What the documents look like
Receiving and put-away is the first physical-and-record reconciliation step when goods enter a warehouse. The working records come from WMS, ERP purchasing, supplier delivery notes, and barcode scanning. Day-to-day documents include purchase arrival notes, inbound acceptance sheets, put-away tasks, and discrepancy reports. Updates occur per batch and daily, so retrieval must line up with the arrival record of the current batch.
Cross-border material and multilingual retrieval means one arrival may carry a Chinese purchase order, a foreign-language delivery note, and acceptance notes in more than one language. The retrieval query and the material language can differ, which affects recall. This page addresses only the receiving-and-put-away category, the cross-border and multilingual retrieval task, and the knowledge-base search and recall feature. Retrieval and similarity scores do not perform permission or entity isolation. A retrieval result is not authorization, and a form that collects values does not by itself perform business verification, approval, database writes, or external synchronization. Those steps need their stated downstream implementation.
Constraints at the retrieval step
The knowledge-base search node can be linked to one or more knowledge bases that use the same vector model. Knowledge bases using different vector models cannot be linked together. The practical step is to check the vector model identifier, exclude the ones that do not match, and record what was observed. Do not run a cross-model search to compare missed recall.
Search mode is compared per test question across semantic, full-text, and hybrid retrieval. No single mode is assumed to suit every kind of material. Semantic retrieval depends on vector distance and supports cross-language matching, while full-text retrieval depends on keyword hits and suits part numbers, model numbers, proper nouns, and error codes. Hybrid retrieval uses both and merges the two result sets through RRF. RRF is a fusion step in the retrieval pipeline, not a switch that is turned on.
The reference cap is measured in tokens. The minimum relevance value is a 0-1 number that applies only in semantic retrieval or when result re-ranking is used. It is a filter threshold, not a final ordering rule. Question optimization performs coreference resolution and question expansion before retrieval; the original question and the optimized question are compared to check whether the subject was changed incorrectly. Re-ranking requires a re-rank model and is enabled according to configuration, so it does not run on every request. Image retrieval requires image input and matching model capability. The retrieval output is an array and its length can be zero, which means the downstream path still runs when nothing is found.
Assumed paper exercise sample. The source material to be checked reads: "Purchase arrival note PO-2024-0417: supplier is a trading company in Ningbo, arriving goods are folding tables, quantity 120 units, arrival date 17 April 2024." "Inbound acceptance sheet RV-0417-02: quantity actually received 118 units, discrepancy 2 units, discrepancy reason is outer packaging damage." The draft sentence to be judged reads: "This batch has been fully accepted as qualified and put away." This sentence conflicts with the acceptance sheet figures of 118 units received and 2 units discrepancy, and the material does not state that put-away was completed.
Configuration and implementation checks
| Configuration item | Assumed starting point for this exercise | Reason and comparison object |
|---|---|---|
| Linked knowledge bases | Chinese purchase-order base and foreign-language delivery-note base using the same vector model | Check the vector model identifier first and exclude bases that do not match |
| Search mode | Hybrid retrieval | Compare against semantic and full-text retrieval on part-number questions |
| Reference cap | Assumed starting point 2000 tokens for this exercise | Compare against 1000 and 4000 for content completeness |
| Minimum relevance | Assumed starting point 0.3 for this exercise | Label relevant and irrelevant samples before adjusting the threshold |
| Question optimization | Enabled | Compare the original question against the optimized question for subject changes |
| Result re-ranking | Enabled when a model is available | Enabled according to configuration, not on every request |
These values are assumed starting points for a paper exercise, not official defaults. The reference cap uses the documented token unit. The minimum relevance value keeps its documented condition of applying only in semantic retrieval or with result re-ranking.
Three common errors
- Treating retrieval as authorization. Retrieving a purchase arrival note does not mean the batch was accepted or put away. Business confirmation needs a separate system implementation, and a pending request is not an approval.
- Linking knowledge bases across vector models. Knowledge bases with different vector models cannot be linked together. The check is to verify the identifier, exclude the non-matching base, and record the observation, without inventing a runtime result such as missed recall or redundant returns.
- Writing a pending state as completed. The draft sentence states full acceptance and completed put-away. The acceptance sheet records 118 units received and 2 units discrepancy. The draft claim is unsupported by the material, and an unsigned termination is not a completed termination.
How to verify the setup
- Enter the acceptance sheet text above and the draft sentence. Compare the quantity fields 118 and 120 and observe whether the draft sentence is judged inconsistent with the source text. The condition is met when the comparison identifies the mismatch between the draft claim and the recorded figures.
- Enter the purchase arrival note text and check the four fields of supplier, goods, quantity, and date. Observe whether the retrieval result contains this batch record. If no result appears, record the gap and do not claim the result must be empty. The condition is met when the returned array either contains the batch record or the absence is recorded as an open gap.
- Enter a Chinese retrieval query against the foreign-language delivery-note base and compare semantic retrieval against full-text retrieval hits. Observe whether cross-language recall appears. A failed branch is recorded as a miss, not as proof that no record exists. The condition is met when the hit difference between the two modes is observed and the failure branch is recorded as a miss.
An empty-result path can be checked with a clearly labelled paper input of an empty array, confirming that the downstream design still runs. This does not claim that a fictional rule always returns an empty array.