Mooney viscometers, vulcanizing machines, and laboratory test reports. Daily documents include formula sheets, weighing ledgers, mixing curves, rubber compound quick test records, vulcanization process cards, mold usage logs, and finished product performance reports. Data updates trigger when each raw material shipment arrives, each rubber compound batch is mixed, and each vulcanization production cycle completes. Fields include specific operational parameters such as temperature (degrees Celsius), time (minutes), batch numbers, and Mooney viscosity values.
Constraints for the model access and configuration workflow
This category has frequently updated operational data. It requires limiting context length during model access to avoid loading redundant or expired mixing and vulcanization parameters. Multiple structured and semi-structured document types require clear knowledge base classification matching rules in configuration settings. This ensures the model accurately recalls relevant process cards or formula sheets. Operational parameters have strict accuracy requirements. Configuration must constrain model output randomness, while adapting to the high-frequency request rhythm of each shipment and batch. This prevents request timeouts or invalid returned results. This configuration guidance uses FastGPT v4.17.0.
How to set configuration values
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-document associated queries for mixing and vulcanization processes, avoids exceeding the model's context capacity limit |
maxTokens | 2000–3000 token | Ensures complete output of process parameters and formula details, while controlling single-round response latency |
quoteMaxToken | 1500 characters | Limits the reference length of recalled documents, avoids interfering with the model's judgment of core process requirements |
temperature | 0.1–0.3 | Matches the category's high requirement for parameter accuracy, reduces randomness of generated results |
vision | Enable as needed | Adapts to query needs for image-based process documents, such as vulcanization mold drawings and mixing curve analysis |
AIPROXY_API_ENDPOINT | Fill in the actual API address based on the deployment environment | Adapts to local or cloud-based model invocation links, ensures requests are forwarded correctly |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Ensures valid identity verification for model invocation, prevents unauthorized access |
Three common configuration errors
- Model invocation throws driver-related errors. Logs show graphics card initialization failure. The cause is failure to adapt the Ollama deployment environment for AMD graphics cards, and failure to correctly configure underlying driver compatibility parameters.
- After configuring a domestic large language model, the model cannot be selected for loading. The interface displays an empty model list. The cause is failure to correctly fill in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, preventing the platform from pulling model metadata. - Using a vLLM image understanding model returns empty values. The request returns a 200 status code but contains no valid content. The cause is failure to enable the
visionconfiguration item, or failure to correctly configure the model's image input adaptation parameters. This prevents the model from processing incoming process drawing requests.
How to confirm correct configuration
- Submit a test request for mixing process parameters. Check if the returned result includes reference markers for the corresponding document.
- Review model invocation logs. Confirm that the request address and key match the configured settings, with no network or identity verification errors.
- Adjust the
temperatureparameter. Observe the stability of generated results to verify the parameter's effect on controlling output randomness. - Upload a vulcanization process card document unique to this category. Confirm that the knowledge base recall rule correctly matches the corresponding query scenario.
The values provided are common starting points and should be evaluated against local operational samples.