What the data for this category looks like
Documents involved in daily operations for this category include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, defect records, and more. Data sources cover raw fiber inspection reports, enterprise ERP systems, real-time data from spinning machines and looms, evenness testers, test results from fabric inspection machines, and order specification documents. Operational teams adjust data update frequency to match business rhythm; each batch of raw materials, each shift’s production run, and each bolt of fabric produced generates new business data. Document fields include fiber grade, blending ratio, spinning twist, weaving structure parameters, defect type, lot number, and other items, with specific units such as twist, fabric length, and batch number.
What constraints these characteristics impose during deployment and upgrade
Multi-source heterogeneous data sources require interface adaptation between different systems during deployment to prevent missing data collection or format errors. High-frequency business data updates require adjusting the trigger logic of the index refresh mechanism during upgrade to match the update rhythm of each raw material batch, each shift, and each bolt of fabric, ensuring real-time performance of process documents and exception reports. Diverse document structures and specific field units require dedicated field mapping rules to ensure parsed business parameters comply with model call format requirements. The large volume of batch data from high equipment headcount requires advance planning of storage capacity and connection pool limits to avoid performance bottlenecks during concurrent access.
How to set the configurations
These configuration items apply to the FastGPT v4.17.0 deployment and upgrade workflows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Matches the high-concurrency business data access needs of spinning and weaving scenarios, prevents connection pool overflow |
MONGODB_URI | Fill in according to the actual deployed MongoDB instance address | Used to store unstructured business data such as defect records and process documents |
REDIS_URL | Local Redis instance address on port 6379 | Caches frequently accessed process parameters and test results to improve query efficiency |
STORAGE_S3_ENDPOINT | Fill in according to the self-built object storage endpoint, set single file maximum size to 1000 MB | Used for batch storage of large-volume files such as equipment operation records and inspection reports |
ROOT_KEY | Exclusive key generated according to platform security specifications | Configures platform administrator permissions to ensure access security of core configurations |
Three common mistakes
- Phenomenon: Connection timeout error occurs after adding a local index model. Cause: The database connection pool upper limit was not adjusted according to the
DB_MAX_LINKconfiguration, causing concurrent connections from multiple data sources to exceed the limit. - Phenomenon: No API key entry is found when configuring a third-party model. Cause: The third-party model access switch was not enabled in the backend, causing the corresponding configuration interface to not load.
- Phenomenon: A 400 error is triggered when calling GPT-4o mini with a query containing yarn parameters. Cause: Dedicated field mapping rules were not configured, causing parameter units such as twists per meter to not match the format expected by the model.
- Phenomenon: A user count limit prompt appears during collaborative operations. Cause: The built-in collaborative quota parameter of the platform was not adjusted according to team size, causing the preset limit to be exceeded.
How to confirm the configuration is correct
- Upload a single cotton blending sheet or process setup sheet, check if the parsed fields include core business parameters such as fiber grade and spinning twist, confirm that the field mapping complies with business requirements.
- Initiate a simulated request for exception report drafting, check if the returned result includes content matching spinning and weaving scenarios such as defect types and treatment suggestions.
- View the data source connection status panel in the platform backend, confirm that the connection status of configuration items such as
MONGODB_URIandREDIS_URLis normal. - Trigger a batch document synchronization task, check if the synchronization completion time matches the business update rhythm of each raw material batch and each shift.
The values provided are common starting points and should be evaluated against samples specific to the actual deployment.