What the documents look like
Third-party overseas warehouses serve multiple sales channels, and the seller allocates stock across those channels. This differs from a single-channel fulfillment model where allocation is handled by the platform. Procurement and supplier records for replenishment draw on overseas warehouse WMS data, ERP sales forecasts, freight forwarder systems and carrier bills. The working documents are replenishment plans, inbound appointment requests, inventory aging tables and last-mile delivery orders.
For the procurement and supplier task, the content that gets searched typically includes supplier names, purchase batch numbers, delivery terms, inbound appointment status and the aging fee basis. These fields update weekly or daily, and retrieval has to work from the version actually stored in the knowledge base.
This page covers the vector model and index step only. A retrieval result is not a procurement authorization, an inbound permission or a supplier qualification decision. Those determinations belong to separate business processes.
Constraints at the vector indexes step
Knowledge bases linked into one search must share the same vector model. The actual model and its input type are checked first, and a base built on a different vector model is not linked into the same search. One source data block can carry several indexes, and each index is checked against the source text separately. Index content is most useful when it is complete and focused; retrieval coverage and index length are weighed against sample documents.
Image retrieval depends on whether an image description or image vector index was created at ingestion time. Uploading an image on the query side does not by itself determine whether a match occurs. After a model or index change, the same question set is compared against the cited source text, and a vector score alone does not support a claim about outcome quality. RRF is the fusion step for mixed retrieval paths; it is not a switch that can be turned on. Result reranking requires a model and is enabled according to settings.
Hypothetical paper exercise: on 10 March 2025 an overseas warehouse operator assembles a supplier record. The source text reads: "Supplier A submitted an inbound appointment request on 20 February 2025; the appointment status is pending review; it covers batch B-031, quantity 1,200 units; the purchase contract signing date field is empty." The draft sentence under review is: "Supplier A's inbound appointment has been approved and can be received within March." That sentence does not match the source. The source shows pending review only.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Linked knowledge base | One knowledge base with a confirmed vector model | Check the model identifier first and exclude bases on a different vector model |
| Search mode | Semantic retrieval together with full-text retrieval | Supplier names and batch numbers need exact hits; delivery terms need semantic matching |
| Citation limit | A token amount that covers a full replenishment plan passage | Compare against the sample source length so status fields are not truncated |
| Minimum relevance | A value that recalls the pending-review sample | Compare against recall results for the same question set and adjust step by step |
| Question optimization | Enabled for short questions | Compare recalled source text with and without it, and observe whether the subject is completed |
| Result reranking | Enabled according to settings when a model is available | Compare candidate order before and after reranking against the cited source text |
Three common errors
- Reading a pending-review status as approved. The input is the sample source text above. Compare the field values "pending review" and "approved" and observe whether the draft sentence matches the source. A mismatch is the error.
- Linking knowledge bases built on different vector models into one search. The input is the model identifier of each knowledge base. Compare whether the identifiers are the same. When they differ, exclude that base and record the actual observation without inferring runtime behavior.
- Treating a similarity score as an authorization decision. The input is the retrieval score and the purchase contract signing field. Compare the score level with the signing status. When the signing field is empty, the contract is not treated as effective.
How to verify the setup
- Input the sample source text and the draft sentence. Compare "pending review" with "approved" and observe whether the retrieved citation returns the source status field. A returned source with status pending review shows the index retained that field. This is a paper check; it was not executed.
- Input the model identifiers of two knowledge bases. Compare whether they are identical and observe whether the linking step excludes the mismatched base. Different identifiers with no combined search performed shows the pre-check worked. This is a paper check; it was not executed.
- Input an empty-result scenario as a paper input of an empty array and compare whether the downstream design accepts a citation array of length zero. A completed downstream path with no fabricated business action shows the empty-result handling matches the documented behavior. This is a paper check; it was not executed.