What the documents look like
Receiving and putaway handles the first physical-and-record confirmation of goods entering a warehouse. The working materials are the purchase arrival note, the inbound acceptance note, the putaway task and the discrepancy report, with data drawn from WMS, ERP procurement, supplier delivery notes and barcode scanning. These materials are produced on a per-batch basis, and the most time-consuming steps are checking arrived quantities, printing barcodes and assigning storage locations.
For vector search, material quality decides whether retrieval can return the right entries. Incomplete fields, inconsistent quantity units and differing supplier name spellings all push semantic retrieval away from the target. In this task the vector model and index serve document lookup only. Search results supply candidate references; inventory confirmation and supplier dispute handling still require human work and established procedures.
Two category facts shape this page. First, receiving is the point where goods and records are reconciled, so a retrieved document may carry a quantity that differs from the physical count. Second, supplier identity is a business fact recorded in the source document, not a property the retrieval layer enforces. Both facts matter because a reference returned by search is evidence to compare, not a conclusion.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Check the actual model and the input type before associating. One original data block can be linked to several indexes, and the index content and the original text are checked separately. Index content should be complete and focused; retrieval coverage and index length are weighed together against samples. Image retrieval also depends on whether an image description or image vector was generated at the time of ingestion, so the query side alone is not sufficient. After a model or index change, compare cited original text using the same set of questions; do not promise an outcome from a vector score alone. Retrieval does not carry permission or entity isolation, and a similarity setting does not exclude unrelated business records or unauthorized records.
Hypothetical drill sample: the source text reads "Purchase arrival note PO-2024-0517 states that supplier A delivered 120 pieces of rebar HRB400 on 17 May 2024, diameter 12 mm. The receiving clerk generated inbound acceptance note YS-0517-02 in the WMS, recording an actual receipt of 118 pieces. Discrepancy report DR-0517-A records a shortage of 2 pieces, with status pending supplier confirmation." The draft sentence to be judged is "This batch of rebar has been put away in full at 120 pieces, and the supplier has no objection." This sample is used to check retrieval recall and the boundary of human judgment. It does not indicate that any real business action occurred.
Configuration and implementation checks
| Configuration item | Setting or check for this page | Note |
|---|---|---|
| Associated knowledge bases | Select one or more bases using the same vector model | Bases with different models cannot be associated together; check the identifier and exclude non-matching ones |
| Search mode | Semantic search, full-text search or hybrid search | Queries for numbers and models suit full-text or hybrid search |
| Minimum relevance | A 0-1 value, effective only in semantic search or with result reranking | This hypothetical trial starts at 0.3 because the sample question is short; compare against 0.5 to observe recall changes |
| Citation limit | Content ceiling measured in tokens | top k is not used because chunk lengths differ widely |
| Question optimization | Coreference resolution and question expansion before retrieval | Adds one model call; can be enabled for short questions |
| Result reranking | Second-pass ordering with a ReRank model | Rerank scores are 0-1 and can be used for filtering |
Three common errors
- Treating retrieval recall as authorization or inventory confirmation. With the draft sentence "This batch of rebar has been put away in full at 120 pieces" as input, retrieval may return inbound acceptance note YS-0517-02, but that note records 118 pieces actually received, and the discrepancy report status is pending supplier confirmation. Comparing the retrieved citation with the source text shows the quantity fields are mutually exclusive, so the draft sentence is not supported.
- Inferring entity isolation from a similarity score. Entering supplier A's name may return several citations; concluding from this that other suppliers' records were excluded goes beyond what the documents state. Minimum relevance is a filter threshold, not a permission filter. Compare the actual citation list against the target supplier identifier; entries lacking that identifier may still be recalled.
- Associating several knowledge bases without first confirming the vector model matches. Compare the model identifiers of the two bases as input; if the identifiers differ, they cannot be associated together. This action only checks identifiers and excludes non-matching ones. It does not run cross-model retrieval to compare missed recalls.
How to verify the setup
- Enter the hypothetical source text above together with the draft sentence, compare the quantity fields in the retrieved citations against the two values in the source, 118 pieces and 120 pieces, and observe whether the citations include the inbound acceptance note and the discrepancy report. The expectation is that the citations can locate these two materials, and the draft sentence is not supported because the quantities conflict.
- Enter supplier A's name combined with "rebar HRB400" and compare whether the returned citations contain the target supplier identifier. Observe the failure branch: if entries for other suppliers appear in the citations, record that observation. Do not conclude that permissions failed; record only that retrieval performed no entity isolation.
- Enter a query with no corresponding material, such as "return record for PO-2024-0517", and compare the output citation array. The expectation is an array of length 0, with the downstream chain still completing. This result only shows that no content was recalled for this paper input; it does not show that a fictional rule always returns an empty array.