This document is developed for FastGPT v4.17.0, and addresses database and operations configuration needs for quality traceability and defect analysis workflows in complete vehicle final assembly scenarios.
What the Data for This Category Looks Like
The core of this category’s data is the VIN (Vehicle Identification Number) of each individual vehicle. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, off-line inspection reports, rework records, and consistency release documents. Data primarily comes from ERP/MES systems, VIN and vehicle configuration systems, supplier batch management tools, workshop tightening equipment, EOL inspection lines, and quality traceability systems. Data update frequencies cover two categories: real-time generation per vehicle, and batch summary per shift. Core fields include unique VIN identifiers, assembly parameters, inspection results, and rework status.
Constraints Imposed on Database and Operations Workflows
The requirement to link a single VIN to hundreds of assembly and inspection data entries demands database support for efficient exact-match queries and multi-table join operations. Real-time tightening and inspection data creates high concurrent write loads. Configure sufficient connection counts and caching mechanisms to maintain write efficiency. Mixed-line production generates massive volumes of structured data daily. Reasonably plan storage sharding strategies to avoid overloading single tables. Data association consistency directly affects subsequent quality traceability and defect analysis. Configure transactions to ensure atomicity of cross-table operations, preventing mismatches between VINs and their corresponding data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:password@127.0.0.1:27017/fastgpt_automotive?authSource=admin | Adapts to storage needs for multi-source structured and semi-structured data in vehicle final assembly scenarios, supports index queries centered on VIN |
DB_MAX_LINK | 200–300 connections | Addresses concurrent load from real-time per-vehicle writes and batch queries, prevents service interruptions caused by exhausted connections |
REDIS_URL | redis://127.0.0.1:6379/0 | Caches frequently queried VIN configuration data, reduces direct database query pressure and improves retrieval speed |
| Database Sharding Strategy | Shard by first 3 characters of VIN | Matches business needs for grouped queries by VIN in mixed-line production, distributes single-table data volume pressure |
| Data Backup Cycle | Full backup once per shift, incremental backup once per hour | Matches data update frequency, enables rapid recovery to the latest valid state during outages |
Three Common Configuration Errors
- Database connection errors such as
connection refusedorauthentication failedprevent FastGPT from binding to the database. Common causes include incorrect host address, port, or authentication information in theMONGODB_URIconfiguration, or missing access permissions for the target database. - Confused tool calling and knowledge base retrieval logic result in no specified preset response when no matching knowledge base content is found. Common causes include incorrect configuration of retrieval trigger conditions and branch logic, or failure to set retrieval and tool calling priorities according to business needs.
- Inability to read data after database cross-version upgrade, with some VIN-linked data missing or query timeouts. Common causes include failure to fully copy all index files when migrating database directories, or failure to run data format compatibility scripts during cross-version upgrades.
How to Verify Successful Configuration
- Run network connectivity checks to confirm that the database address and port configured in
MONGODB_URIare accessible. - Submit a simulated VIN query request to verify that the database returns corresponding assembly and inspection data, confirming that join query logic operates correctly.
- Trigger a knowledge base retrieval and tool calling workflow to confirm that a preset response is returned when no matching content is found, and that retrieval results match expected configurations.
- Check the database connection count monitoring dashboard to confirm that current connections do not exceed the limit set in
DB_MAX_LINK, with no exhausted connections.
The values provided are common starting points, and should be evaluated against the reader's own samples.