What Data for This Category Looks Like
Data for this category originates from customer color swatches, formula libraries, dye chemical batch records, dye vat control equipment, colorimeters, setting machines, and environmental monitoring devices. Daily generated documents include dyeing formulas, chemical weighing logs, dye vat curves, proofing confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data updates follow a cadence of per vat, per batch, and per shift. Fields include color difference values, vat temperature parameters, feeding amounts, emission indicators, and more. Most data consists of structured logs exported from equipment, plus hand-filled electronic or paper forms.
Constraints Imposed by These Characteristics During Deployment and Upgrade
This category’s data combines industrial device exported logs and hand-filled forms. During deployment, multi-format data sources must be supported, and appropriate parsing rules and storage paths must be configured. Data updates happen at high frequency, with new content created for each vat, batch, and shift. After deployment, the vector database index update rhythm must be adjusted to prevent data synchronization delays. Professional industrial parameters such as color difference values and vat temperature curves require preset field parsing logic, to ensure the knowledge base accurately extracts core audit-related information. Compliance ledger data must be configured with strict access permissions, to protect the security of production and environmental data.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
REDIS_URL | redis://:custom password@deployment server address:6379/0 | Adapts to high-frequency session caching and vector index synchronization requirements, ensuring fast reading of multiple vat run data |
MONGODB_URI | mongodb://username:custom password@deployment server address:27017/fastgpt?authSource=admin | Stores structured audit documents such as dyeing formulas and color difference reports, supports high-concurrency writes |
STORAGE_S3_ENDPOINT | Internal enterprise object storage service address | Stores large-volume industrial log files such as dye vat curves and sewage ledgers, adapts to bulk data uploads |
DB_MAX_LINK | 100–150 | Matches the concurrent audit request volume of a single shift in a dyeing and finishing plant, avoiding database connection exhaustion |
FE_DOMAIN | Internal enterprise network access address or public network bound domain name | Adapts to scenarios of in-plant intranet deployment or providing external audit query services |
ROOT_KEY | Randomly generated 32-character string | Ensures access permission security for the system backend, preventing unauthorized operations |
Three Common Misconfigurations
- When creating a new knowledge base, a database connection timeout pop-up appears, and creation cannot be completed. The cause is that access permissions for
MONGODB_URIorREDIS_URLare not correctly configured, resulting in failure to establish a persistent storage connection. - When calling a large language model, empty results or a 500 status code are returned, and audit-related content cannot be generated. The cause is that the access address of the local large language model is not correctly configured in the environment variables, and network permissions for the corresponding port are not enabled.
- When batch uploading dye vat curve files, some files show upload failures. The cause is that a valid key for
FILE_TOKEN_KEYis not configured, resulting in upload verification failure for large-volume industrial log files.
How to Verify Successful Configuration
- Run the system's built-in database connection test tool to verify whether the configurations of
MONGODB_URIandREDIS_URLtake effect normally. - Upload a standard dyeing formula document, check whether the knowledge base can correctly extract key parameters from the formula, and confirm that the field parsing logic is working properly.
- Initiate a test audit request, check whether the system can normally return matching historical data, and verify that the vector database recall process is smooth.
- Check the object storage upload logs to confirm that the storage path configured via
STORAGE_S3_ENDPOINTcan normally write large-volume files such as dye vat curves.
The values provided here are common starting points and should be evaluated against the user’s own samples.