Data characteristics of this category
This category covers carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily documents include recipe sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspections, and finished product inspections. Data comes from water source and raw material testing, recipe systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS platforms. Updates occur per batch, per shift, or via continuous collection. Document fields include raw material batch numbers, sugar and acidity monitoring values, filling liquid level parameters, sealing pass rates, label batch numbers, and more. Units include numerical values, liquid level in millimeters, time in seconds, and other standard units.
Constraints on deployment and upgrade
This category’s high-volume multi-source data, frequent updates, and high-risk business attributes create multiple constraints for deployment and upgrade. Multi-source heterogeneous data sources require configuring compatible format access channels during deployment, to prevent data parsing failures. The per-batch or per-shift update frequency requires optimized incremental synchronization configurations during upgrades, to reduce resource usage from full synchronization. Frequent product switches drive document iteration needs, so a lightweight knowledge base version management module must be pre-configured during deployment. High-risk data in core processes requires enhanced storage and retrieval security configurations during upgrades, to avoid data leaks.
Configuration settings
This guide is written for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapts to high-frequency data connection requirements for continuous multi-source collection, avoids connection pool exhaustion |
MONGODB_URI | Intranet address of local replica set | Supports high-volume data storage for continuous collection in this category, ensures read/write stability |
STORAGE_S3_ENDPOINT | Intranet address of enterprise self-built object storage | Reduces transmission latency for high-volume documents and inspection data |
ROOT_KEY | Exclusive key generated by production line grouping | Isolates knowledge base access permissions for different production lines |
FILE_TOKEN_KEY | 16–32 character random string | Secures access to uploaded recipe sheets and inspection documents |
Three common configuration mistakes
- Failing to configure a MongoDB replica set during deployment leads to a
Connection refusederror after startup. Continuous collection data for this category requires a stable replica set storage architecture; a single node cannot support high-frequency read/write operations. - The document input feature cannot load recipe sheets normally after an upgrade. This occurs when the knowledge base’s document parsing plugin is not updated to adapt to this category’s long document format.
- The number of retrieval results decreases abnormally after migration deployment. This occurs when the
DB_MAX_LINKparameter is not configured correctly, leading to insufficient connection pools to retrieve all device data.
How to verify correct configuration
- Run a data source access test. Confirm that documents and inspection data from each source can be synchronized to the knowledge base normally, then adjust synchronization frequency based on business scenarios.
- Start the FastGPT service. Check the MongoDB connection status to confirm the connection count does not exceed the configured
DB_MAX_LINKthreshold. - Upload a recipe sheet document. Verify that access permissions comply with preset grouping rules, then adjust key configurations based on production line requirements.
- Trigger an incremental update task. Confirm that the number of updated documents matches actual business output.
The values provided are common starting points, and should be evaluated against the reader’s own samples.