What the documents look like
Store cold-chain receiving for fresh food operates on transport temperature control, a WMS, the store receiving system, and quality-inspection photographs. The working documents are cold-chain temperature records, arrival acceptance forms, rejection forms, and supplier delivery notes. Receiving staff measure temperature, sample quality, and register rejection reasons, with data updated per batch and per day.
The cross-border and multilingual retrieval task changes what citation tracing must handle. One arrival can produce a Chinese delivery note, a temperature record in another language, and an entry in the store receiving system at the same time. Citation tracing has to line up each material's original wording, its version, and the retrieval output. The update frequency is a business-side fact. Retrieval configuration does not synchronize automatically because records change daily, and it does not run verification automatically because a batch arrives.
Two features of this category shape the task directly. First, temperature measurement and quality sampling happen at receiving, while origin price inquiry belongs to fruit procurement and stays outside this scope. Second, the consequence of error is concrete: spoiled ingredients, supplier disputes, or regulatory penalty. A citation that cannot be traced back to the original temperature record or delivery note leaves the receiving decision without a defensible source.
Constraints at the citation tracing step
The knowledge base search node can select one or more knowledge bases that share the same vector model, and it outputs cited content as an array whose length may be zero. When nothing is retrieved, that output path still runs through. This means an empty citation array is a valid output, not by itself a workflow failure.
Citation tracing requires comparing each answer statement against the cited original text. Fluency of the answer and the model's thinking process are not substitutes for source evidence. Semantic retrieval supports cross-language understanding, so a Chinese question can match an English knowledge point, but a similarity score does not perform permission filtering or entity isolation. The same-vector-model requirement for linked knowledge bases also does not extend into a claim that an external business registry or the store receiving system must use the same model.
Full-text retrieval suits exact queries such as numbers, models, and proper nouns. Hybrid retrieval merges multiple result paths through RRF. Reranking depends on a model and runs when configured. Image retrieval requires image input and the corresponding model capability. The paths listed in the overall retrieval diagram do not all execute on every request.
Hypothetical paper exercise sample. A store receives one cold-chain arrival. The supplier delivery note states: "Shipment batch A-0715, product frozen beef, arrived quantity 40 boxes, transport temperature record attached." The cold-chain temperature record states: "15 July 2025, 08:20, compartment temperature -18.2°C, uninterrupted throughout." The store receiving system entry states: "Receiving note R-0715, actual received 40 boxes, status pending acceptance." A draft sentence reads: "This batch's transport temperature is qualified, acceptance passed, settlement may proceed." The pending request reads: "Please search the knowledge base to confirm whether this batch has completed acceptance and authorized settlement." The subject is the store receiving role, the object is batch A-0715, the status is pending acceptance, and the authorization boundary is that acceptance and settlement authorization are not yet complete.
Configuration and implementation checks
The table lists documented settings or implementation checks tied to this page's function and primary task. Where a number is needed, it is stated as a starting point for this hypothetical exercise, with the comparison object named.
| Configuration or check | Starting point for this hypothetical exercise | Comparison method and reason |
|---|---|---|
| Linked knowledge bases | Select Chinese and English cold-chain material bases that share one vector model | Compare the selected knowledge base identifiers and their vector model identifiers; knowledge bases with different vector models are not linked together, so exclude non-matching ones and record the observation |
| Search mode | Hybrid retrieval | The sample contains batch number A-0715 and a natural-language question; hybrid retrieval uses semantic recall and full-text recall together and merges them through RRF |
| Citation limit | Starting point for this hypothetical exercise: 3000 tokens | Compare against the combined length of the sample originals and observe whether the cited content covers the delivery note, temperature record, and receiving entry |
| Minimum relevance | Starting point for this hypothetical exercise: 0.35 | This value takes effect only under semantic retrieval or result reranking; compare against rerank scores and observe whether low-relevance fragments are filtered |
| Result reranking | Enabled as configured | When a rerank model is available it re-sorts candidate text and its score is used for filtering; compare against the candidate order without reranking |
| Citation display check | Manual line-by-line comparison of the answer against the cited originals | Use the sample originals to check whether the answer contains acceptance-passed or settlement-authorized wording; do not substitute the thinking process for source evidence |
Three common errors
- Treating retrieval output as authorization to act. In the sample the receiving status is pending acceptance. Retrieving the delivery note and temperature record shows the materials exist; it does not show that acceptance is complete or that settlement is authorized. The erroneous practice is to output an acceptance-passed conclusion directly from the draft sentence.
- Using a similarity score in place of permission or entity filtering. Minimum relevance is a filtering threshold, not a final ranking rule, and it does not exclude unrelated businesses or unauthorized records. The erroneous practice is to assume that raising relevance isolates other stores' or suppliers' data.
- Writing a pending-review status as missing operating permission or as an acceptance conflict. In the sample the receiving entry status is pending acceptance, and the temperature record and delivery note do not yet state an acceptance conclusion. This is unsupported or pending verification; it is not a contradiction and not a denial.
How to verify the setup
- Input the sample originals and the draft sentence "This batch's transport temperature is qualified, acceptance passed, settlement may proceed." Compare whether the acceptance-passed and settlement-authorized wording in the draft corresponds to any field in the delivery note, temperature record, or receiving entry. If the originals state only pending acceptance, that wording is unsupported; record the evidence gap and do not supply an authorization or effective clause.
- Input the pending request "Please search the knowledge base to confirm whether this batch has completed acceptance and authorized settlement." Compare whether the retrieved cited content contains an acceptance conclusion or a settlement authorization field. If the cited content contains only the batch, temperature, and pending-acceptance status, the observation is unsupported. If the output is an empty array, use an explicit paper input of [] to check the downstream design; do not claim that a fictional rule search necessarily returns [].
- Input the identifiers of the Chinese and English cold-chain material bases that share one vector model. Compare whether the vector model identifiers of the selected knowledge bases match. If the identifiers do not match, exclude the non-matching ones and record the actual observation. Do not run cross-model retrieval to compare missed recall, and do not infer a runtime result from linking them together.