What data for this category looks like
Data for this category comes from registered and archived formulas, raw material safety documents, production equipment such as weighing and emulsifying machines, ERP/MES systems, laboratory records, and packaging material version information. Daily documents include formula process sheets, weighing and feeding records, emulsification or filling records, site cleaning records, semi-finished and finished product inspection reports, and label review documents. Data updates are triggered with each production batch and each line change. Core fields include raw material ratios, production parameters, batch numbers, archive numbers, and packaging material information.
What constraints these characteristics impose on the model access and configuration link
Because data updates rapidly with each production batch, the model context must support dynamic loading of batch-level data. Mixed structured and semi-structured documents from multiple sources require adapting text splitting and retrieval logic for different formats. Highly compliant fields such as archive numbers and restricted raw material substances require that the context retrieved by the model must accurately match business rules. A single production process involves multiple equipment parameters and batch information, so token consumption per single round of interaction must be limited to avoid interface timeouts or result overflow. At the same time, due to compliance requirements for cosmetics production, model output must be associated with archived data. Therefore, the permission scope of the associated data source must be fixed to prevent calls to unauthorized production documents.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-document context for each production batch, covering associated queries of formulas, production records, and archived information |
maxTokens | 2000–3000 token | Limits single-round reply length, meets the conciseness requirement of equipment inspection Q&A, and avoids interface timeouts |
quoteMaxToken | 3000–4000 token | Limits total token count of retrieved context, ensuring only batch production data relevant to the current inspection question is called |
temperature | 0.1–0.3 | Reduces generation randomness, ensures accuracy of equipment maintenance steps and compliance parameters, and matches the rigor of production scenarios |
| Workflow/API tool selection | auto | Allows the model to automatically choose whether to call tools for parameter queries related to equipment inspection, adapting to multiple types of maintenance scenarios |
AIPROXY_API_TOKEN | Fill in the exclusive key allocated by the platform | Ensures compliance of interface call permissions, matching data access requirements for cosmetics production scenarios |
Three common mistakes
- Interface calls return a 403 permission error. The cause is that
AIPROXY_API_TOKENis not configured correctly, or an access key that does not match production scenario permissions is used. - The context retrieved by the model only contains old batch production data and cannot match the line change parameters of the current inspection. The cause is that the context refresh rule for
maxContextis not adjusted according to the batch update frequency. - The latest models of some manufacturers cannot be selected in the configuration interface. The cause is that the interface mapping rules for the corresponding models are not added in the access configuration, causing the platform to not load the optional list of such models.
How to confirm the configuration is complete
- Initiate a simulated equipment inspection query, check whether the returned results include the archived formula information of the current batch, and confirm that the context retrieval range meets the configuration requirements.
- View the interface call log, confirm that the token count of a single round of reply does not exceed the set value of
maxTokens, and verify the compliance of the reply length. - Check the model access key and endpoint configuration, confirm that there are no permission errors in interface calls, and verify the validity of the access link.
- Trigger a query for a line change scenario, check whether the retrieved production data is the latest batch, and confirm that the update frequency matches the configuration rules.
The values provided are common starting points and should be measured against the reader's own samples.