What Data Looks Like for This Category
Technical documents for spinning and weaving scenarios typically include cotton blending lists, process setup sheets, equipment operation records, yarn inspection sheets, grey fabric inspection sheets, and defect logs. Data sources include raw fiber inspection reports, ERP systems, real-time data from spinning machines and looms, evenness tester results, information collected by fabric inspection machines, and order specification requirements. Data updates are synchronized with each batch of raw material feeding, each shift’s work progress, and each roll of fabric produced. Individual documents contain fields such as fiber indicators, twist parameters, weave specifications, defect types, and corresponding processing records. Most field units use metric values: twist is measured in twists per meter, and length is measured in meters.
Constraints Imposed on Deployment and Upgrade
The multi-source heterogeneous data sources, high-frequency update rhythm, and specific field formats of this category impose multiple constraints on deployment and upgrade workflows. Multi-source data access must support different formats from industrial instruments, ERP systems, and offline documents. Adaptation rules for data pulling must be configured in advance during deployment. High-frequency updates require stable database connections to avoid data synchronization interruptions caused by connection timeouts. Specific field units and business logic require format verification before vector database ingestion. During upgrades, ensure that field mapping rules are not overwritten. Additionally, a single batch of raw materials corresponds to multiple associated documents. Reasonable document association index rules must be configured to avoid missed associations during retrieval.
How to Configure Settings
The following configuration items are tailored for the spinning and weaving scenario using FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:secure_pass@10.0.0.1:27017/fastgpt?authSource=admin | Supports high-frequency read/write requirements for multi-source business data, enabling batch writing of device logs and inspection records |
REDIS_URL | redis://:redis_secure_pass@10.0.0.2:6379/0 | Caches frequently queried data such as process parameters and defect classifications to shorten retrieval response times |
STORAGE_S3_ENDPOINT | http://minio.intranet domain:9000 | Stores large-volume documents such as offline cotton blending lists and grey fabric inspection reports, adapting to intranet storage needs |
DB_MAX_LINK | 80–120 connections | Handles real-time data upload requests from multiple spinning and looms, preventing connection pool resource exhaustion |
| Single recall limit | Top 8 entries | Matches the range of process parameters and defect records associated with a single roll of fabric, ensuring retrieval results cover core business information |
| Document association threshold | Calibrated based on actual testing | Adapts to the association matching logic of cotton blending lists and corresponding batch yarn inspection records, avoiding missed associations or redundancy |
Three Common Misconfigurations
- Issue: When deploying on ARM architecture devices, logs display "exec format error", and model containers fail to start after restarting. Cause: Containers were built without using ARM-compatible base images, and the mirror source was not adjusted to obtain compatible versions.
- Issue: After Docker deployment, startup fails with the getPluginGroups interface returning a 500 status code, and the interface shows that the commercial version link is not configured. Cause: The open-source version deployment did not correctly configure
ROOT_KEY, or the mandatory verification logic for commercial version plugins was not disabled, causing the system to attempt requests for unconfigured commercial interfaces. - Issue: After deployment, document parsing tasks cannot execute, and the interface shows parsing failure. Cause: The sandbox service was not started, or the connection parameters for the sandbox were not configured, preventing successful extraction of document content.
How to Verify Successful Configuration
- Log in to the FastGPT admin backend’s database monitoring panel, confirm that the connection statuses corresponding to
MONGODB_URIandREDIS_URLshow normal. - Upload a cotton blending list and the corresponding batch’s yarn inspection records, trigger an association retrieval operation, and confirm that the retrieval results include the core business fields of both documents.
- Perform a test operation to batch import 10 grey fabric inspection records, confirm that the service does not display connection timeout or write failure prompts.
- View the sandbox service’s running logs, confirm that no connection exception or startup failure error messages appear.
The values provided are common starting points and should be measured against the reader's own samples.