What the data for this category looks like
Typical entities include raw material warehouses, line-side warehouses, finished product warehouses, and logistics and material control departments. Daily documents include receiving slips, putaway slips, material picking and returning slips, warehouse transfer slips, inventory reports, lot ledgers, first-in-first-out and expiration date records. Data sources include ERP/WMS systems, barcode or RFID equipment, purchase work orders, warehouse location information, AGV transport systems, and weighing data. Update frequencies fall into three categories: real-time (such as AGV location and barcode scan data), daily (such as material picking and returning records), and monthly (such as inventory reports). Core fields include material code, lot number, warehouse location, expiration date, and inspection status. Common units include pieces, kilograms, cubic meters, and similar units.
Constraints on model access and configuration
Multi-source heterogeneous data sources require model access to adapt to different data formats. Corresponding proxy or interface adaptation parameters must be configured. Differences in update frequencies require index refresh strategies to match data update rates. Real-time data requires high-frequency synchronization. Monthly data can be updated in batches as needed. The specialized nature of core fields requires model retrieval and generation to accurately match exclusive fields such as material codes and lot numbers. Generalized processing must be avoided. The rigor of audit tasks requires model parameters to lean toward low randomness. This ensures consistent results.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Audit tasks for this category need to cover multiple lot ledgers, inventory reports, and work order data. Sufficient context ensures complete information coverage |
maxTokens | 3000–4000 token | Audit tasks do not require overly long generated content, avoiding redundant output that reduces audit efficiency |
quoteMaxToken | 1500–2000 token | Field information from multiple documents such as receiving slips and warehouse transfer slips must be referenced. Limiting quoted tokens focuses processing on core data |
temperature | 0.2–0.4 | Audit tasks require rigorous results. Lower temperature reduces generation deviations |
AIPROXY_API_ENDPOINT | Fill in a dedicated address based on the deployment environment | Adapt to access requirements for multi-source data including ERP/WMS and barcode systems |
| Workflow/API tool selection | auto | Tools are needed to parse unstructured data such as barcodes and RFID. Automatic tool selection improves processing efficiency |
Three common configuration mistakes
- Issue: Persistent errors are returned when accessing third-party embedding services. Cause: No separate index model is configured, and parameters from the chat model are incorrectly reused. This causes a mismatch between embedding dimensions and material fields for the warehousing category.
- Issue: Request errors are triggered when uploading lot ledger PDFs. Cause: The
visionparameter is not correctly enabled, preventing the model from parsing key fields such as barcodes and expiration dates in PDFs. - Issue: Results returned when checking material lots do not match ERP data. Cause: The
maxContextvalue is too small, failing to cover all receiving slips and warehouse transfer slips associated with the current work order, leading to missing context.
How to confirm proper configuration
- Test reading a single receiving slip document, and verify that the model can correctly extract material code, lot number, and supplier information.
- Initiate a batch material check task, and verify that returned results match synchronized data from ERP/WMS systems.
- View model call logs in FastGPT v4.17.0, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave no format errors. - Adjust the
temperatureparameter, and verify that the rigor of generated results meets audit requirements.
The values provided here are common starting points, and should be evaluated against the user’s own samples.