What Data for This Category Looks Like
Data for this category comes primarily from daily work documents and equipment-collected information for garment OEM/ODM and cutting and sewing workshops. Daily documents include process sheets, patterns, layout plans, cutting orders, first sample seals, process quality forms, and packaging size matching lists. Data sources include order and size charts, CAD patterns, fabric and accessory inspection records, hanging systems, needle detector data, and customer sample seals. Data updates follow the rhythm of individual orders, styles, and batches. Fields include cut piece specifications, size ratios, fabric batch numbers, process man-hours, and packaging size matching information. Units include meters, pieces, piece counts, and similar units.
Constraints on Database and Operations Maintenance
This category of data has a high update frequency, and single-batch data volume fluctuates with order complexity. The system must support high-frequency, small-batch writes and queries. Daily document types are diverse, so storage must accommodate both structured tabular data and unstructured drawings and image files. Data fields involve multi-dimensional associations, such as the binding relationship between cut pieces and processes, and between packaging size matching and orders. Reasonable associated indexes must be designed. The irreversible nature of fabric cut pieces means data cannot be modified arbitrarily after writing. Read-only permissions or versioned storage must be configured. High manual process density leads to elevated data entry error rates. The system must support data validation rule configuration and abnormal data alerts.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt_clothing?authSource=admin | Supports storage of diverse unstructured documents and associated queries between cut pieces and processes |
DB_MAX_LINK | 20–30 connections | Matches the high-frequency, small-batch database access rhythm of this category, preventing connection resource exhaustion |
REDIS_URL | redis://:redis_pass@host:6379/1 | Caches frequently accessed order size charts and layout plan metadata to reduce load on the primary database |
| Database connection timeout | 30 seconds | Adapts to workshop network fluctuations and equipment collection delays, preventing data loss from connection interruptions |
| Data write validation rules | Triggered per order batch | Matches the batch-based data update rhythm of this category, ensuring consistency of single-batch data |
Three Common Misconfigurations
- When calling a model to query MongoDB, the token consumption shown in the interface does not match the API backend logs. This happens because FastGPT preprocesses and concatenates field content during the database query phase. This additional context content is not displayed in the front-end token statistics.
- The Oracle database type cannot be selected in the database connection module. The current version only includes built-in MongoDB and PostgreSQL database drivers, and Oracle connection dependency packages are not integrated.
- Attempting to return JPG images via database queries as answer outputs results in empty results or garbled text. FastGPT only supports querying and returning structured text data by default, and parsing and conversion logic for binary file streams is not configured.
How to Confirm Proper Configuration
- Execute a ledger data write operation for a single order, and confirm that the active connection count shown on the database connection status panel falls within the interval configured for
DB_MAX_LINK. - Import a process sheet and cutting order dataset, and verify that the configured write validation rules intercept abnormal data missing size ratio information.
- Call the API to query cached layout plan metadata, and compare the results with a direct database query to confirm that the cached data matches the source data.
- Check backend logs, and compare the token consumption displayed in the front end with the token statistics values returned by the API to confirm that the two match.
The values provided are common starting points and should be evaluated against the reader's own samples.