What the data for this category looks like
This data covers business data from raw material warehouses, line-side warehouses, finished product warehouses, and logistics material control departments. Daily documents include receiving slips, putaway slips, material picking and return slips, warehouse transfer slips, inventory counts, lot ledgers, first-in-first-out records, and expiration date records. Data sources include ERP/WMS systems, barcode or RFID equipment, procurement and work order information, storage location records, AGV transport systems, and weighing data. Update frequencies include real-time synchronization, daily synchronization, and monthly inventory counts. Core fields include material identification, lot numbers, storage locations, expiration dates, and work order association information.
What constraints these characteristics impose on model integration and configuration
Data sources for this category are scattered, and update frequencies vary widely. Synchronization and association of multi-source data must be supported, so model integration requires support for format conversion across multiple data sources. Core fields have strong associations, so retrieval content must cover material lot, storage location, and expiration date information. This means the context window must be large enough to hold multiple types of document fragments. Errors directly impact production workflows, so retrieval result accuracy must be strictly verified to avoid generating inspection recommendations that do not match actual warehouse status. Additionally, document types are diverse, so appropriate retrieval ranges must be configured to cover documents for different business scenarios.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must cover multiple document types including lot ledgers, storage location information, and expiration date records, and sufficiently associate material identification with work order information |
maxTokens | 2000–3000 token | Equipment inspection and maintenance Q&A requires detailed troubleshooting steps, to avoid truncating critical operation guidance |
quoteMaxToken | 1500–2000 characters | Need to reference document fragments such as receiving slips and inventory counts, ensuring retrieval content fully covers material lot and storage location data |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call retrieval tools, adapting to complex Q&A scenarios involving multi-source data association |
temperature | 0.3–0.5 | Maintain accuracy of retrieval results, avoiding generating content that does not match actual material lots or storage locations |
AIPROXY_API_TOKEN | Exclusive key assigned by the platform | Adapt to permission verification requirements for multi-source data access, ensuring secure data calls |
Three common configuration mistakes
- Symptom: The inspection recommendations generated by the model do not associate material lots and storage location information for the current work order, and retrieval results do not match actual warehouse scenarios. Cause: The
maxContextvalue is too small, failing to cover key context data such as lot ledgers and storage location records. - Symptom: After deploying FastGPT v4.17.0, the OneApi page fails to load, and the interface prompts an interface error. Cause: The
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENconfiguration is incorrect, preventing connection to the model provider. - Symptom: Unable to directly operate when adding a default model provider via the interface, requiring submission of a PR to modify code. Cause: FastGPT's visual model management function is not enabled, or the access entry for the model provider is not configured.
How to confirm configuration is complete
- Submit an inspection question that includes material lots and storage locations, check whether the model output associates corresponding lot ledgers and storage location information.
- Access the OneApi page, confirm that the page loads normally with no interface error prompts.
- Test whether the integrated model can normally generate maintenance recommendations with retrieval basis, with no truncation or empty results.
- Adjust the
temperatureparameter, check the consistency of output results, and confirm that the configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.