What this category's data looks like
Typically, business data for the mold design and manufacturing category originates from customer product models, mold flow or forming simulation files, CAD/CAM design files, machining and measurement data, and test sample part inspection results. Daily documents include mold design task sheets, DFM reports, part drawings, machining process cards, mold trial reports, mold modification notices, and mold history records. Data is organized by project, with updates triggered each time a mold trial or modification is performed. The data includes structured fields such as mold number, project ID, mold trial count, and machining duration, using units like millimeters and hours. It also includes unstructured drawings and report files.
What constraints these characteristics impose on database and operations workflows
Since data sources cover semi-structured and unstructured content from multiple tools, the database must support both document storage and relational metadata management. Given the irregular update schedule for projects, mold trials, and modifications, the database must support incremental read/write operations to avoid resource strain from full-volume batch tasks. Multiple document types and associated fields require clear data association rules to ensure core identifiers like mold number and project ID remain unique. Additionally, mold delivery delays directly impact mass production timelines. The database must maintain high availability to prevent data inaccessibility during maintenance operations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://mold-admin:secure-pwd@db-host:27017/mold-doc-db?authSource=admin | Stores semi-structured documents such as mold drawings and mold trial reports. MongoDB's document model is suited for multi-format content storage |
PG_URL | postgresql://metadata-user:md-secure@pg-host:5432/mold-metadata-db?sslmode=require | Stores structured metadata such as mold number, project ID, and mold modification records. Relational databases ensure consistent data association |
DB_MAX_LINK | 15–25 connections | A single mold project will initiate read/write requests for multiple ledger data entries. This connection count suits concurrent task scenarios for small and medium-sized mold enterprises, calibrated via actual testing |
REDIS_URL | redis://redis-host:6379/0 | Caches frequently accessed basic mold information and mold trial status identifiers to reduce repeated database query overhead |
| Data Backup Strategy | Trigger full backups when a project is completed, trigger incremental backups after each mold modification | Mold iterations occur frequently. All versions of ledger data must be retained to avoid data loss caused by mold modification errors |
Three Common Misconfigurations
- Phenomenon: After starting FastGPT v4.17.0, connection to the MongoDB database fails. The console returns a
Connection refusederror. Cause: The host address and port forMONGODB_URIare not configured correctly, and the firewall access permissions for the database server are not opened. - Phenomenon: When running locally, calling the ledger API returns a
500 Internal Server Error. Structured metadata cannot be written to PostgreSQL. Cause: The access permissions forPG_URLare not configured, or the corresponding data table structure is not pre-created in the database. - Phenomenon: Mold trial count statistics in the ledger are abnormal, with duplicate records counted multiple times. Cause: Incremental update logic is not configured. Each mold trial data upload overwrites existing records in full instead of appending new mold trial entries, leading to distorted data statistics.
How to Confirm Proper Configuration
- Log in to the background database configuration page of FastGPT v4.17.0, verify the connectivity of configuration items such as
MONGODB_URI,PG_URL, andREDIS_URL, and check the connection success prompt returned by the system. - Upload a mold design task sheet, check whether the system can correctly extract core fields such as mold number and project name, write structured data to PostgreSQL, and store unstructured files to MongoDB.
- Initiate an import operation for mold modification records, check whether the corresponding incremental backup file is generated in the database, and confirm that the data backup strategy is triggered normally.
- Initiate ledger query requests for three different mold projects at the same time, check that there are no connection limit error messages in the console, and confirm that the
DB_MAX_LINKconfiguration suits the current concurrent scenario.
The values provided are common starting points and should be measured against the user's own samples.