What data looks like for this category
Typical data for spinning and weaving scenarios comes from daily production documents and industrial equipment used by cotton spinning, chemical fiber spinning, woven, and knitting enterprises. Daily documents include cotton blending or batching lists, process setup sheets, equipment operation logs, yarn inspection reports, grey fabric inspection reports, defect records, and more. Data sources include raw fiber inspection reports, ERP systems, real-time data from spinning and weaving machines, evenness testers, fabric inspection machines, and order specification files. Data updates align with production workflows. New records are created for each raw material batch, each production shift, and each roll of fabric produced. Fields include production-specific details such as raw material metrics, equipment models, process parameters, defect types, and inspection results.
What constraints these characteristics impose during deployment and upgrade
The data sources for this category cover industrial equipment, business systems, and paper or electronic production documents. During deployment, access configuration for multi-format data sources must be adapted to avoid data synchronization gaps. The high-frequency update rhythm requires that regular synchronization tasks be configured during deployment, ensuring knowledge base content aligns with real-time production data. During upgrades, compatibility with new data source field mapping rules must be maintained. The exclusive field structure of daily documents requires that knowledge base metadata tags correspond to production-specific classification dimensions such as raw material type, equipment number, and defect category. During upgrades, index rules must be adjusted to accommodate new fields. At the same time, production data grows continuously with each batch. During deployment, expansion space for storage and computing resources must be reserved to avoid performance bottlenecks as production scale increases later.
How to set configurations
The following configurations are formulated for FastGPT v4.17.0, and adapt to the needs of equipment inspection and maintenance Q&A for spinning and weaving scenarios.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt_user:your_pwd@192.168.1.20:27017/fastgpt?authSource=admin | Stores unstructured production documents such as defect records and process setup sheets, uses a dedicated database instance to isolate business data |
REDIS_URL | redis://:fastgpt_redis_pwd@127.0.0.1:6379/1 | Caches user-asked equipment inspection context and real-time equipment status, uses database 1 to isolate cached business data |
DB_MAX_LINK | 80–120 connections | Adapts to concurrent data query requirements for multiple spinning and weaving machines, avoids exhausting database connections |
FE_DOMAIN | https://fastgpt.spinning-weaving.com | Configures the front-end access entry, adapts to Nginx reverse proxy deployment scenarios |
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 | Stores large volumes of equipment operation logs and inspection reports, uses local object storage to reduce long-term storage costs |
Three Common Mistakes
- Scenario: 502 error occurs after Nginx reverse proxies to the FastGPT container. Cause: The internal container port is not correctly mapped to the Nginx listening port, and
proxy_passis not configured to point to the correct container service address. - Scenario: FastGPT fails to start after upgrading to v4.9.2, with a prompt that configuration parameters are missing. Cause: The
systemEnvrelated fields are not correctly configured in config.json as required by the documentation, and some older version parameters have been adjusted to the new configuration structure. - Scenario: 404 not found error appears after Vercel automated deployment. Cause: The project output path is not correctly configured, resulting in static resource files not being properly packaged and uploaded to the deployment platform.
How to Confirm Proper Configuration
- The configured
FE_DOMAINis accessed, and the FastGPT front-end interface is verified to load normally, with no resource loading failure prompts. - The database connection test script is run, and configuration items such as
MONGODB_URIandREDIS_URLare verified to connect normally to the corresponding database and cache instances. - A spinning process setup sheet is uploaded as a test document. After indexing completes, keywords such as "twist setting" and "thread break handling" are searched, and matching document results are verified to be returned.
- After Nginx reverse proxy is configured, the back-end interface is accessed via curl command, and normal business data is verified to be returned, with no connection timeout or error messages.
The values provided are common starting points and should be measured against the reader's own samples.