What data for this category looks like
Data for this category primarily comes from ERP/WMS systems, barcode or RFID collection devices, procurement and work order documents, bin management ledgers, AGV conveyance systems, and weighing equipment. Daily documents include receiving slips, putaway slips, material picking and return slips, inventory transfer slips, inventory count sheets, lot ledgers, first-in-first-out (FIFO) and expiration date records. Fields include material code, lot number, bin number, expiration date, inventory quantity, inspection status, and others. Update frequencies are divided into three categories: real-time (e.g., bin changes, AGV scheduling), daily (e.g., inventory count snapshots), and monthly (e.g., full warehouse inventory data).
What constraints do these characteristics impose on the "model integration and configuration" phase
The multi-source heterogeneous nature of this category's data requires the model integration link to adapt to data sources with different update frequencies. This prevents synchronization delays from causing Q&A results to not match actual inventory. Uniqueness requirements for core fields such as lot number, expiration date, and inspection status require clarifying constraints on field matching accuracy for the model during configuration. The business goals of inventory accuracy and lot traceability require strengthening multi-document associative retrieval capabilities in configuration. It also requires limiting the output scope of single Q&A to prevent results inconsistent with warehousing processes. Additionally, errors directly affect production rhythm, so fault tolerance and verification links must be added in configuration to avoid abnormal logistics actions triggered by incorrect instructions.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category requires associating multiple documents such as lot ledgers, bin records, and work order data. Sufficient context ensures the model fully understands material flow logic |
maxTokens | 1500–2000 token | Warehousing Q&A needs to output multi-dimensional content such as material location, lot information, and expiration date. This range covers the output requirements of conventional Q&A |
quoteMaxToken | 3000–4000 token | Multiple documents related to bins and lots need to be recalled. Limiting the total token count of referenced content prevents model overload |
temperature | 0.1–0.3 | Warehousing processes must strictly follow standards. A lower temperature value ensures consistency and accuracy of Q&A results |
AIPROXY_API_ENDPOINT | http://192.168.1.100:3000/v1 (adjust per actual deployment address) | Adapt to intranet deployment of ERP/WMS data synchronization, avoiding delays and security risks from public network access |
AIPROXY_API_TOKEN | Consistent with the secret key configured for the proxy service | Ensure secure identity verification for model integration and prevent unauthorized access |
Three common configuration errors
- Phenomenon: A FATAL-level error occurs after starting the proxy service, and FastGPT cannot call the model. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, or the proxy service cannot connect to intranet data sources. - Phenomenon: After uploading a query containing material barcodes or bin photos, the model cannot parse the image content. Cause: The
visionconfiguration item is not enabled, or preprocessing for image recognition is not optimized for barcodes and shelf labels in warehousing scenarios. - Phenomenon: FastGPT still returns partial Q&A results after incorrectly configuring the port number of
AIPROXY_API_ENDPOINT. Cause: The proxy service has a default fallback mechanism and does not strictly verify the legitimacy of the request address, allowing partial public interface access even with incorrect configuration.
How to confirm successful configuration
- Use the model connectivity testing tool built into FastGPT v4.17.0, enter the address corresponding to
AIPROXY_API_ENDPOINT, and check that a normal model response is returned with no timeout or error logs. - Import a test document containing a lot ledger and bin records, initiate a query for "current bin location of specified lot material", and verify that the returned result matches the records in the document.
- After enabling the
visionconfiguration item, upload an image containing a material barcode and expiration date label, and check that the model can accurately identify the barcode number and expiration date. - After configuring the API channel for the workflow, initiate a request through the specified API interface, and check that input interaction is triggered and corresponding prompts are returned.
The values provided are common starting points and should be measured against the reader's own samples.