What the documents look like
Convenience-store fresh food is ordered at short shelf-life and high frequency. A store produces order sheets, damage reports, near-expiry handling sheets and temperature records every day or every shift. These materials exist in Chinese, in a foreign language, or in bilingual versions. An order sheet carries item names and quantities. A damage report carries a damage reason and a quantity. A temperature record carries a time band and a reading. Cross-border material and multilingual retrieval must let one query reach the same item or the same document type across languages. The retrieval scope has to cover the current day and recent documents.
The practical consequence for this task is that a single query may need to reach two language versions of the same record. A store manager, a fresh-food buyer and a regional supervisor all work from these documents, and the cost of a miss is a missed expiry, a stockout or a food-safety complaint. Retrieval quality therefore depends on how the material is indexed and how the query is formed, not on any business authority the search step might appear to carry.
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 on different vector models cannot be associated together. The vector model identifier is checked first, non-matching bases are excluded, and the observation is recorded. A cross-model retrieval is not run to compare missed recall.
Reference content is output as an array, and its length can be 0. When nothing is found, the output path still completes. Search mode is compared across semantic, full-text and hybrid retrieval for the test question; no single mode is assumed to suit every document. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used. Question optimization performs coreference resolution and question expansion; the original question and the optimized question are compared to check whether the subject was altered. The reference upper limit is measured in tokens, and documents of different lengths are used to test whether content stays complete. RRF is a fusion process for mixed results, not an on switch. Image retrieval requires a corresponding image input and model capability. Reranking requires a model and is enabled by its setting. Retrieval and relevance scores do not perform permission or entity isolation.
Hypothetical paper exercise: a convenience-store regional supervisor receives a foreign-language temperature record. The original text reads "2025-03-10 morning shift, refrigerated cabinet A temperature 7.2°C, lasting 40 minutes." A Chinese damage-report draft sentence reads: "Because of the abnormal temperature, the entire batch of rice balls is to be written off as damage." Pending request: associate this draft sentence with the temperature record in retrieval and confirm whether the damage write-off can be executed directly. The temperature record states only the reading and the time band. It does not state the specific quantity of that rice-ball batch or the person authorized to approve the write-off. Executing the write-off requires a separate approval system.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the Chinese and foreign-language fresh-food bases that use the same vector model | Check the vector model identifier, exclude bases on other models, and do not run cross-model retrieval |
| Search mode | Test hybrid retrieval first | Compare the recalled content for the same test question against semantic retrieval and full-text retrieval separately |
| Reference upper limit | Set 2000 tokens as the exercise starting point | Compare against 1000 and 4000 tokens and observe whether short sheets and long temperature records stay complete |
| Minimum relevance | Set 0.5 as the exercise starting point | Takes effect only under semantic retrieval or result reranking; compare filtered content against 0.3 and 0.7 |
| Question optimization | Enable for multi-turn follow-up questions | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enable when a ReRank model is available | Compare candidate ordering before and after reranking and observe whether the score is more precise |
Three common errors
- Associating knowledge bases on different vector models together and then inferring missed recall or redundant returns. The correct action is to check the vector model identifier, exclude non-matching bases, record the observation, and not fabricate a run result.
- Treating the minimum relevance value as a permission or entity filter, so that a low score is read as excluding an unrelated business or an unauthorized record. That value is only a filtering threshold; retrieval does not carry out authorization decisions.
- Treating the pending damage-report draft sentence as a completed write-off, or treating the reading in the temperature record as the write-off authorization. A pending review does not show that operating permission is absent, and executing the write-off requires explicit authorization and a downstream system implementation.
How to verify the setup
- Input the hypothetical temperature record text and the damage-report draft sentence. Compare whether the reference content returned by retrieval contains that temperature record, and whether it returns only that record without mixing in other batches. If the returned content contains the record and does not mix in unrelated batches, the check condition is met. If the return is an empty array, record the gap and do not claim that a rule-based retrieval necessarily returns empty.
- Input the same test question and run semantic retrieval, full-text retrieval and hybrid retrieval separately. Compare the recalled content of the three paths and the ordering after RRF fusion. If the hybrid retrieval result contains both semantically close content and keyword-hit content, the check condition is met. If one path returns nothing, record that path as empty and do not infer that the other paths necessarily missed.
- Input the original question and the question after optimization. Compare whether the optimized question keeps the original subject and object. If the optimized question does not change "refrigerated cabinet A" to another cabinet number and does not change "rice balls" to another category, the check condition is met. If the subject was altered, record that optimization result and do not conclude from it that question optimization necessarily fails.
This page covers only convenience fresh-food ordering, cross-border material and multilingual retrieval, and knowledge base retrieval and recall. Whether a material, an operation or a request is authorized at handover cannot be decided by retrieval or by a form.