This is a database and operations maintenance configuration guide for this scenario, compatible with FastGPT v4.17.0.
What the data for this category looks like
Data for this category includes pre-press files, imposition sheets, printing process sheets, ink color matching records, first article sample approvals, barcode inspection records, die-cutting and lamination records, and other daily documents. Data sources include customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrometer test results, and barcode detector readings. Updates are triggered per order, per production batch, and each time a printing plate is replaced. Data fields include order ID, printing plate version number, ink batch code, substrate model, color difference parameters, barcode readability values, production duration, and more. Some fields are string type, some are numeric test results, and there are no unified fixed units.
What constraints these characteristics impose on the database and operations maintenance link
High-frequency, multi-node data updates require databases to support high-concurrency writes and transaction consistency, to avoid data conflicts during plate changes or batch switches. Dispersed data sources with multiple content types require compatibility with both structured ledger fields and unstructured image and time-series document storage. Single production batch data volume increases with order complexity, so sufficient storage capacity must be reserved to handle peak writes. Complex data relationships require establishing association indexes between order numbers and documents from each link, to quickly associate full-process data during ledger summarization. Additionally, issues such as misprinted plates or barcode abnormalities can lead to full batch scrap, so a complete data audit trail must be retained to facilitate tracing the root cause of problems.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of documents such as printing process sheets and imposition sheets, ensuring indexes cover complete content |
| Single Search Limit | Top 150 entries | Matches the query scale of batch-level production data, avoiding returning excessive redundant data |
MONGODB_URI | Enter the self-built or hosted MongoDB connection string | Used to store unstructured document data such as pre-press files and first article sample approvals |
PG_URL | Enter the Supabase or self-built PostgreSQL connection address | Used to store structured ledger fields such as ink batch and barcode test values |
DB_MAX_LINK | 12–18 | Adapts to high-frequency plate change and batch update data writes, preventing connection pool exhaustion |
REDIS_URL | Enter the local cache service address | Caches frequently queried order association data to reduce database query latency |
Three common mistakes
- Phenomenon: MongoDB connection failure, returning connection timeout or authentication failure errors. Configuration according to official documentation does not resolve the issue. Cause: The setup does not adapt to the high-frequency write requirements of the printing and packaging processing scenario, has insufficient connection pool configuration, or lacks correct database access permissions in authentication information.
- Phenomenon: After connecting PostgreSQL to Supabase, batch data cannot be queried, returning permission denied or table does not exist errors. Cause: The access whitelist for the corresponding database has not been configured in Supabase, or read/write permissions for ledger data have not been granted.
- Phenomenon: An error
text index required for $text queryis reported during knowledge base query, making production report generation impossible. Cause: A full-text index has not been created for the fields storing printing process sheets and imposition sheets, causing text queries to fail to execute normally.
How to confirm the configuration is complete
- Run a database connection test to verify that the configurations for
MONGODB_URIandPG_URLcan successfully establish connections and read test table data. - Submit a test data set for a simulated production batch to verify that data can be written to the database according to the configured update rhythm and completed association.
- Trigger a production ledger summarization task, check that the returned fields include category-specific data such as order numbers, ink batches, and test results.
- Run a text query test to verify that the created full-text index can normally return matching results for printing process sheets and imposition sheets.
The values provided are common starting points and should be measured against the reader's own samples.