What data looks like for this category
This category primarily covers bread and pastry, cookie, instant noodle, and frozen pastry factories. 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 sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, oven or frying line equipment, laboratory test data, and food traceability ledgers. Data updates roll out according to production batches, each shift, and daily cycles. Fields include content directly related to food safety such as allergen material lists, baking temperature and duration, packaging dates, and shelf life.
What constraints these characteristics impose during deployment and upgrade
Because this category has many formulas, frequent product line changes, and direct consumer-facing sales, the knowledge base for equipment inspection and maintenance Q&A needs frequent updates for sensitive content such as allergen lists and line change standards. It also requires high accuracy in retrieval results to avoid food safety risks. Multiple heterogeneous data sources require the deployment phase to support field formats from different systems, to prevent missing data during synchronization. The frequent update feature requires the upgrade phase to support incremental synchronization configuration, to reduce time costs from full data migration. Additionally, the direct-edible attribute means the accuracy of Q&A results is directly linked to recall risk, so an additional configuration verification step must be added during deployment.
How to set configurations
This configuration is based on FastGPT v4.17.0. Specific parameters are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 concurrent connections | Adapts to high-frequency read/write demands for multi-source equipment data, prevents retrieval interruptions caused by exhausted connection pools |
MONGODB_URI | Address of the document library synced from factory ERP/MES systems | Stores unstructured business documents such as batch production records and baking curves |
REDIS_URL | Local deployed Redis service address, configured with 1000 MB memory quota | Caches frequently retrieved allergen lists and line change standard parameters, reduces access pressure on the primary database |
STORAGE_S3_ENDPOINT | Endpoint address adapted to the factory’s existing object storage | Stores attachment files such as cleaning and disinfection records and factory inspection reports |
FE_DOMAIN | Exclusive domain name bound for factory intranet access | Limits the access scope of the deployed service, ensures access security for the production intranet |
ROOT_KEY | Random 32-bit string generated according to internal enterprise permission specifications | Controls background access permissions after deployment, prevents unauthorized modification of core configurations |
Three common mistakes
- Symptom: Unable to select a locally deployed ChatGLM2 model when creating a knowledge base, with no corresponding option in the interface. Cause: The access address of the local model was not correctly configured in the associated model configuration table, or permission configuration for model routing was not completed via oneapi.
- Symptom: Database query returns empty results, with missing production record fields retrieved. Cause: Incremental synchronization tasks were not configured according to the production batch update frequency, causing the latest ERP/MES data to not be fully imported into the knowledge base.
- Symptom: Speech generation function call fails, with a model loading timeout error returned in the console. Cause: Insufficient memory quota was not configured for
REDIS_URL, causing model cache congestion and preventing normal loading of required speech model files.
How to confirm configurations are correct
- Log in to the FastGPT backend database management page, check that the concurrent connection count for
DB_MAX_LINKdoes not trigger the alarm threshold, to confirm normal connections. - Upload a recent baking curve document, perform a search related to equipment inspection, confirm that matching production batch records and parameters are retrieved.
- Call the locally deployed model interface, input a preset equipment maintenance question, confirm that a response conforming to the factory’s business logic is returned, to verify correct model access configuration.
- Access the intranet address bound to
FE_DOMAIN, confirm there is no permission interception, and that the system can be entered normally to complete basic knowledge base operations.
The values provided are common starting points and should be measured against the reader's own samples.