What data looks like for this category
Daily documents for printing and packaging processing include prepress files, imposition sheets, printing process sheets, ink color matching records, first article approval samples, barcode inspection records, die-cutting and lamination records. Data comes from customer submissions, color management systems, printing press operational data, ink and substrate batch records, spectrometer test results, and barcode detector data. Data updates follow per order, per batch, and each plate change. Multiple associated documents generate synchronously during a single batch plate change. Document fields include version number, color parameters, substrate model, batch number, test results, and more. Fields across different documents have high correlation, requiring synchronous updates.
Constraints imposed by these characteristics on deployment and upgrade
This category has high data update frequency and strong data correlation. Deployment must ensure storage and computing resources support concurrent workloads. Heterogeneous data from multiple sources requires configuring input interfaces compatible with different formats to avoid data parsing failures. Error consequences include full batch product scrap. Deployment must configure failure retry and log retention mechanisms to ensure traceability of exceptions. Internal office access restrictions require configuring secure domain names and permission rules to prevent leakage of core process documents. Upgrades must synchronously adapt database connection counts and caching policies to avoid data reading errors caused by version updates.
How to set configurations
All configurations below are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Point to an independently deployed MongoDB instance | Process document and exception report data for printing and packaging processing grows in batches per order. An independent instance avoids resource contention with other business operations |
REDIS_URL | Configure as a local Redis cluster, set cache validity to 300 seconds | This category has an update frequency of per order, per batch, and each plate change. Caching reduces overhead from repeated reading of spectrometer and barcode detector data |
STORAGE_S3_ENDPOINT | Bind to an object storage endpoint that supports large file chunked upload, set chunk size to 50–100 MB | Prepress files and first article approval sample files have large individual sizes. A reasonable chunk size avoids upload timeouts |
DB_MAX_LINK | Set to 100–150 connections | A single batch plate change generates multiple process documents simultaneously. Sufficient database connection counts support concurrent write and read operations |
FE_DOMAIN | Configure as the enterprise internal office domain name | Only allow internal office devices to access the process document drafting tool to protect enterprise core document security |
FILE_TOKEN_KEY | Generate a 32-bit random string as the secret key | Used to sign access tokens for process documents, preventing unauthorized access to internal documents |
Three common mistakes
- Docker build throws error
ERROR: failed to solve: archive/tar: unknown file mod. The cause is incorrect handling of file permissions for raw log files exported by printing presses during packaging, leading to abnormal tar archive formatting. - API request returns 404 - Resource not found. The cause is incorrect configuration of
STORAGE_S3_ENDPOINTto point to the correct object storage path, leading to failure to read process files normally. - Process document review results are empty after upgrade. The cause is failure to adjust
DB_MAX_LINKto match the new database connection count, leading to connection exhaustion during concurrent reads.
How to confirm the configuration is correct
- Run the
docker pscommand. Confirm all FastGPT containers are running normally, with no abnormal exit statuses. - Upload a prepress PDF file. Check that parsing completes normally and a process document draft is generated, with no parsing failure prompts.
- Trigger an exception report generation process. Check that the system log contains no errors related to
archive/taror database connection timeouts. - Verify that the database configured via
MONGODB_URIcan write imposition sheet data, with no connection timeout or write failure prompts.
The values provided are common starting points, and should be evaluated against the user’s own samples.