What the documents look like
Cold-chain receiving records are produced batch by batch. A refrigerated vehicle temperature record, an arrival inspection sheet, a rejection note and a supplier delivery note together describe one arrival: the temperature range in transit, the sampled quality result and the reason recorded for rejection. These materials update per batch and per day, so retrieval faces multiple batches from the same store and the same supplier at once. Temperature values, batch numbers and rejection reasons behave as exact fields, while quality descriptions read as natural language. For order fulfilment and after-sales questions, retrieval has to keep precise identifiers such as batch number and supplier name separate from semantic content such as temperature wording and quality wording.
The practical consequence is that a question about a rejection can return content from a different batch or a different supplier when the query is phrased loosely. Retrieval returns reference content; it does not decide who is responsible, whether a rejection was justified or whether a supplier owes a replacement.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases built on different vector models cannot be associated together. Before configuration, check the vector model identifier of each library, exclude those that do not match, and record the association result actually observed.
Retrieval returns reference content as an array, and the length may be zero. When nothing is found, the output path still runs. The minimum relevance value is a filter threshold and takes effect only in semantic retrieval or when result reranking is used. It does not perform permission filtering and does not separate suppliers or stores. Full-text retrieval suits numbers, models and proper nouns; semantic retrieval suits natural-language questions; hybrid retrieval merges the two paths through RRF; result reranking reorders candidate text a second time. Image retrieval requires an image in the input and a model capable of handling it, and reranking requires an available model enabled by configuration. These optional paths do not all execute on every request.
Hypothetical paper exercise sample: the material text to be checked reads, "On 12 March 2025, supplier A delivered arrival batch A. The refrigerated vehicle temperature record shows a temperature of 2 to 4 degrees Celsius throughout transit. The arrival inspection sheet records three boxes sampled, of which one box of leafy greens showed wilted leaves, and the store registered the rejection reason as failed appearance." The draft sentence to be assessed reads, "Batch A was rejected because the temperature exceeded the limit, and supplier A bears full responsibility for return and replacement." The draft differs from the source on the rejection reason field: the source registers failed appearance, and the temperature record sits at 2 to 4 degrees Celsius.
Configuration and implementation checks
Configuration below concerns this sample and lists only documented settings or implementation checks relevant to this feature and the order fulfilment and after-sales task.
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate one confirmed cold-chain receiving library using the same vector model | Check the vector model identifier first, exclude non-matching libraries, then compare whether returned reference content comes from the target library |
| Search mode | Hybrid retrieval | Batch numbers and rejection reasons need full-text hits; quality descriptions need semantic hits; compare merged reference content from both paths |
| Reference limit | Hypothetical trial starting point set to cover the token volume of one complete inspection sheet | This value is a trial starting point only; compare whether reference content is truncated using inspection sheets of different lengths, then adjust |
| Minimum relevance | Hypothetical trial starting point set to a middle threshold | Label relevant and irrelevant samples first, compare whether irrelevant fragments decrease after filtering, then adjust the threshold |
| Question optimization | Enabled for short questions in multi-turn follow-ups | Compare the original question with the optimized question, and check whether batch number and supplier subject were altered |
| Result reranking | Enabled according to the actual configuration | Compare candidate ordering before and after reranking, and check whether the rejection reason field moves to the front |
Three common errors
- Treating the minimum relevance value of semantic retrieval as a supplier or store permission filter. Input a query for "batch A rejection reason", compare whether returned references include batches from other suppliers, and observe whether cross-supplier content appears. Relevance filters by similarity only and does not distinguish business subjects.
- Treating "temperature exceeded the limit" in the draft sentence as a source fact. Input the material text above together with the draft sentence, compare the rejection reason field values, note that the source says failed appearance while the draft says temperature exceeded the limit, and observe whether the two conflict. A temperature-exceedance finding that the source does not state remains unsupported.
- Associating knowledge bases built on different vector models directly. Input two libraries with different vector model identifiers, compare whether the identifiers match, exclude non-matching ones and record the observed result. The runtime result of associating different models together has no documentary basis and is not inferred.
How to verify the setup
- Input the material text above together with the draft sentence, compare the rejection reason field values, and if the draft value differs from the source, judge the draft sentence unsupported and correct it to failed appearance.
- Input a query containing a batch number, compare whether the reference content hits that batch's inspection sheet, and if the reference content is empty or points to another batch, record it as a retrieval miss and check whether the data finished training and how the reference limit is set.
- Input a short question in a multi-turn follow-up, compare whether the subject and batch number in the optimized question match the original question, and if the subject was altered, record a question-optimization deviation and adjust the scope where it is enabled.
A pending review state does not demonstrate the absence of operating permission; signing, reaching cooperation and contract effectiveness are three distinct facts. Retrieval and form collection do not constitute authorized execution; business verification, approval, database writes and external synchronization each need their own downstream implementation. Missing evidence remains a gap, and retrieval results are not substituted for authorization judgments.