What the data for this category looks like
Data for this category is generated around Vehicle Identification Numbers (VIN). Sources include ERP/MES systems, VIN and configuration systems, supplier batch ledgers, workshop tightening tools, offline EOL inspection lines, and quality traceability systems. Daily documents cover production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, and compliance release documents. Data is generated in real time per vehicle and summarized and archived per shift. Core fields include VIN codes, part batch numbers, torque parameters, and inspection qualification status, among others.
What constraints do these characteristics impose on deployment and upgrade?
Mixed-model production centered on VIN requires fast VIN-based end-to-end data association during deployment. High-concurrency database connections must be configured. High-frequency updates of real-time tightening data and offline inspection data create storage and retrieval pressure. Sufficient database and cache resources must be reserved. Multi-source heterogeneous data access requires compatibility with diverse document and structured data formats during upgrades. Field mismatch issues are prevented. Large-scale mixed-model production has takt time requirements. Retrieval response speeds after deployment must match workshop work rhythms. Failure to meet this requirement disrupts production workflows.
How to set configurations
The following configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 50–80 | Supports high-frequency data requests generated per vehicle in real time, preventing database connection exhaustion |
MONGODB_URI | Points to a dedicated document storage cluster | Stores large-volume business data such as unstructured assembly work instructions and rework records |
PG_URL | Points to a dedicated structured data instance | Stores structured business data such as VIN lists, tightening torque data, and inspection reports |
REDIS_URL | Configures a high-availability cluster | Caches frequently queried VIN-associated configuration data, reducing database access load |
STORAGE_S3_ENDPOINT | Points to a workshop intranet storage node | Meets compliance requirements for local storage of production data and enables low-latency access |
FILE_TOKEN_KEY | Generated according to production data encryption rules | Protects access security for sensitive business data such as VIN and part batch numbers |
Three common misconfigurations
- Issue: Container fails to start after Docker deployment upgrade. Logs indicate container components fail to install properly. Cause: The correct version of the image was not specified during the upgrade process, leading to version mismatches for basic dependency components.
- Issue: Segments exceeding the ideal length are not automatically truncated after knowledge base splitting. Redundant long text content is returned during retrieval. Cause: Splitting logic was not configured for the chapter structure of assembly work instructions. Only generic delimiters were used to handle overly long paragraphs.
- Issue: External knowledge bases cannot be accessed during cross-instance deployment. Retrieval results are empty. Cause: Cross-node access keys and network access policies were not configured correctly, leading to failed data synchronization links.
How to verify successful configuration
- Log in to the FastGPT management backend, access the database connection monitoring panel, and confirm that the number of connections configured for
DB_MAX_LINKdoes not show exhaustion. - Upload an assembly work instruction document containing a VIN code, run an associated retrieval test, and verify that corresponding tightening data and offline inspection reports can be returned quickly.
- Run the version upgrade script, check that container runtime logs have no errors, and confirm that all service components start normally.
- Test cross-instance data synchronization, and verify that knowledge base data can be exchanged normally between different deployment nodes.
The values provided here are common starting points. They should be evaluated against local operational samples.