This configuration plan is based on FastGPT v4.17.0.
What the data for this category looks like
The core entities in this category are bread, pastry, biscuit, instant noodle, and frozen pastry factories. These organizations daily generate documents including formula and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Organizations source data from raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, production equipment, laboratories, and food traceability ledgers. Update frequency follows per batch, per shift, or daily schedules. Data fields include allergen material identifiers, production window parameters, packaging dates and shelf lives, foreign matter detection results, and more. Some documents require association with batch numbers and production team information.
What constraints do these data characteristics impose on database and operations processes
The data characteristics of this category create multiple constraints for database and operations processes. High-frequency document updates across multiple batches require databases to support high-concurrency writes and low-latency queries. Sensitive fields such as allergen and foreign matter detection data require separate indexes to ensure efficient and secure sensitive data retrieval. Heterogeneous data from different production equipment and systems need a unified storage structure to reduce cleaning costs for subsequent summarization. Bulk daily or shift data imports require transaction rollback mechanisms to prevent single-batch exceptions from affecting overall data. Multi-document linked queries tied to batches need optimized association logic to reduce performance loss from cross-table queries.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt_bakery?authSource=admin | Adapts to multi-batch data storage requirements, specifies a dedicated business database name to isolate production data |
PG_URL | postgresql://user:pass@host:5432/fastgpt_bakery_report | Stores structured ledger report metadata to enable fast queries after batch summarization |
DB_MAX_LINK | 200–300 | Matches concurrent connection needs for high-frequency data writes from multiple devices and shifts, preventing connection exhaustion |
REDIS_URL | redis://:pass@host:6379/0?maxmemory-policy=allkeys-lru | Caches frequently accessed basic batch information to reduce query pressure on core databases |
| Database backup strategy | Full backup at 2 AM daily, incremental backup every hour | Aligns with daily or shift update frequencies to ensure data recoverability |
Three common configuration mistakes
- Symptom: An error stating
MongoDB version 5.0.18 has security vulnerabilitiesappears when starting the service. Cause: The MongoDB version compatible with FastGPT is not used, and the database version has not been updated promptly. - Symptom: Some fields appear empty during bulk import of production records. Cause: Field formats of data exported from different devices and systems are not unified, leading to failed field mapping during storage.
- Symptom: The number of results returned by allergen material associated queries does not match expectations. Cause: No separate index is created for allergen fields, resulting in incomplete query coverage or low retrieval efficiency.
How to confirm the configuration is properly set up
- Perform a bulk import test for batch data, and check if complete data entries for the corresponding batch are generated in the database.
- Initiate an associated query for allergen materials, and confirm returned results match the actually entered batch data.
- Check database connection number monitoring to confirm concurrent connection counts do not exceed the configured
DB_MAX_LINKlimit. - Trigger a full ledger summarization task, and check if the generated report includes all required document fields.
The values provided are common starting points and should be measured against the reader's own samples.