What the Data for This Category Looks Like
The data for this category covers the entire production process of skin care, hair care, makeup, and special cosmetics. Daily documents include formula work sheets, weighing and feeding records, emulsification and filling records, cleaning and site clearance records, semi-finished and finished product inspection reports, and label review documents. Data sources include registered and filed formulas, raw material safety materials, production equipment, ERP/MES systems, laboratory data, and packaging material version information. Updates occur with each production batch or each line change. Core fields include raw material name, dosage, production batch number, inspection indicators, packaging material specifications, and claimed content.
Constraints Imposed on Model Access and Configuration
Since data is updated per production batch or line change, the model must support high-frequency incremental data access. This avoids performance overhead caused by full re-scanning. Multiple document types have significant structural differences. Parsing rules for different fields must be adapted. For example, the raw material dosage field in formula sheets must match filed values. Label documents must verify that claimed content matches registered filings. Key production parameters such as emulsification temperature and homogenization time must be bound to device logs. Model calls must be associated with batch numbers to enable data traceability. Content that does not match filings must be strictly filtered to prevent incorrect information from entering the analysis process. This setup adapts to the model access rules of FastGPT v4.17.0. No additional adaptation for treatment-related data modules is required. Focus should be placed on compliance and batch-associated analysis.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total length of documents related to a single production batch, covering context information from multiple types of files including formulas, feeding records, and inspection reports |
maxTokens | 2000–3000 token | Ensures sufficient detail in defect analysis results while avoiding timeout during single calls |
quoteMaxToken | 1500 characters | Limits the fragment length of referenced documents, adapting to precise matching needs for short documents such as label reviews and formula sheets |
temperature | 0.2–0.4 | Reduces randomness in analysis results, ensuring accuracy of formula and compliance checks |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools, adapting to batch traceability requirements involving multiple associated documents |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Associates the access address of a third-party model, ensuring normal operation of the model call link |
Three Common Configuration Errors
- Phenomenon: After configuring GPT-4o on a third-party API platform, FastGPT page calls return a 401 unauthorized error. Cause: The
AIPROXY_API_TOKENparameter is not filled correctly, or the token does not have call permissions for the corresponding model. - Phenomenon: A locally deployed DeepSeek model cannot be selected in the model thinking settings. Cause:
AIPROXY_API_ENDPOINTis not configured as the API address of the local model, or the public access permission of the model is not enabled. - Phenomenon: The output result of a previous AI model in a workflow cannot be set to an empty value via variable update, and old data remains after execution. Cause: The correct scope is not selected in the variable update node, or the forced refresh mechanism for variables is not triggered.
How to Verify Successful Configuration
- Initiate a traceability test for a single batch of production documents. Check whether the results returned by the model are associated with the corresponding batch's formula, feeding, and inspection data.
- Simulate a compliance check scenario. Verify whether the model can accurately identify differences between filed formulas and actual feeding records.
- Call the configured model service. Check whether the returned response status code is normal and the result format meets expectations.
- Adjust the
temperatureparameter. Verify whether analysis results under different values meet the precision requirements for compliance checks.
The values provided are common starting points and should be measured against the reader's own samples.