What the data for this category looks like
This category covers typical entities including carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily technical documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data sources cover water source and raw material testing systems, formula management systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection equipment, and LIMS systems. Data update frequencies include per production batch, shift-based inspections, and continuous device collection. Document fields include concentrate dosage, sterilization temperature, filling liquid level deviation, product batch number, and others. Some fields have standardized units attached.
What constraints these characteristics impose on deployment and upgrade
High-frequency, multi-source data updates require configuring stable read-write connection pools during deployment. This prevents production document processing interruptions caused by exhausted connections. Differentiated field structures across multiple document types require adapting multi-source parsing logic during deployment. This ensures accurate field extraction for different documents. Data comes from industrial equipment and management systems, with inherent format differences. Preprocessing rules must be configured during deployment to unify data formats and support cross-review. Severe error consequences require high availability for cross-review verification logic and data storage. Upgrades must ensure smooth configuration migration. This avoids impacting document verification in production links.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | This category has high-frequency data updates. A stable database connection pool must be maintained to avoid production document processing disruptions caused by exhausted connections |
MONGODB_URI | cluster address with read-write separation suffix | This category includes continuously collected device data and batch document sets. Read and write pressure is balanced. A cluster address can distribute load |
REDIS_URL | dedicated cluster address with password | This category needs to cache frequently queried formula parameters and device status data. This ensures fast response times for cross-review |
STORAGE_S3_ENDPOINT | domestically compliant object storage node address | This category includes sensitive data such as production records. Domestic nodes ensure data storage compliance and access speed |
FILE_TOKEN_KEY | 32-bit random string | Used to encrypt tokens for uploaded production documents. This prevents unauthorized access to production data |
ROOT_KEY | encryption key bound to deployment environment | Ensures permission security for system backend configurations. Prevents cross-environment configuration leaks |
Three common mistakes
- Symptom: Upload failure when uploading batch production documents larger than 2 MB. Cause: The configuration item for maximum upload file size was not adjusted. The default limit does not match the batch production document specifications for this category.
- Symptom: Timeout error when pulling deployment-related images on a Linux environment. The issue persists even after configuring a domestic mirror source. Cause: No dedicated acceleration domain name was specified for image pulling, or the mirror source configuration does not cover the full path of FastGPT official images.
- Symptom: The number of results returned by the associated vector retrieval logic does not meet cross-review requirements. Cause: The connection pool configuration was not adjusted based on the data update frequency of this category. This leads to insufficient database connections and inability to return complete matching results.
How to confirm correct configuration
- Upload a batch production document of up to 1000 MB. Check the upload progress and storage status to confirm no upload errors occur.
- Execute the image pull command in a Linux environment. Check that no timeout prompts appear during the pull process, and the image is successfully pulled locally.
- Log in to the FastGPT v4.17.0 system backend. Check the active connection count of the database connection pool. Confirm the count is within the configured
DB_MAX_LINKrange. - Call the associated model interface. Check that the returned result fields cover the core verification items for production documents of this category, and the matching degree meets preset requirements.
The values provided are common starting points and should be measured against the reader's own samples.