What data for this category looks like
This category's data primarily comes from raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, ovens or frying lines, laboratory testing equipment, and food traceability ledgers. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data update frequency is per batch, per shift, or daily. Fields include allergen labels, feeding weights, baking temperature curves, shelf life, and more. Due to frequent line changes, the structure of single-batch ledgers varies significantly.
What constraints do these characteristics impose on deployment and upgrade?
The multi-source data access, high-frequency updates, flexible ledger structure, and sensitive field verification requirements for this category impose multiple constraints on deployment and upgrade workflows. Multi-system integration requires adaptation to different formats of production data, and data mapping rules must be configured in advance. High-frequency updates require database connection pool and cache configurations to support concurrent scenarios. Frequent line changes lead to dynamic adjustments to ledger structure, so flexible field expansion must be supported. Access control for sensitive data such as allergens and formulas must be strictly configured to prevent unauthorized disclosure.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | Fill in according to the factory's own S3 storage node | Connect multi-source production data files, adapt to the distributed storage architecture commonly used in food factories |
REDIS_URL | redis://:custom password@storage address:6379/0 | Cache frequently accessed formula and production record data to ensure the response speed of ledger queries |
DB_MAX_LINK | 200 | Meet concurrent connection requirements when multiple shifts and production lines submit ledger data simultaneously |
FILE_TOKEN_KEY | Generate a random 32-bit string | Encrypt access tokens for ledger files to prevent unauthorized access to sensitive data such as allergens and formulas |
PG_URL | Fill in according to the PostgreSQL instance connected to the factory's ERP | Store structured production ledger data, align field formats with ERP/MES systems |
Three common configuration mistakes
- After an upgrade, model vendor icon loading fails, and the interface displays image placeholders or blank spaces. The cause is that the
FE_DOMAINparameter is not configured correctly, preventing the frontend from obtaining the correct access domain name for static resources. - An error occurs when uploading production ledger files via RAG, prompting "fail to create post presigned url". The cause is incorrect configuration of the
STORAGE_S3_ENDPOINTparameter, or that cross-domain access permissions for the S3 storage bucket are not enabled. - Database connection timeout occurs when multiple production lines submit ledger data simultaneously. The cause is that the
DB_MAX_LINKconfiguration value is too low to meet concurrent connection requirements.
How to confirm configuration is successful
- For FastGPT v4.17.0, log in to the backend, navigate to the storage configuration page, manually upload a test production record file, confirm that there are no upload errors and the file can be previewed normally.
- Check the system operation logs to confirm that the
REDIS_URLconfiguration is correct, with no log records of connection timeouts or authentication failures. - Submit production ledger data for more than 3 different batches, confirm that database connections operate normally and data can be properly stored in the corresponding storage instance.
- Configure allergen field verification rules, submit test data with incorrect allergen labels, confirm that the system triggers corresponding verification prompts.
The values provided here are common starting points and should be evaluated against the reader's own samples.