This document is written based on FastGPT v4.17.0.
What the Data for This Category Looks Like
Data sources include resin batches and property sheets, injection molding machine operating parameters, mold temperature controller data, manufacturing execution system (MES), and dimensional and appearance inspection equipment. Daily generated documents include injection molding process cards, raw material drying records, mold history logs, first article inspection reports, patrol inspection forms, and material change and machine cleaning records.
Data is updated per production batch, each mold change or material change, and each shift handover. Data fields include resin batch number, injection pressure, mold temperature, drying duration, detected dimensional tolerance, material change time, and more. Corresponding units include MPa, ℃, hours, millimeters, and others.
Constraints on Database and Operations Link
Multi-source, high-frequency updated data requires databases to support high-concurrency connections and incremental synchronization to avoid data delays. Diverse document structures require databases to support mixed storage of structured and unstructured data, adapting to files in different formats such as process cards and inspection reports.
Frequent mold changes, material changes, and batch switches require the operations link to quickly perform associated queries for full-link data of the same batch. Strict access control is required to prevent leakage of sensitive data related to material mixing.
Injection molding data has strong timeliness. Caching mechanisms must adapt to frequently read real-time parameters to reduce database load.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/fastgpt?authSource=admin | Adapt to vector storage and associated query requirements for multiple batches and inspection data |
PG_URL | postgresql://username:password@host:port/fastgpt | Store structured static document data such as process cards and mold history logs |
DB_MAX_LINK | 20–30 | Handle high-frequency database connection requests from multiple mold and material changes per shift |
REDIS_URL | redis://:password@host:port/0 | Cache frequently read injection molding machine parameters and mold temperature data to reduce database load |
Three Common Misconfigurations
- Phenomenon:
Mongo connect init root user errorappears in the service startup log. Cause: Authentication parameters forMONGODB_URIare not configured correctly, or the database does not grant corresponding access permissions. - Phenomenon: Unable to add or delete vector database data via an external management system. Cause: The external access port for the FastGPT vector database is not open, or the specified data operation interface specification is not followed.
- Phenomenon: Segmented content is lost after knowledge base backup. Cause: Incremental backup of injection molding-related segmented knowledge bases is not performed per production batch. Direct full backup causes timeout or data truncation.
How to Verify Correct Configuration
- Run a database connection test command to verify connectivity for
MONGODB_URIandPG_URL, with no timeout or authentication failure prompts. - Submit simulated injection molding process card data to confirm that data can be stored normally and associated with corresponding batch inspection records.
- Check database connection count monitoring to confirm that connection counts do not frequently trigger the upper limit threshold of
DB_MAX_LINK. - Perform a knowledge base backup operation to verify that segmented injection molding-related documents can be fully exported to local storage.
The values given are common starting points and should be measured against the reader's own samples.