What data for this category looks like
Typical data for this category comes from ERP systems, WMS systems, barcode/RFID equipment, purchase work orders, storage location records, AGV transport systems, and weighing equipment. Daily generated documents include receiving slips, putaway slips, material picking and return slips, inventory checklists, lot ledgers, and more. Fields include material codes, lot numbers, storage location numbers, expiration dates, inspection statuses, quantity units, and others. Data update frequencies cover real-time scenarios such as AGV positions and weighing data, daily scenarios such as inventory changes, and monthly inventory reconciliation.
What constraints these characteristics impose on the model access and configuration step
Dispersed data sources and widely varying update rhythms require model access to support time-series alignment of multi-source data, and avoid field format conflicts across data sources. Document fields are numerous and involve strongly correlated attributes such as lots and expiration dates. This requires limiting the model’s context length to accurately match material and lot information. High-frequency updates of real-time data require configuring calls adapted to short response delays. Bulk data from monthly inventory checks requires adjusting the model’s output token limit to handle long text. Additionally, the core goal of matching physical and book inventory requires that process documents generated by the model strictly match the standardized formats of fields such as storage locations and lots.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context loading for long documents such as lot ledgers and monthly inventory checklists, avoiding truncation of critical lot-related information |
maxTokens | 2000–3000 token | Meets full generation requirements for process documents and exception reports, while controlling response time per single call |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring output accuracy for strongly correlated fields such as material codes and lot numbers |
| Workflow/API tool selection | auto | Automatically triggers tool calls to retrieve real-time inventory and lot expiration date data, adapting to dynamic information needs in warehousing scenarios |
AIPROXY_API_TOKEN | Exclusive key bound to the deployed model service | Ensures secure identity verification for model calls, preventing unauthorized access |
Three common misconfigurations
- Phenomenon: Material lot numbers, storage location numbers, and other fields in generated exception reports are empty or use inconsistent formats. Cause: The
temperatureparameter is not adjusted for strongly correlated fields such as lots and storage locations, leading to excessive generation randomness that causes errors in matching key fields. - Phenomenon: Intermediate results from tool calls are displayed in the chat window, interfering with the readability of the final process document. Cause: The output hiding rule for tool calls is not configured correctly, and full tool call logs are retained by default.
- Phenomenon: Context truncation errors occur after calling the model, resulting in incomplete generated process documents. Cause: The
maxContextparameter is set too small, failing to load complete lot ledgers or inventory checklist data, so the model cannot obtain sufficient associated information.
How to confirm the configuration is valid
- Upload a real receiving slip and lot ledger, trigger process document generation, and verify the output accuracy of fields such as material codes, lot numbers, and storage locations.
- Simulate a book-versus-physical inventory discrepancy scenario, trigger exception report generation, and check whether associated data such as inventory and work orders is called.
- View the model call logs in FastGPT v4.17.0 to confirm that the actual effective values of parameters such as
maxContextandmaxTokensmatch the configured values. - Test the tool call function to confirm whether the output of intermediate steps can be adjusted to the desired display state.
All provided configuration values are common starting points. Adjust them based on your own specific samples and use cases.