What the documents look like
Store counts and loss control work around book-to-physical differences and shrinkage. The materials handled are count sheets, variance analyses, write-off approvals and monitoring access records. Supplier account settlement sits outside this scope. In cross-border operations, one set of count materials can exist in both a Chinese version and a foreign-language version, and the retrieval step has to connect quoted content back to a specific document version and a specific field. Count materials are updated monthly or quarterly, drawing on POS inventory, WMS, monitoring systems and count standards. Citation tracing answers one question: can the quoted content be traced back to a concrete document version and field.
This matters because the same variance figure can appear in several versions with different approval status. A retrieval hit is evidence that content matched a query. It is not evidence that a write-off was approved, that a store owns the record, or that the record falls inside the current period.
Constraints at the citation tracing step
The knowledge base search node can select one or more knowledge bases that share the same vector model, for vector search. Quoted content is output as an array, and its length can be zero, so the output path still runs when nothing is found. Semantic retrieval supports cross-language understanding, which helps when a Chinese question has to match a foreign-language count entry, but the retrieval result itself performs no authorization judgment. Minimum relevance takes effect only under semantic retrieval or result reranking, and it is a filter threshold; it is not an entity or permission filter. Hybrid retrieval merges multiple result paths through RRF, reranking depends on a model and on the setting being enabled, and image retrieval requires image input and model capability. These paths do not all execute on every request.
Hypothetical paper exercise sample: a store's March 2025 count sheet (Chinese version) states "closed-store count variance totals 12 SKUs, of which 3 write-off approvals passed." The foreign-language count sheet of the same date states the corresponding entries. A draft sentence reads "the store's March count variances have all completed write-off approval." The subject needed for judgment is the store, the object is the March count variance, the status is write-off approval completion, and the effective scope is the March 2025 count sheet. What remains to be checked is whether the draft sentence and the two source texts agree on the field "scope of completed write-off approval."
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| Linked knowledge bases | Select the Chinese count base and the foreign-language count base | First check the two vector model identifiers; they can be linked together only when identical |
| Search mode | Semantic retrieval | Used to match count entries across languages, compared against full-text retrieval results |
| Minimum relevance | Hypothetical starting point 0.5 | Takes effect only under semantic retrieval or reranking; compared against unfiltered results |
| Citation limit | Set in tokens, hypothetical starting point 2000 for this exercise | Controls quoted content length; compared against the number of returned citation entries |
| Citation output check | Accept the array output, length may be zero | Compared entry by entry against whether the downstream answer step receives citations |
| Material currency check | Use the March 2025 version and a historical count sheet version | Compared against the review conclusion for the currently effective version |
Three common errors
- Treating a retrieval hit as approval. Input a count variance record whose write-off approval status is pending review, compare the retrieved citation against the approval status field, and if the citation appears while the approval field reads pending review, approval completion cannot be concluded.
- Forcing knowledge bases with different vector models into a shared link. Input two count bases whose vector model identifiers differ, compare whether the identifiers match, and if they do not, exclude them; no further inference about missed recall or redundancy follows from that.
- Treating minimum relevance as a permission or entity filter. Input a count record from an unrelated store, compare its relevance score against the threshold, and even when the score exceeds the threshold, the record cannot thereby be treated as belonging to the current store or as authorized.
How to verify the setup
- Input the Chinese and foreign-language count sheets and the draft sentence from the hypothetical sample above, compare the draft sentence "write-off approval has all completed" against the source wording "3 write-off approvals passed, 12 variances in total" on the field of completed write-off approval scope. If the source states only 3 approvals passed, the draft sentence is unsupported.
- Input a count variance record whose write-off approval status is pending review, compare the citation content returned by retrieval against the approval status field. If the citation exists while the approval field reads pending review, record the item as pending verification; it cannot be recorded as approval completed.
- Input two count bases whose vector model identifiers differ, compare whether the identifiers are the same, and if they differ, record them as not linkable together and exclude them. Then input an explicit paper empty array [] and compare whether the downstream answer step accepts it as an empty citation; if the downstream step accepts it normally, that matches the documented behavior. Do not claim that retrieval under some invented rule necessarily returns an empty array.