What the data for this category looks like
Data sources for this category include customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrometer test data, barcode detector results, and more. Daily documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first article sample approval reports, barcode inspection records, die-cutting and lamination process ledgers. Data update frequency follows per production order, per material batch, or each plate change. Each document contains fields such as plate version identifier, color parameters, barcode rules, process parameters, and others. There is no unified fixed unit, and values adjust dynamically based on specific production links.
What constraints these characteristics impose during deployment and upgrade
The high-frequency, multi-stage dynamic data updates for this category require deployments to adapt to high-concurrency, small-batch data write requirements. Multiple types of heterogeneous data sources (device-collected data, electronically converted paper records, original files submitted by customers) need to support import and parsing of different formats. Deployment links must reserve data format expansion interfaces. Real-time audit requirements for stages such as first article sample approval and barcode inspection require that the latency of the vector database and retrieval link be controlled within a reasonable range. The characteristic that plate errors can spread rapidly requires strengthening data version verification and duplicate content recognition capabilities during upgrades to avoid batch error diffusion. Some specialized scenarios such as pharmaceutical packaging materials require storage links configured to meet industry regulatory requirements for compliance data retention.
How to set the configurations
The following configuration items are designed for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://user:pass@host:5432/fastgpt?sslmode=disable | Adapts to the batch structured data storage needs of printing and packaging processing, compatible with vector plugin version 0.7.4 in a pg15 environment |
REDIS_URL | redis://:pass@host:6379/0?maxmemory-policy=allkeys-lru | Caches frequently accessed imposition sheets and first article sample approval data to reduce database query pressure |
STORAGE_S3_ENDPOINT | https://s3.cn-north-1.amazonaws.com.cn | Adapts to domestic storage links, retains traceability data such as ink batches and die-cutting records |
DB_MAX_LINK | 200–300 | Matches high-frequency data write requests per order and per batch to avoid connection exhaustion |
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt | Stores unstructured pre-press files and process sheet attachments, adapts to flexible document structures |
Three common mistakes
- Using the pgvector plugin version 0.7.4-pg17 when deploying a pg15 environment results in database connection failure errors. The cause is failing to match the plugin version corresponding to the PostgreSQL version, leading to database initialization exceptions.
- Pulling images fails when deploying locally using Docker Compose, returning timeout or image not found prompts. The cause is failing to configure domestic mirror sources, making it impossible to access the official DockerHub repository.
- Calling workflow API versions above v4.8.10 returns empty values, making it impossible to obtain knowledge base retrieval results. The cause is failing to correctly configure the associated parameters for workflow nested knowledge base assistants, leading to retrieval link interruptions.
How to confirm the configuration is complete
- Run the built-in database connection test script to verify that the
PG_URLandREDIS_URLconfiguration items can establish normal connections, with no authentication failure or timeout prompts. - Upload a printing process sheet and imposition sheet, check that the data parsing module can normally read fields such as plate version and color parameters, with no format parsing errors.
- Trigger a simulated first article audit process, confirm that the retrieval link can return matching process records and color matching data, with no empty results returned.
- View the storage service logs, confirm that the storage bucket configured via
STORAGE_S3_ENDPOINTcan normally write process records and batch traceability data.
The values provided are common starting points and should be measured against the reader's own samples.