What the Data for This Category Looks Like
Data for this category originates 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 updates occur per batch, per shift, or daily. Fields include batch number, type and weight of fed materials, baking temperature and duration, metal detection results, allergen labels, shelf life, and others. Units include degrees Celsius, kilograms, batch numbers, and additional relevant units.
Constraints Imposed by These Characteristics on Deployment and Upgrade
Multi-source, scattered data sources require permission configuration and format adaptation rules for multi-data source docking during deployment. High-frequency data updates require optimization of vector database incremental update logic during upgrades, to avoid service interruptions from full re-import. Frequent product line changes create demand for knowledge base switching, so configuration items must support quick switching of associated document sets by product batch. High compliance requirements for allergen and foreign matter control require precise field matching during retrieval, so index rules for corresponding fields must be preset during deployment. Single-batch data volume fluctuates with production scale, so sufficient storage and computing resources must be reserved to prevent service freezes.
How to Set the Configuration
This configuration scheme applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to the multi-source, high-frequency data access needs of this category, balancing concurrent connections and server resource usage |
MONGODB_URI | Configure according to the production environment MongoDB instance address | Used to store unstructured document data such as production records and food traceability ledgers |
PG_URL | PostgreSQL instance address with the pgvector extension enabled | Used to store structured retrieval data such as batch numbers, baking curves, and test results, supporting vector matching |
REDIS_URL | Local Redis instance address, port 6379 | Caches frequently accessed data such as formulas and production curves, reducing repeated query pressure on the database |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Ensures signature verification security for file upload and download, preventing unauthorized access to attached files |
STORAGE_S3_ENDPOINT | Configure according to the enterprise cloud storage service provider address | Used to store attached files such as test reports and cleaning and disinfection records |
Three Common Configuration Mistakes
- Issue: Container startup fails during deployment, with logs indicating missing pgvector dependencies or database connection authentication errors. Cause: Reusing the general vector database compose file directly, without removing redundant pg configuration items that do not match the baked goods data scenario, leading to resource conflicts.
- Issue: Image pull failures or startup errors occur when deploying on arm64 architecture servers. Cause: Not using the FastGPT image adapted for arm64 architecture, mistakenly using the x86 architecture image package.
- Issue: Search results fail to match production data for the corresponding batch, or return results that do not match actual production records. Cause: Not using the officially recommended mineru model version, resulting in insufficient vector embedding accuracy that cannot accurately match batch-related data.
How to Confirm Successful Configuration
- Log in to the data source management page in the FastGPT backend, check the connection status for
MONGODB_URIandPG_URL, confirm both show normal connection. - Upload a test production record document, enter a known batch number for retrieval, confirm that associated data such as corresponding baking curves and feeding lists is returned.
- Run an incremental update test for single-batch data, verify that the vector database update speed meets production batch processing time requirements.
- Upload a test report attachment, confirm normal download in the storage management page, verify that the
STORAGE_S3_ENDPOINTconfiguration is active.
The values provided are common starting points and should be measured against the reader's own samples.