What this category’s data includes
Data for this category is generated across the entire food sampling process. Daily documents include sampling plans, sampling forms, inspection reports, unqualified food verification and disposal reports, and risk control notices. Data sources include the food safety sampling monitoring system, the State Administration for Market Regulation sampling results query system, testing institution LIMS, and food producer and operator ledgers. Update frequency is monthly. Data fields include sampling points, inspection items, non-conformity judgment bases, disposal measures, and more. Core evidence consists of laboratory-issued inspection reports, which require association and verification with sampling processes and regulatory ledgers.
Constraints for Deployment and Upgrade
Monthly updated bulk data requires deployment environments to support stable bulk data import and storage. Reserve at least 1000 MB of database storage space. Core document correlation verification requirements mean configuration items must support multi-data source docking, and ensure data synchronization consistency. Multi-actor usage scenarios including grassroots supervision institutes and testing institutions require deployment configurations to support access control with different permissions. During cross-version upgrades, data association logic varies by version. Ensure that field mapping for sampling forms and inspection reports is not lost during migration. For offline deployment scenarios, prepare dependency packages and model files locally in advance. This avoids upgrade failures caused by external network restrictions.
Configuration Reference
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 | Adapts to concurrent read/write requirements of monthly bulk sampling data, prevents database connection exhaustion |
MONGODB_URI | Local intranet-accessible MongoDB instance address, such as mongodb://192.168.1.100:27017/fastgpt | Stores unstructured documents including inspection reports and sampling forms, supports flexible field expansion |
REDIS_URL | Local intranet-accessible Redis instance address, such as redis://192.168.1.101:6379 | Caches frequently accessed sampling plans and point data to reduce database load |
STORAGE_S3_ENDPOINT | Intranet endpoint of local object storage service, such as http://192.168.1.102:9000 | Stores batch-generated inspection report scans and sampling form attachments, meets offline storage requirements |
FE_DOMAIN | Intranet-accessible domain name or IP address, such as http://192.168.1.103 | Adapts to intranet access scenarios for grassroots supervision institutes, avoids external network exposure risks |
ROOT_KEY | Custom high-strength random string | Highest permission key for the admin backend, ensures permission isolation for multi-actor access |
Three Common Misconfigurations
- Phenomenon: Historical sampling form data fails to load normally after a major cross-version upgrade. Cause: Database field changes between versions were not validated ahead of time, and the database directory was migrated directly without field adaptation.
- Phenomenon: Sampling plan data does not load after commercial edition initialization. Cause: The commercial edition initialization logic and free deployment upgrade logic were confused, and intranet-accessible data source connection parameters were not configured.
- Phenomenon: Context truncation error occurs when the model processes inspection reports. Cause: Storage parameters set during deployment and the front-end configured context upper limit are not unified, resulting in parameter conflicts during actual calls.
How to Verify Successful Configuration
- Access the intranet-deployed frontend address, confirm that the sampling plan list loads normally.
- Upload a simulated sampling form file, confirm that the file can be stored normally and associated with the corresponding sampling plan.
- Run a bulk data import test, confirm that there are no timeouts or connection exhaustion issues with database connections.
- Check the Redis service logs, confirm that the cache service is running normally with no connection exceptions.
The values provided here are common starting points. They should be evaluated against the user’s own specific samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.