What the data for this category looks like
The daily documents for this category include process sheets, CAD patterns, marker plans, cutting orders, first article sealed samples, process quality sheets, packaging and size matching sheets, etc. Data sources cover orders and size charts, CAD design patterns, fabric and accessory inspection records, hanging production systems, needle inspection equipment, and customer sealed samples. Data updates are adjusted with each order, each garment style, and each production batch, with no fixed cycle. Fields include pattern parameters, process requirements, fabric specifications, size ratios, packaging and size matching rules, etc. The format changes flexibly based on customer needs and production batches.
What constraints these characteristics impose on deployment and upgrade
Data for this category is updated frequently with production batches. During deployment, efficient incremental indexing configuration must be adapted to avoid impacting real-time Q&A services. Non-pure text files such as CAD patterns and marker plans require adapted file parsing rules during deployment to ensure correct content extraction for these formats. Multi-source and heterogeneous document structures require the system to support custom field mapping to avoid missing key process parameters during retrieval. Production-stage Q&A relies on real-time and accurate process data. Deployment and upgrades must ensure no service interruption, and configuration adjustments must not affect calls from existing production processes.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Dedicated instance connection string for local deployment, with read-write separation nodes configured | Data volume for this category grows continuously with production batches; read-write separation reduces retrieval latency |
REDIS_URL | Dedicated instance with 16 GB memory configured, timeout set to 300 seconds | Frequently used process data needs to be cached for high-frequency Q&A scenarios; 300 seconds matches the context duration of a single production Q&A |
FILE_TOKEN_KEY | Encryption key consistent with the file storage service | Ensures permission verification passes for parsing non-text files such as CAD patterns and marker plans |
DB_MAX_LINK | Set to 200–300 | Adapts to process Q&A requests initiated by multiple workshops simultaneously, avoiding connection exhaustion |
STORAGE_S3_ENDPOINT | Bind the intranet address of the local object storage service | Reduces network latency for file reading, adapting to real-time calls during production |
FE_DOMAIN | Configured as the intranet access address for workshops | Restricts external access, only open to personnel related to production |
Three common mistakes
- An error occurs when opening the workflow after local deployment, with the prompt "Cannot read properties of undefined (reading 'incl')". The cause is that the
FILE_TOKEN_KEYconfiguration does not match the file storage service, leading to initialization failure of the file parsing module. - An error occurs when creating a new knowledge base. The cause is that the instance configured in
MONGODB_URIhas insufficient permissions to create the dedicated data collection required for process Q&A. - No valid results are returned when calling the content extraction module. The cause is that no adapted file parsing rules for CAD patterns and marker plans have been configured, preventing core content extraction from non-text format process documents.
How to confirm configurations are correctly set
- Log in to the FastGPT backend, import a garment process sheet or CAD pattern file, and confirm that process parameters and pattern information can be parsed and extracted normally.
- Initiate a process Q&A request that includes a specific size ratio, and confirm that the returned result contains the latest size matching rules and process requirements for the corresponding order.
- Check the database monitoring panel in the backend, confirm that the connection status of
MONGODB_URIandREDIS_URLis normal, with no timeout or error records. - Verify that the workshop intranet address can normally access the Q&A interface, and that external networks cannot log in to the system, ensuring access permissions meet production scenario requirements.
The values provided are common starting points and should be measured against the reader's own samples.