This document was written based on FastGPT v4.17.0.
What this category of data looks like
Data for this category covers the entire production workflow of automotive component suppliers including braking, steering, chassis, interior and exterior trim, and power systems. Daily documents include control plans, process flowcharts, work instructions, first-piece and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data sources include customer drawings and special characteristic lists, ERP/MES systems, measuring tool data, equipment process parameters, and supplier quality certificates. Data is updated per production batch, per shift patrol inspection, and continuously collects equipment and process parameters. Document fields include customer-specified special characteristics, process parameter thresholds, inspection result values, and supplier qualification information.
What constraints these characteristics impose on deployment and upgrade
The data for this category is multi-source, heterogeneous, and updated at high frequency. This requires deployments to support access to multiple formats including structured ERP/MES data, measuring tool export files, and real-time equipment data streams. The high-frequency update rhythm (per batch and per shift) requires configuring a near-real-time vector index refresh mechanism to avoid delays in audit data. Fixed-format quality documents such as PPAP and 8D reports require preset chunking and field extraction rules that match the customer’s quality system during deployment. Upgrades must retain this configuration to avoid damaging existing adaptation logic. Additionally, the concurrent scenario of batch production requires ensuring the system’s concurrent processing capability to handle audit data submitted by multiple processes simultaneously, avoiding system lag or request timeouts.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 | Adapts to concurrent connection requirements for multi-source data access and batch audits, preventing database connection exhaustion |
MONGODB_URI | Points to an intranet-deployed replica set instance | Stores large-volume quality documents such as PPAP and 8D reports; intranet deployment ensures data security |
REDIS_URL | Points to a local cache instance on port 6379 | Caches frequently queried customer special characteristic lists and process parameter thresholds to reduce database load |
STORAGE_S3_ENDPOINT | Access address of an enterprise intranet S3-compatible storage | Stores large files such as patrol inspection records and SPC reports from batch production, adapting to high-frequency write requirements |
FILE_TOKEN_KEY | A randomly generated 32-character string | Ensures access permission security for stored files, preventing unauthorized disclosure of quality documents |
Three common mistakes
- The version number displayed on the frontend page after upgrade does not match the actual deployed version. This occurs because frontend static resource files were not updated correctly, resulting in old version resources cached by the browser not being cleared.
- The knowledge base fails to display document lists normally. This occurs because the storage access permission corresponding to
STORAGE_S3_ENDPOINTwas not configured correctly, preventing the system from reading quality documents stored in the storage. - An error of empty model stream response occurs when calling tools, or logs cannot be viewed via the console for troubleshooting. This occurs because model tool call parameters were not configured correctly, long context requirements were not adapted, or the console log printing function was not enabled.
How to confirm the configuration is correct
- Perform a database connection test to verify that the connection number set via
DB_MAX_LINKsupports concurrent quality data queries. - Upload a PPAP document to verify that the system can correctly extract customer special characteristic fields from the document and match the preset chunking rules.
- Check system logs to confirm that frequently cached data via
REDIS_URLcan be updated and read normally, with no connection timeout errors. - Trigger a batch audit task to verify that the system can normally call the configured storage service, with no file read or write failures.
The values provided are common starting points and should be evaluated against samples relevant to the specific deployment scenario.