What the data for this category looks like
Packaging and printing yield and market data comes from three primary sources:
- Production order ledgers in the enterprise’s internal ERP system
- Real-time equipment operation data collected by printing press SCADA systems
- Revenue and expense records from the financial settlement module
A full accounting report for the previous day’s production batches is generated each early morning. Each report includes fixed fields for one production batch: production number, raw material input volume, finished product output volume, raw material purchase unit price, order settlement unit price, production time spent, and other standard fields.
Data is stored as structured tables. Each row corresponds to complete yield accounting information for one independent production batch. Field units include tons, square meters, yuan per kilogram, yuan per square meter, hours, and similar standard units. No semi-structured or unstructured redundant content is included.
What constraints these characteristics impose on deployment and upgrade
The data characteristics listed above create clear constraints for deployment and upgrade workflows:
- Cross-system data source integration requires configuring cross-system API access permissions during deployment. This prevents incomplete accounting caused by pulling data from only one module.
- Daily batch updates must run during non-peak hours. Scheduled tasks must be set to trigger during production gaps, avoiding occupation of production network bandwidth and disruption of normal operations.
- The fixed field format of structured tables requires vector database chunking to split data by batch, while preserving relational links between fields. This prevents chunking from breaking accounting logic.
- Monthly batch import requirements mean upload file capacity thresholds must match the total size of monthly settlement reports. During upgrades, compatibility with older ERP data field formats is required to avoid failures when importing historical data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CRON_SCHEDULE | 0 2 * * * | Matches the daily post-settlement data update rhythm of the packaging and printing industry, avoids occupying bandwidth during production hours |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Single-batch packaging and printing data documents may include multi-process accounting content, requiring sufficient parsing time |
RECALL_TOP_N | Top 8 entries | Packaging and printing batch data has high correlation, requiring recall of enough related batches for yield accounting reference |
SIMILARITY_THRESHOLD | 0.75–0.85 | Prevents recall of irrelevant, low-similarity batch data while covering similar batches from the same process |
VECTOR_CHUNK_SIZE | 600–800 characters | Single-batch accounting content for packaging and printing data has moderate length, retaining complete process correlation information after chunking |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch import of monthly packaging and printing production settlement data files |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: When configuring DingTalk integration, the open platform returns "Receiving message address verification failed". Cause: The FastGPT service port was not mapped to a publicly accessible IP and port, and the correct public address was not filled in the callback configuration.
- Symptom: Docker image pull fails during single-machine offline deployment. Cause: Full dependent images were not packaged offline in advance, and a network pull command was executed directly, making it impossible to obtain required component images such as LLM and vector database.
- Symptom: A large number of irrelevant non-packaging and printing batch data appears in knowledge base recall results. Cause: The
SIMILARITY_THRESHOLDconfiguration value is too low, leading to recall of low-similarity data from other categories.
How to confirm the configuration is complete
- Manually trigger a scheduled task once, check the FastGPT task logs to confirm no timeouts or errors occur during data pulling and parsing.
- Initiate a batch data recall query, verify that returned result fields match those of packaging and printing production data.
- Check the DingTalk callback configuration, use the verification tool provided by the open platform to test address connectivity, confirm no verification failure prompts appear.
- Check the status of locally deployed components, confirm that LLM, vector database, TTS, and STT services are running normally with no abnormal resource usage.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.