What the documents look like
Convenience-store fresh food is ordered by the store at high frequency against short shelf life, and it is not handled through a joint-operation revenue split. Order forms, damage reports, near-expiry handling sheets and temperature records are therefore produced per store and per shift. The people who touch these materials are store managers, fresh-food buyers and regional supervisors. The update rhythm given for this category is daily and per shift, so a multilingual retrieval task has to reconcile the same set of materials across language versions while keeping the citation back to the source text.
The main data sources named for this category are POS sales, the headquarters ordering system, store inventory and cold-chain temperature records. In retrieval these appear as documents or data chunks. The knowledge-base search node outputs cited content as an array, and the length may be zero; a zero-length array means nothing was retrieved, and the output path still completes. The citation cap is expressed in tokens, and the minimum relevance value takes effect only under semantic retrieval or result re-ranking. Retrieval does not perform authorization: finding an order form in the index does not mean the current party is permitted to place an order, register damage or handle near-expiry stock.
Constraints at the citation tracing step
Multilingual retrieval can match a Chinese question to an English knowledge point through semantic retrieval, but the citation still has to be checked against the original text. The search node can associate one or more knowledge bases that use the same vector model; knowledge bases with different vector models cannot be associated together, so the vector model identifier is checked first and non-matching bases are excluded. The search result is an array and may be empty, and an empty array is not a workflow failure. The minimum relevance value is a filter threshold and not a final ordering rule, and it applies only under semantic retrieval or result re-ranking. The citation cap is a token count, not a top-k item count. RRF is the merge step for combining multiple retrieval paths, and it is not a switch that is turned on. Re-ranking requires a re-rank model and an enabled setting, and image retrieval requires image input plus the relevant model capability; the optional paths in the overall flow are not all executed on every request.
A hypothetical paper exercise. Source material for checking: a store's fresh-food order form dated 10 March 2026 states "rice ball order quantity 120 pieces, delivery date 11 March 2026"; the temperature record for the same day states "refrigerated cabinet temperature 4 degrees Celsius, recorded at 08:00". A draft sentence to be assessed: "The store completed receipt of the rice balls on 11 March 2026 and confirmed the temperature was acceptable." The draft sentence asserts two facts, completed receipt and acceptable temperature. The materials contain an order quantity and a temperature reading. They contain no receipt confirmation record and no acceptability determination record.
Configuration and implementation checks
The table below uses the sample above and lists only settings or implementation checks that relate to this feature and this primary task. The starting values are hypothetical starting points for this paper check and are not official defaults.
| Configuration item | Hypothetical starting point and reason | How to check |
|---|---|---|
| Associated knowledge bases | Select the knowledge base holding the store's order form and temperature record, to check the citation source | Verify the selected knowledge bases and material versions, and confirm the vector model identifier is the same |
| Search mode | Use hybrid retrieval, covering both semantic and identifier-style queries | Observe whether the cited content contains the data chunk matching the sample source text |
| Citation cap | Hypothetical starting point of 800 tokens, because the sample material is short and citation coverage of the source is easy to observe | Compare against the length of the sample source text and confirm the citation contains subject, object and date |
| Minimum relevance | Hypothetical starting point of 0.5, to observe whether low-relevance fragments are filtered | Compare this value only under semantic retrieval or result re-ranking, and record the cited content after filtering |
| Question optimization | Not enabled for this paper check, because the sample question has a clear subject | If enabled, compare the completed question with the original and observe the change in retrieval terms |
| Result re-ranking | Not enabled for this paper check, because the candidate count is small | If enabled, compare the re-rank score with the order of the cited content |
Three common errors
- Treating a retrieved order form as receipt authorization. The input is the sample order form text; the comparison target is the fact "receipt completed"; the observable result is that the materials contain no receipt confirmation record, so the draft sentence is unsupported. A retrieved order form only shows that the document is inside the retrieval scope; it does not show that a receipt action was performed.
- Treating the temperature reading as an acceptability determination. The input is the sample temperature record "refrigerated cabinet temperature 4 degrees Celsius"; the comparison target is whether an acceptability conclusion exists in the materials; the observable result is that the material records only a value and no determination, so "confirmed the temperature was acceptable" in the draft sentence is unsupported.
- Associating knowledge bases with different vector models and then comparing missed recall. The input is two knowledge bases with different vector model identifiers; the comparison target is whether the vector model identifiers match; the observable result is that they do not match, so the non-matching base is excluded and the actual observation is recorded, without running a cross-model retrieval to compare missed recall.
How to verify the setup
- Citation source check. The input is the sample order form text and the draft sentence; the comparison is whether "receipt completed" in the draft sentence shares the same subject, the same period and the same field as the source; the observable result is that the source contains only order quantity and delivery date and no receipt confirmation, so the claim is unsupported. If the draft sentence is changed to "The store ordered 120 rice balls on 10 March 2026, delivery date 11 March 2026", it matches the source.
- Empty-result handling check. The input is an explicit paper citation array []; the comparison is whether the downstream design treats the empty array as a normal output that continues along the path; the observable result is that if the downstream accepts the empty array and records the gap, this matches the documented behaviour that the output path still completes. No claim is made that a fictional rule retrieval necessarily returns [].
- Material timeliness check. The input is the current version and a historical version of the order form; the comparison is whether the date cited in the draft sentence corresponds to the version; the observable result is that if the draft sentence cites a value from the historical version while the current version has changed, the version difference is recorded, and whether the citation is adopted still depends on review against the effective version. Retrieval and forms do not constitute authorized execution; signing, reaching cooperation and contract effectiveness are three distinct facts, and when the materials do not state them the evidence gap is preserved.