What the documents look like
Inbound receiving and put-away is the first physical-and-book confirmation that goods have entered the warehouse. Its data comes from the WMS, ERP purchasing, supplier delivery notes and barcode scanning, and its day-to-day documents include purchase arrival notes, receiving inspection sheets, put-away tasks and discrepancy reports. In a cross-border setting the same arrival can carry a Chinese purchase order, an English packing list and a multilingual supplier delivery note, and receiving staff reconcile quantity, barcode and storage location across those materials.
Once these materials enter a knowledge base, retrieval quality depends on whether the vector model can match similar meaning across languages, and on whether the indexes cover exact fields such as numbers and model codes. A multilingual arrival batch makes this concrete: a semantic query in one language may need to reach a document written in another, while a purchase order number needs an exact match rather than a paraphrase. The two needs pull in different directions, so the search mode and the index content both matter at this step.
Constraints at the vector indexes step
When a search associates several knowledge bases, those knowledge bases must use the same vector model. Image retrieval additionally depends on whether an image description index or an image vector index was generated at ingestion time. One source data block can be linked to several indexes, and the index content is checked separately from the original text.
Semantic retrieval relies on the training quality of the model. Cross-language understanding is one of its stated advantages, and its precision is affected by keywords and by how complete the sentences are. Full-text retrieval suits exact lookups such as numbers, model codes and proper nouns. Hybrid retrieval combines semantic recall and full-text recall through RRF, and a ReRank model re-sorts candidate text to produce a score between 0 and 1. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used, and it is a filter threshold rather than a sort rule. The reference limit is controlled in tokens, not in item count. Retrieval itself is not authorization: forms collect values, while business verification, approval, database writes and external synchronization each need their own downstream implementation.
Hypothetical paper exercise sample: the purchase arrival note reads "PO-2024-8871, supplier A, arrival date 12 June 2024, quantity 480 pieces, storage location pending"; the receiving inspection sheet reads "inspected quantity 480 pieces, barcode scanning completed, discrepancy report not generated"; the put-away task reads "storage location to be assigned, status is pending review". A draft sentence to be assessed reads "this batch has completed put-away, storage location A-03-02". Neither the purchase arrival note nor the receiving inspection sheet records a location assignment result, and the put-away task status is pending review, so the location and the completion state claimed in the draft sentence are not supported by the available materials.
Configuration and implementation checks
| Configuration item | Assumed starting point for this exercise | Reason and comparison method |
|---|---|---|
| Vector model | Same model identifier as the already associated knowledge base | Associated retrieval requires the same vector model; check the actual model and input type first, and exclude knowledge bases with a different model |
| Index content | One focused index group each for the purchase arrival note and the receiving inspection sheet | Retrieval precision rises when index content is complete and focused; compare with the same question whether recalled original text covers the subject and the number |
| Image index | Generate an image description index for the packing list image at ingestion | Whether an image can be retrieved depends on whether the corresponding index was built at ingestion; compare with a pure image query whether it is matched |
| Search mode | Semantic retrieval and full-text retrieval used together, fused through RRF | Full-text retrieval is more accurate for number-type questions and semantic retrieval for natural-language questions; compare recall differences between the two modes with the same question |
| Minimum relevance | Set to 0.3 as the assumed starting point for this exercise | This value takes effect only in semantic retrieval or result reranking; compare the cited original text before and after filtering with the same question |
| Reference limit | Set to 2000 tokens as the assumed starting point for this exercise | The reference limit is controlled in tokens; compare the length and coverage of returned cited content with the same question |
Three common errors
- Associating knowledge bases that use different vector models. Input: two knowledge bases with different model identifiers. Compare their model identifiers. Observable result: the model identifiers differ, so exclude the non-matching one and record the actual observation; do not run a cross-model retrieval.
- Treating a retrieval result as an authorization credential. Input: a retrieved purchase arrival note. Compare whether it records an approval or release status. Observable result: the original text records no approval field, retrieval is not authorization, and business verification and approval need their own downstream implementation.
- Writing a pending-review status as completed put-away. Input: the put-away task text in the hypothetical sample. Compare its status field with the completion state claimed in the draft sentence. Observable result: the original status is pending review, and the location and completion state claimed in the draft sentence are not supported.
How to verify the setup
- Input the purchase arrival note text from the hypothetical sample. Compare whether the cited content returned by retrieval contains PO-2024-8871 and the quantity 480 pieces. Observable result: if the cited content covers that number and quantity, the index content is consistent with the original text.
- Input the put-away task text and the draft sentence from the hypothetical sample. Compare the location claimed in the draft sentence with the status field in the original text. Observable result: the original records no location and its status is pending review, so the draft sentence is not supported; record the evidence gap.
- Input a pure image query for the packing list image. Compare whether the returned result matches that image's description index. Observable result: a match indicates an image description index was generated at ingestion; with no match, record it as pending verification and do not infer that no index exists.