What the documents look like
Multi-warehouse allocation and inventory synchronization sit at the order-routing stage and determine which warehouse ships an order. The working data comes from WMS, OMS, ERP inventory, and platform inventory interfaces. The documents handled day to day include allocation rules, inventory synchronization tables, transfer orders, and stockout warnings, with daily or hourly update frequency.
In cross-border documentation and multilingual retrieval, one allocation rule may exist in Chinese and in a target-market language at the same time. Retrieval must reach the same subject across languages. A retrieval result supplies cited content only. It does not authorize a shipping-warehouse priority to take effect, make inventory sellable, or confirm that a transfer has been executed.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases built on different vector models cannot be associated together. The identifier should be checked first and non-matching bases excluded. Semantic retrieval supports cross-language understanding, so a Chinese question can match an English knowledge point. Hybrid retrieval merges semantic and full-text results through RRF, which is a fusion process, not a standalone switch. The minimum relevance score takes effect only under semantic retrieval or when result reranking is used; it is a filter threshold and does not perform permission or entity isolation. The citation limit is measured in tokens, and allocation rules and transfer orders with very different lengths need separate observation for content completeness. Question optimization performs coreference resolution and question expansion and adds one model call.
Hypothetical paper exercise sample. One cross-border seller's paper material states: "Allocation rule A-03: the East China warehouse takes priority for orders with East China receiving addresses, and the South China warehouse takes orders with South China receiving addresses, effective from March 1, 2025." A separate draft transfer order states: "Transfer order T-118: from the East China warehouse to the South China warehouse, quantity 200 pieces, status pending review." The draft sentence to be assessed is: "Transfer order T-118 has been approved, and South China sellable inventory increases by 200 pieces." The subject, object, and status in this sentence must be checked against the source text. The transfer order is pending review, which does not show approval. An increase in sellable inventory requires a write implementation in WMS or OMS, and a retrieval citation does not perform that action.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| Associated knowledge bases | Associate only the allocation rule base that uses the same vector model | Check the vector model identifier first and exclude bases with a different model |
| Search mode | Run semantic retrieval and full-text retrieval separately | Compare hit differences between a natural-language allocation rule question and a transfer order number question |
| Citation limit | Measured in tokens, tentatively set to cover one complete allocation rule | Compare against the length of the draft transfer order and observe whether the rule is truncated |
| Minimum relevance score | Label relevant and irrelevant samples first, then choose a value | This value takes effect only under semantic retrieval or result reranking |
| Question optimization | Enable for multi-turn follow-up samples, disable for single-turn explicit questions | Compare whether the subject is altered between the original question and the optimized question |
| Result reranking | Enable the rerank model when candidates are numerous | The reranked score is 0-1 and can be used for filtering |
Three common errors
- Treating pending review as approved. The input is the draft transfer order with "status pending review," the comparison object is the draft sentence "has been approved," and the observable result is that the source text carries no approval field, so the claim is unsupported.
- Treating a retrieval citation as an inventory write. The input is the cited content array returned by retrieval, the comparison object is "South China sellable inventory increases by 200 pieces," and the observable result is that the cited content contains no WMS write action, so a separate system implementation is required.
- Associating knowledge bases built on different vector models. The input is the vector model identifiers of the two bases, the comparison object is the node requirement for the same vector model, and the observable result is that the identifiers differ, so the non-matching base is excluded and the observation is recorded.
How to verify the setup
- Input the hypothetical sample source text "Allocation rule A-03: the East China warehouse takes priority for orders with East China receiving addresses, and the South China warehouse takes orders with South China receiving addresses, effective from March 1, 2025." Compare whether the draft sentence shares the same subject, the same period, and the same field as the source. The observable result is that the source text contains no approval wording, so the judgment is pending verification.
- Input the draft transfer order "status pending review." Compare it against the draft sentence "has been approved." The observable result is that the status field reads pending review, so the judgment is unsupported.
- Input an explicit paper empty result
[]. Compare how the downstream node is designed to accept an empty citation. The observable result is that the output path runs through and the citation length is 0, so the judgment is that an empty result can enter downstream. No claim is made that a search for a fictional rule necessarily returns empty.