What the data for this category looks like
Core data for this category includes injection molding process cards, raw material drying records, mold histories, first-piece inspection reports, daily inspection sheets, material change and machine cleaning records, and more. Data sources include resin batch and property sheets, injection molding machine operating parameters, mold temperature controller data, workshop MES systems, and plastic part dimension and appearance inspection results. Data updates trigger synchronously for every production batch, mold swap, material change, and shift inspection. Each data entry includes fields such as raw material model, process parameters, mold number, and inspection results. It uses standard injection molding industry units including degrees Celsius, megapascals, seconds, batch numbers, and others.
What constraints these characteristics impose during deployment and upgrade
This page is compatible with FastGPT v4.17.0. Data for this category has high update frequency and scattered trigger scenarios. Updates synchronize for every production batch, mold swap, material change, and shift inspection. This requires support for high-frequency incremental synchronization during deployment, to avoid excessive system resource usage from full-volume data pulls. Multi-source heterogeneous data sources cover device parameters, inspection results, scanned paper documents, and more. Access configuration for different protocols must be supported to ensure stable data pulls from injection molding machines, mold temperature controllers, and MES systems. Significant differences exist in field structures across different documents. Flexible field mapping rules must be reserved during deployment to adapt to different types of business documents such as injection molding process cards and inspection sheets. Data version compatibility must be ensured during upgrades, to avoid abnormal question-and-answer logic caused by temporary data changes.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapt to high-frequency incremental synchronization data requests, avoid data pull interruptions caused by insufficient connection count |
REDIS_URL | Intranet Redis address with password authentication, memory configuration no less than 2000 MB | Cache frequently queried injection molding process parameters and inspection records, reduce database load |
STORAGE_S3_ENDPOINT | Node address adapted to workshop intranet storage, such as http://192.168.1.100:9000 | Store unstructured business data such as mold histories and scanned process cards, adapt to intranet deployment scenarios |
MONGODB_URI | Intranet MongoDB cluster address, enable read-write separation configuration | Store large volumes of batched injection molding production data, support flexible field queries |
ROOT_KEY | Randomly generated 32-character string | Manage highest-privilege access to the FastGPT backend, avoid unauthorized configuration modifications |
Three Common Mistakes
- Phenomenon: The interface displays "Model loading failed" or fails to call the locally deployed question-and-answer model. Cause:
REDIS_URLis not configured correctly, leading to cache failure and inability to normally pull context data for model calls. - Phenomenon: Vector database calls report the error "Reached the max retries per request limit". Cause:
DB_MAX_LINKis not configured to limit connection count, so high-frequency data pulls exhaust the database connection pool. - Phenomenon: Question-and-answer results do not synchronize with the latest process parameters after mold or material change. Cause: Incremental synchronization trigger rules are not set, only full-volume data updates run, so new data is not entered in a timely manner.
How to Confirm the Configuration is Correct
- Run a simulated query for injection molding process parameters. Verify that returned results match locally stored business document data, confirming data synchronization configuration is effective.
- Initiate multiple sets of high-frequency device data queries. Observe that system resource usage meets expected levels, confirming database connection configuration is reasonable.
- Upload a scanned mold history document. Confirm the document can be retrieved via keywords, confirming storage configuration is effective.
- Initiate a complete equipment inspection question-and-answer conversation. Confirm corresponding interaction logs generate in the backend, confirming permission and log collection configurations are correct.
The values provided are common starting points and should be measured against the reader's own samples.