What the data for this category looks like
Business data for welded structural component manufacturing comes from design drawings, material quality certificates, welding equipment parameters, welding material warehouses, non-destructive testing systems, and personnel qualification ledgers. Daily produced documents include welding procedure qualifications, welding process specifications, welding material consumption records, welder qualification records, weld inspection reports, and repair orders. Data updates occur per weld, per batch, or per shift. Fields include weld number, base material grade, welding material model, welder certification number, inspection results, repair times, and more. Some documents include attachments in scanned file format.
What constraints these characteristics impose on deployment and upgrade
Business data for welded structural component manufacturing has scattered sources and high correlation. Deployment requires integration with multiple heterogeneous systems, so multi-data source integration rules must be configured. Data updates happen at high frequency and fine granularity, with each weld corresponding to independent data. Incremental knowledge base updates must adapt to high-frequency write scenarios to avoid synchronization delays. Business documents involve rigid qualification and process constraints, so retrieval and generation links must accurately match fields, without generalized calls. Exception report verification logic must cover structural failure risks. Deployment must reserve configuration space for fault tolerance and exception alerts.
How to set the configurations
This configuration guide applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host address]:27017/welding_struct?authSource=admin | Connects structured business data from welding material warehouses and non-destructive testing systems, stores documents such as weld inspection reports and repair orders |
DB_MAX_LINK | 20–30 connections | Adapts to high-frequency weld data writes and queries per shift, prevents database connection exhaustion |
REDIS_URL | redis://:[password]@[host address]:6379/0 | Caches high-frequency query results from welder qualification ledgers and welding material inventories, reduces repeated read pressure |
STORAGE_S3_ENDPOINT | http://192.168.1.100:9000 (use actual address of local storage node) | Stores large files such as scanned copies of welding procedure qualifications and design drawings |
FILE_TOKEN_KEY | Randomly generated 32-character secure string | Generates encrypted access tokens for uploaded process documents to prevent unauthorized access |
Three common mistakes
- Issue: When creating a knowledge base, locally deployed large language models cannot be selected, and no corresponding entries appear in the interface model list. Cause: The oneAPI access address was not correctly associated in the FastGPT model configuration file, or the model name did not fully match the model identifier returned by oneAPI.
- Issue: Speech synthesis function calls fail, only lossless transcription functions run normally. Cause: The model loading parameters for CosyVoice2-0.5B were not correctly configured, or the model has compatibility issues with the current FastGPT version.
- Issue: Database connection times out, and real-time data from welding material warehouses cannot be read. Cause: The
DB_MAX_LINKconfiguration value is too low, causing high-frequency requests to exhaust the database connection pool, or the database access port was not opened for corresponding permissions.
How to confirm the configuration is complete
- Run a database connection test to confirm that the configured
MONGODB_URIcan normally access data sources from welding material warehouses and non-destructive testing systems. - Upload a welding process specification document to verify that the file storage service can complete the upload and generate a valid access path.
- Trigger an exception report generation process to confirm that the configured model service can return output results that conform to the business format.
- Check system logs to confirm that database connection counts do not show persistent over-limit conditions.
The values provided here are common starting points and should be evaluated against the reader's own samples.