What the data for this category looks like
Cosmetics production data comes from registered and filed formulas, raw material safety documents, weighing and emulsification equipment, ERP/MES systems, laboratory test records, and packaging material version ledgers. Daily documents include formula process sheets, weighing and feeding records, emulsification and filling records, cleaning and site clearance records, semi-finished and finished product inspection reports, and label review forms. Data updates are triggered with each production batch or line change. Fields include production batch number, raw material name and dosage, restricted substance content, emulsification temperature, homogenization duration, packaging material version number, and others. Units involve degrees Celsius, kilograms, minutes, and similar units.
What constraints these characteristics impose on model access and configuration
Batch-based, high-frequency updated data for cosmetics production requires that model access supports synchronizing data sources by production batch to avoid information lag from static knowledge bases. Multiple types of professional documents and dedicated fields require configuring parsing rules adapted to formats such as formula processes and inspection records, to ensure that professional fields such as restricted substance content and production batch number can be accurately identified. Since errors involve compliance and recall risks, strict result verification logic must be configured. At the same time, it is necessary to link with filed data sources to ensure consistency between production data and filed information.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate the content of multiple documents per batch to avoid truncation of critical filed and production data |
maxTokens | 4000–6000 token | Meets the output length requirements for ledger summary and compliance reports, covering all batch details and verification instructions |
quoteMaxToken | 1500–2000 token | Ensures that quoted segments such as raw material filings and inspection records are complete, without loss of professional information |
temperature | 0.1–0.3 | Reduces output randomness, ensures rigorous wording in compliance reports, and avoids deviations in formula data |
AIPROXY_API_ENDPOINT | Fill in according to platform deployment node | Adapts to access paths for local or cloud deployments, ensuring stable data transmission |
AIPROXY_API_TOKEN | Exclusive key assigned according to permissions | Ensures the security of interface calls and prevents unauthorized access to production data |
Three common errors
- Phenomenon: After creating a new knowledge base, the image understanding model cannot be called to process process sheet scans. Cause: The
visionparameter is not enabled in the configuration, or the corresponding visual model dependencies are not loaded for local deployments. - Phenomenon: After knowledge base search, only matching content is returned, and no reasoning is performed for unmatched compliance questions. Cause: The model's reasoning trigger logic is not configured correctly, and search matching results are mistakenly used as the sole output basis.
- Phenomenon: The interface call returns a 401 status code, and ledger synchronization cannot be completed. Cause: The
AIPROXY_API_TOKENconfiguration is incorrect, or the key permissions do not cover the access scope of production data sources.
How to confirm the configuration is complete
- Upload the formula process sheet and filed formula for a single batch, and check whether the raw material comparison results output by the model include restricted substance verification content.
- Call the interface to synchronize the production records from the most recent line change, and check whether the returned ledger data includes all required fields and corresponding units.
- View the interface log to confirm that the
maxContextparameter does not trigger content truncation, and the output content completely covers the uploaded documents. - Test compliance verification questions not included in the knowledge base, and confirm that the model can perform reasonable reasoning based on existing rules.
The values provided are common starting points and should be measured against the reader's own samples.