What the documents look like
Fresh-food weighing and sorting takes place inside the warehouse. The working documents are the sorting task sheet, the weighing label, the short-weight compensation form and the quality inspection record. Data comes from the WMS, electronic scales, the order system and inspection photos, and it updates per order and per day. For operations handover, this means the two sides compare one sorting task sheet over one time period, checking the weight and specification wording, and checking whether the compensation form and inspection record sit in the same authorization state. Knowledge base retrieval and recall takes these documents, retrieves them against a search term and produces cited content. Retrieval does not decide who may act, and it does not change the state of any document.
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; when identifiers differ, the correct step is to check the identifiers, exclude the mismatched base and record what was observed, without running a cross-model search to compare missed recalls. Cited content is output as an array and its length can be 0, which means the output path still runs even when nothing is found. The citation limit is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used, and it is a filter threshold rather than a final ordering rule. Question optimization performs coreference removal and question expansion and adds one extra model call. Hybrid retrieval merges semantic and full-text recall through RRF; RRF is a fusion step, not a switch that can be turned on. Image retrieval requires a corresponding image input and model capability, and reranking requires a rerank model enabled in settings; these paths do not run on every request.
Hypothetical paper exercise sample: the sorting task sheet of a front warehouse for the day shift of 12 June 2024 reads "Task sheet A-0612, specification 500g±20g, sorter A, status is awaiting review". The short-weight compensation form of the same day reads "Compensation form B-0612, related task sheet A-0612, application status is awaiting approval". The draft sentence to be judged is "The short-weight compensation for task sheet A-0612 has been approved". Comparing this sentence with the source text, the source states only that the application is awaiting approval and carries no approval field, so the sentence is unsupported and remains to be verified. It does not amount to a denial of operating permission.
Configuration and implementation checks
| Configuration or check item | Setting relevant to this page | Hypothetical starting point and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only knowledge bases that use the same vector model | Check the vector model identifier of each base first; exclude those whose identifiers differ; do not associate across models |
| Search mode | Semantic retrieval, full-text retrieval or hybrid retrieval | Compare whether the cited content for a task-sheet-number question contains that number under full-text and under hybrid retrieval |
| Citation limit | Measured in tokens | Start from a value that can hold the complete fields of one task sheet, and compare completeness against a citation containing only a number fragment |
| Minimum relevance | Takes effect only under semantic retrieval or result reranking | Label related and unrelated samples first, then compare how cited content changes when the threshold changes |
| Question optimization | Coreference removal and question expansion | Compare the original question with the optimized question and check whether the subject "task sheet A-0612" has been altered |
| Result reranking | Requires a rerank model enabled in settings | Check whether a rerank model is available, and compare the ordering of candidate text before and after enabling |
Three common errors
- Treating retrieval results as a basis for authorization. The input is the draft sentence and the compensation form text that is awaiting approval; the comparison is whether the source text carries an approval field. When the source does not carry it, the record is unsupported; a retrieval hit does not make the compensation approved.
- Associating knowledge bases across vector models. The input is the vector model identifier of several bases; the comparison is whether the identifiers match. Bases with different identifiers are excluded and recorded, and no inference is drawn about missed recalls or redundancy.
- Treating RRF or the minimum relevance value as a permission or entity filter. The input is the hybrid retrieval setting and the minimum relevance value; the comparison is the change in ordering and filtering of cited content. A similarity value does not exclude unrelated businesses or unauthorized records, and authorization requires a separate downstream implementation.
How to verify the setup
- Input the task sheet text and the draft sentence from the hypothetical sample, and compare whether the source text contains an approval field. When the result is "approval field not stated", the condition for preserving the evidence gap is met; the expected outcome is that the matter remains to be verified, and no claim is made that this was actually tested.
- Input the compensation form text and the handover request from the hypothetical sample, and compare whether the application status and the requested action share the same subject, the same period and the same field. When the status is awaiting approval, it is not treated as approved; the expected outcome is a recorded gap.
- Input the paper empty result array [] and compare whether the downstream design accepts cited content of length 0 and continues execution. When the downstream design accepts the empty array and proceeds, the condition is met; no claim is made that a fictional rule search always returns an empty array.