What the Data for This Category Looks Like
The core data for this category covers daily operations of 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, inventory transfer slips, inventory count sheets, lot ledgers, first-in-first-out and expiration date records. Data sources include ERP/WMS systems, barcode or RFID equipment, procurement and work order information, warehouse location archives, AGV or conveyor system data, and weighing measurement data. Data update frequencies include real-time (such as AGV scheduling data), daily (such as material picking and return records), and monthly inventory count data. Core fields include material codes, lot numbers, warehouse location numbers, expiration dates, and corresponding quantity units.
Constraints on Model Access and Configuration
The data characteristics of this category create multiple constraints for model access and configuration. Multi-source data with varying update frequencies requires models to support comparison logic between real-time scheduling data and static inventory count data. Multiple types of warehousing documents require models to adapt to field extraction rules for different formats, including receiving slips and lot ledgers. The need for precise matching of core fields such as lot numbers and expiration dates limits the model’s allowable divergent output parameters. On-site documents are often uploaded as images, so models must support visual parsing to read content from barcodes or handwritten records.
How to Configure Settings
This document is written for FastGPT v4.17.0. Specific configuration items and recommended values are listed below:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Warehousing cross-review requires comparing lot and warehouse location information across multiple documents, so sufficient context must be retained to complete cross-document verification |
maxTokens | 2000–3000 token | The content length of a single warehousing document typically ranges from 1000 to 2000 characters, and sufficient token space must be reserved for outputting verification results |
temperature | 0.1–0.3 | Cross-review requires strict matching of field rules to avoid generating verification conclusions that do not align with business logic |
vision | Enabled | Adapts to on-site uploaded documents in the form of barcode and RFID tag images, and supports direct parsing of key information in images |
AIPROXY_API_ENDPOINT | Fill in according to the deployed proxy address | Resolves access restrictions for domestic large models, and supports access to domestic models such as Tongyi Qianwen |
AIPROXY_API_TOKEN | Fill in according to the key provided by the proxy service | Completes identity verification for model calls, and ensures valid interface call permissions |
Three Common Misconfigurations
- Symptom: The interface model dropdown list only displays overseas large models, and domestic models such as Tongyi Qianwen cannot be selected. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured, and domestic large model services are not accessed via the proxy. - Symptom: A compatibility error is returned when calling the Qwen model deployed via Xinference. Cause: The
toolChoiceparameter is not set correctly, or the function call format of the model is not adapted, causing the model to fail to recognize cross-review instructions. - Symptom: Uploaded warehousing document images cannot be parsed, and the logs show an abnormal
image_urlfield format. Cause: Images are not converted to the required base64 encoding format, or are not correctly passed to the model input parameters.
How to Verify a Successful Configuration
- Import a receiving slip and its corresponding lot ledger, and check whether the model can extract material codes, lot numbers, warehouse location numbers and complete cross-checks.
- Call the configured model interface, and check whether the returned results include preset verification fields with no abnormal errors.
- Switch between different types of warehousing documents (such as inventory count sheets, inventory transfer slips) to verify whether the model can adapt to the field structures of different documents.
- Check the system logs to confirm that the request parameters for model calls match the configuration items, with no missing parameters or format errors.
The values provided are common starting points and should be evaluated against the reader’s own samples.