What the data for this category looks like
The data for this category comes from dyeing and finishing workshops' dyeing, setting, laboratory, and quality departments. Daily documents include dyeing formulas, chemical dosing records, dye vat curves, sample confirmation reports, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color swatches, formula libraries, dye and chemical batches, dye vat control equipment, colorimeters, setting machines, and environmental monitoring devices. Data is updated per vat, per batch, or per shift. Each individual record includes precise fields such as dye and chemical ratios, temperature curves, weighing values, and batch numbers. Some files include image-based sample confirmation results. The data structure primarily mixes structured numerical values and unstructured documents.
What constraints these characteristics impose on the deployment and upgrade phase
Since data sources cover industrial equipment and manual ledgers, the format mixes structured and unstructured content. Deployment must support parsing and storage for multiple file types. The high-frequency update rhythm (per vat, per batch, per shift) requires deployed storage and query services to have sufficient concurrent processing capacity. Individual process data includes precise parameters such as temperature, duration, and ratios. Deployment must ensure accurate field parsing to avoid data distortion. Data migration errors during upgrades can lead to loss of process records, which creates risks of rework or scrap in production. Therefore, reliable backup and migration processes must be configured. Containerized deployment solutions can be used to quickly adapt to configuration requirements across multiple environments.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host address]:27017/fastgpt_dyeing?authSource=admin | Dyeing process data includes unstructured document-type content such as dye vat curves and color difference reports. MongoDB adapts to this storage requirement. A dedicated database and authentication information must be configured. |
REDIS_URL | redis://:[password]@[host address]:6379/0?maxmemory=1000 MB&maxmemory-policy=allkeys-lru | Used to cache frequently queried process Q&A results and session status. The 1000 MB memory configuration can meet daily caching requirements and avoid frequent database reads. |
STORAGE_S3_ENDPOINT | https://s3.[region].aliyuncs.com/fastgpt-dyeing/ | Stores large files such as dye vat curves and sample photos. Storing files categorized by batch number allows quick location of associated data for a single vat. |
DB_MAX_LINK | 20–30 connections | Adapts to high-frequency data writes and queries per vat, per batch. Too few connections will cause read/write blocking, too many will exceed database load limits. Set based on actual testing. |
ROOT_KEY | Randomly generated 32-character string | Used for admin backend permission control. A high-strength key prevents unauthorized access to process data, meeting security requirements for production scenarios. |
FILE_TOKEN_KEY | Randomly generated 16-character string | Used to generate temporary tokens for file access, avoiding direct exposure of public access permissions for the storage bucket. |
Three common mistakes
- The symptom is that some dye vat process records cannot be retrieved after upgrade. The cause is failing to back up the database corresponding to
MONGODB_URIin advance and run the incremental migration script, resulting in failure to synchronize metadata for existing batches to the new database structure. - The symptom is connection timeouts or 403 status codes when calling overseas large models. The cause is failing to configure correct proxy parameters or API key permissions in the environment variables of FastGPT v4.17.0, preventing normal access to model services.
- The symptom is only single-threaded execution when running batch process Q&A tasks. The cause is failing to adjust
DB_MAX_LINKto a reasonable range, resulting in insufficient database connection pool to support concurrent requests from parallel tasks.
How to confirm configurations are correct
- The FastGPT admin backend can be accessed, and a check performed to confirm that files such as dye vat curves and sample photos categorized by batch number exist in the storage directory, verifying storage configuration is effective.
- A test process Q&A request may be submitted, with verification that the returned result includes matching process data such as dyeing formulas and temperature parameters, confirming database connection configuration is normal.
- A batch Q&A task may be started, with a check of the task execution status to confirm that parallel running entries exist, verifying concurrent connection configuration is effective.
- An attempt to access the admin interface using an invalid key may be made, confirming permission control configuration is effective.
The values provided are common starting points and should be evaluated against the reader's own samples.