What data looks like for this category
This category covers liquid milk, yogurt, milk powder, and dairy ingredient enterprises. Daily business documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, and sample retention and microbial testing reports. Data sources include milk source testing equipment, MES production management systems, homogenization and heat treatment equipment, online temperature and flow monitoring devices, CIP cleaning systems, and LIMS laboratory management systems. Update frequency is per milk tank, per batch, or real-time. Core business parameters include raw milk antibiotic and physical and chemical indicators, heat treatment time and temperature, aseptic filling status, and other related fields.
What constraints these characteristics impose on the deployment and upgrade phase
Multi-source heterogeneous real-time data requires deployment to support low-latency caching and storage capabilities to avoid data loss or synchronization delays. High-frequency batch updates require the database connection pool to adapt to concurrent requests, preventing business interruptions caused by exhausted connections. The continuous production nature of perishable liquid raw materials requires upgrade processes to ensure core services are not interrupted, avoiding impact on normal production workflows. Field differences across different devices and systems require configuring unified data mapping rules during deployment to ensure consistent fields in process documents and exception reports. Additionally, the business characteristic of severe error consequences requires configuring complete failure retry and data verification mechanisms during the deployment phase to reduce exception risks.
How to set the configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
REDIS_URL | redis://<redis-host>:6379?password=<your-password> | Adapts to real-time data caching requirements. Using the Redis image from the Alibaba Cloud Container Image Service resolves pull latency issues and prevents deployment failures |
MONGODB_URI | mongodb://<user>:<pass>@<mongo-host>:27017/fastgpt?authSource=admin | Stores large volumes of batch records and microbial testing reports, matching the high-frequency update rhythm per milk tank or batch |
STORAGE_S3_ENDPOINT | oss-cn-<region>.aliyuncs.com | Stores large documents such as CIP cleaning records and sample retention microbial testing reports, meeting compliance archiving requirements for food processing |
DB_MAX_LINK | 200–300 connections | Handles concurrent connections from real-time reports by multiple devices, preventing database connection exhaustion |
FE_DOMAIN | Enterprise-specific domain | Binds a dedicated access domain to ensure controllable permissions for process documents and exception reports |
Three common mistakes
- Phenomenon: Redis image pull failure during docker-compose deployment, with console prompts for pull timeout or non-existent image. Cause: Alibaba Cloud container image acceleration is not configured, or the correct Redis image address is not specified, leading to pull latency or insufficient permissions from the default image repository.
- Phenomenon: After submitting a raw milk acceptance record, the system fails to generate the corresponding exception report draft, and some fields display as empty. Cause: The
MONGODB_URIconfiguration is incorrect, failing to correctly point to the database instance storing batch data, leading to inability to read core business data. - Phenomenon: When synchronizing CIP records from multiple devices in batches, the system returns connection timeout or 503 errors. Cause: The
DB_MAX_LINKsetting is too small, unable to support concurrent connection requests from multiple devices reporting simultaneously.
How to confirm the configuration is correct
- Execute the
docker-compose up -dcommand, check the startup status of core containers such as Redis and MongoDB, and confirm there are no abnormal exit logs. - Upload a simulated raw milk acceptance record, confirm the system automatically associates the corresponding batch's standardized ingredient list and generates a process document draft conforming to business formats.
- Adjust
DB_MAX_LINKto a test value, simulate 100+ concurrent connection requests, and confirm no database connection exhaustion errors occur. - Access the configured
FE_DOMAIN, confirm the preview pages for process documents and exception reports load normally, with no permission or resource loading failure prompts.
The values provided are common starting points and should be measured against the reader's own samples.