What data for this category looks like
Data comes from customer drawings, special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Daily generated documents include control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data updates occur per production batch, during each shift’s patrol inspections, and via continuous collection of equipment parameters. Document structures include fixed fields: part number, process number, characteristic parameters, test results, and exception records. Some documents must follow format and unit requirements specified by customers.
Constraints during deployment and upgrade
Data sources are dispersed, including customer drawings, production systems, equipment, and supplier materials. During deployment, configure format verification and field mapping rules for multi-source data. Multiple document types exist, and all must match customer-specified formats. The deployment phase must support bulk import of files such as control plans and 8D reports. Data updates happen frequently with strict real-time requirements. Deployment and upgrade must support incremental synchronization to avoid overwriting historical production data. Exception reports must bind to part batch and process information. Retrieval and generation processes must retain consistent field and unit rules.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:xxxx@192.168.1.100:27017/auto_parts?authSource=admin | Stores multiple types of process documents and exception report data generated during high-volume production |
REDIS_URL | redis://:xxxx@192.168.1.101:6379/1 | Caches frequently accessed part characteristic parameters and process rule data to reduce database load |
STORAGE_S3_ENDPOINT | https://s3.example.com | Adheres to object storage interface specifications for FastGPT v4.17.0, supports storage for large-capacity process documents, SPC reports, and PPAP materials |
DB_MAX_LINK | 150–250 connections | Limits database concurrent connection count to avoid resource contention with on-site ERP systems |
FILE_TOKEN_KEY | 32-character random hexadecimal string | Ensures secure authentication for process document upload and download |
Three common configuration errors
- After container startup, logs display “CUDA error: out of memory” and the process document parsing model fails to load. The cause is failure to map the host machine’s GPU device to the FastGPT container, plus incorrect configuration of runtime parameters compatible with CUDA 12.8.
- Accessing the deployed FastGPT service returns a 404 Not Found error. The cause is a mismatch between the Nginx reverse proxy path and the FastGPT container’s internal port, with failed proper request forwarding to the container service.
- After bulk import of PPAP materials, document search results are empty. The cause is incorrect configuration of the
DB_MAX_LINKparameter, leading to insufficient database connections to complete document index construction.
How to confirm successful configuration
- Run the GPU detection command inside the container, confirm the CUDA version matches the host machine, and verify GPU resources are available to FastGPT.
- After configuring Nginx reverse proxy, use the curl command to test the proxy path, confirm requests are properly forwarded to the FastGPT container’s internal port.
- Upload a control plan document, check for generated database indexes, and verify storage and retrieval functions for the
MONGODB_URIconfiguration work correctly. - Trigger an incremental data synchronization task, confirm production data from ERP/MES systems syncs to the FastGPT knowledge base per preset rules.
The values provided are common starting points and should be measured against the reader's own samples.