This document applies to FastGPT v4.17.0.
What the data for this category looks like
Data sources include customer drawings and special characteristic lists, ERP/MES systems, measurement and inspection tool collection data, equipment process parameters, and supplier quality certificates. Daily documents include control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data update frequency follows per batch, per shift, or continuous collection. Fields include batch number, part number, process parameters, test values, qualification judgments, supplier information, and others. Units include torque N·m, pressure MPa, dimensions mm, and additional standard engineering units.
Key constraints for database and operations & maintenance workflows
Multi-source heterogeneous data sources require the database to support storage of both structured ledgers and non-standard documents. The high-frequency update rhythm requires the database connection pool to handle concurrent write requests. Diverse document structures require the database to have flexible indexing and query capabilities. Constrained by vehicle customer quality systems, data accuracy and traceability requirements are extremely high. Complete backup and disaster recovery mechanisms are necessary. Massive data generated by mass production requires a reasonable sharding strategy to optimize summary efficiency.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Configure a multi-node read-write split authentication connection string | Support high-concurrency read and write demands for multi-source ledger data |
DB_MAX_LINK | 150–250 | Adapt to high-frequency data write requests from per-batch or per-shift continuous collection |
REDIS_URL | Enable persistence, set key expiration time to 6–12 hours | Cache frequently accessed part characteristic and process parameter data |
PG_URL | Configure a read-replica connection | Store archived document data such as PPAP and 8D reports |
Three Common Configuration Mistakes
- Phenomenon: After Docker deployment, logs continuously report slow operation timeouts, and MongoDB response latency is excessively high. Cause: The
DB_MAX_LINKparameter is not adjusted for mass production high-frequency data writes, so the connection pool cannot support concurrent requests. - Phenomenon: After starting the service, the management interface cannot be accessed, and the log prompts a MongoDB connection failure. Cause: The authentication information configured in
MONGODB_URIis incorrect, or the cluster node address configuration is invalid. - Phenomenon: After interrupting a conversation or closing the client, chat records are not saved to the database. Cause: The automatic database transaction commit mechanism is not enabled, so data is not persisted when the request is interrupted.
How to Confirm Proper Configuration
- Run a database connectivity test script to verify the connection status of
MONGODB_URIandREDIS_URL. - Simulate mass data write requests to check whether the database connection count falls within the range configured by
DB_MAX_LINK. - Submit archived documents in PPAP format to confirm that data is successfully stored in the corresponding database instance.
- Interrupt an ongoing ledger summary task to verify that all data is fully persisted.
The values provided here are common starting points, and should be evaluated against the reader's own samples.