What the documents look like
In warehouse picking and packing, the work handles outbound orders rather than supplier arrivals and putaway. The main data sources are the WMS, the order system, the electronic waybill platform and weighing equipment. Day-to-day documents include wave picking lists, review records, waybills and packaging consumable sheets. In the store-service and internal-training setting, retrieval targets the completion rules for these documents, the required review actions and the instructions for choosing consumables. Data updates per order and per hour; this page does not derive batch synchronization or real-time triggers from that frequency. Retrieval results answer operational questions. Retrieval itself does not constitute authorization to execute.
A hypothetical drill sample is used throughout this page. Training material states: “After the wave picking list is printed, the picker picks in storage-location order; the reviewer scans the waybill barcode to check item quantity; the packaging supervisor confirms the consumable specification.” A draft sentence under review reads: “After picking is complete, the picker performs the review and seals the carton directly for dispatch.” The draft differs from the source on the reviewing party and on the sealing step; it is unsupported.
Constraints at the retrieval step
The knowledge base search node can associate one or more libraries that use the same vector model. Libraries with different vector models cannot be associated together; verify the identifiers first and exclude those that do not match, without running cross-model retrieval to compare missed recalls. Quoted content is output as an array, and its length can be 0, so the output path still completes when nothing is found.
Semantic retrieval and the minimum relevance score do not perform permission or entity isolation. Full-text retrieval suits precise queries such as numbers, models and proper nouns; semantic retrieval suits natural-language phrasing. Reranking and image retrieval are optional paths that require model capability and must be enabled by configuration; they do not run on every request. RRF is a fusion process for mixed results, not an on/off switch.
The minimum relevance score is a filter threshold, not a final ordering rule. After question optimization, hybrid retrieval, image retrieval or reranking, final results may come from multi-path fusion and need not follow a single vector similarity order. Image retrieval depends on whether a vision model is configured or the vector model supports image input, and on whether an image index was built at ingestion time.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this trial | How to check |
|---|---|---|
| Associated knowledge base | Associate only the warehouse operations standard library | Compare the vector model identifier of each library for consistency |
| Search mode | Run semantic retrieval and full-text retrieval once each | Compare the two result sets against the test question |
| Quote limit | Set to 2000 tokens as a hypothetical starting point | Test content completeness with long and short material, then adjust |
| Minimum relevance | Set to 0.3 as a hypothetical starting point | Label related and unrelated samples first, then tune the threshold |
| Question optimization | Enabled for multi-turn follow-up scenarios | Compare the original and optimized question to check whether the subject was altered |
| Result reranking | Enabled when candidates are numerous | Check the relation between the rerank score and question completeness |
These values are hypothetical starting points for this trial, used for comparison against other values. They are not official defaults.
Three common errors
- Treating a retrieval hit as an authorization basis. Input: the draft sentence “the picker performs the review and seals the carton directly for dispatch.” Comparison object: the reviewing party in the training material source text. If new staff are trained directly on this basis, the retrieval result is being used as an execution basis. Record that the draft is inconsistent with the source and keep the evidence gap open.
- Associating libraries with different vector models. Input: two libraries whose vector model identifiers differ. Comparison object: the two identifiers. Attempting joint association is a configuration error. Verify the identifiers first and exclude those that do not match.
- Treating an empty quote result as a negative conclusion. Input: one retrieval that returns an empty array. Comparison object: whether the material was actually ingested. Concluding that “this rule does not exist” is overreach. Record it as unsupported and check whether data training has completed.
How to verify the setup
- Input the hypothetical source text and the draft sentence above. Compare the reviewing party and the sealing step. The condition is met if retrieval returns the source passage and the draft sentence is marked unsupported. If an empty array is returned, record the gap and do not conclude that the source text does not exist.
- Input two libraries whose vector model identifiers differ. Compare the identifiers. The condition is met if the system indicates they cannot be associated together, or if only qualifying libraries are returned. Do not invent an observed result of joint association.
- Input a number-type question. Compare the full-text retrieval result with the semantic retrieval result. The condition is met if full-text retrieval matches the number while semantic retrieval does not. Do not guarantee a first-position hit or complete recall.
For the empty-result branch, use an explicit paper input of an empty array to check the downstream design. Do not claim that a fictional rule necessarily returns an empty array. Across different vector models, libraries cannot be associated together; verify identifiers, exclude non-matching ones and record actual observations only.