What the documents look like
Warehouse picking, checking, and packing handles outbound orders rather than supplier arrivals. The document set for this category consists of wave picking lists, checking records, shipping labels, and packaging material sheets. These come from the WMS, the order system, the electronic shipping label platform, and weighing equipment, and they update per order or per hour.
In a cross-border setting, one batch of materials may contain both a Chinese-language wave picking list and an English-language shipping label. Picking staff, checking staff, and packing supervisors need to locate the matching passage for the same order across languages. Several structural features shape how parsing and chunking must work. A wave picking list often compresses order numbers, storage locations, SKUs, and quantities into table rows, so a condition statement and the line items it governs can be split apart during segmentation. Checking records and shipping labels share an order number while field names may differ by language. Packaging material sheets carry specifications and quantities, and queries about these identifiers depend on exact matches. Chunking therefore needs to preserve the correspondence between a field and its governing condition, and retrieval needs to cover terminology, identifiers, and natural-language questions.
Constraints at the document chunking step
Parsing must retain a document identity and version that can point back to the original text, so every chunk can be traced to a specific wave picking list or checking record. Chunking must follow the structure of the material itself and avoid separating an applicable condition from the detail rows it constrains.
Data and indexes are distinct. One group of original data can correspond to multiple indexes, and the indexes field in dataset.datas is an array, so one group of data maps to multiple vectors. Multi-vector mapping lets the same chunk be recalled through more than one path.
At retrieval, semantic search relies on vector distance and suits natural-language questions. Full-text search relies on keyword hits and suits identifiers, model numbers, and proper nouns. Hybrid search uses both and merges the results through RRF. Similarity filtering and the reference cap take effect at the result stage, and the minimum relevance value applies only under semantic search or when result reranking is used. Retrieval itself does not perform authorization judgment, and a retrieval result cannot serve as proof of operating permission.
Hypothetical paper exercise sample: the source material for comparison is three sentences. Sentence one: "Wave list W-0715 covers orders O-8801 and O-8802, picking locations A-03 through A-07." Sentence two: "Checking record R-0715 marks O-8801 as checked, and the checker signature field is blank." Sentence three: "Shipping label S-8802 is printed in English and corresponds to order O-8802." One draft sentence to be assessed: "O-8801 has completed checking and can be handed over for outbound." The draft sentence concerns subject O-8801, the object being checking status, and the effective scope of wave W-0715. Whether it holds requires comparison with the source text.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid search | The sample contains both identifiers and natural-language questions, so both recall types need coverage; compare against semantic search alone |
| Minimum relevance | Hypothetical starting value 0.3 | This value is a paper-exercise starting point for observing the candidate count after filtering; compare against running without filtering |
| Reference cap | Hypothetical starting value 1500 tokens | The cap controls reference volume by tokens; observe returned content length and compare against a smaller value for candidate completeness |
| Question optimization | Not enabled for now | The sample question has a clear subject; enabling it adds one model call, so compare against the elapsed time when enabled |
| Result reranking | Not enabled for now | A rerank model must exist and be enabled by configuration; compare against score ordering when enabled |
| Chunk granularity | Split by document structure | Preserves the field-to-condition correspondence; compare against fixed-length splitting for passage completeness |
Three common errors
- Treating the draft sentence "O-8801 has completed checking and can be handed over for outbound" as an established fact. Sentence two only states that the checking record marks O-8801 as checked and that the signature field is blank; it does not record handover authorization. The input is the draft sentence and the source text; compare whether subject, object, status, and effective scope match. The observable result is that the signature and authorization fields are absent, so the draft is judged unsupported.
- Treating a retrieval hit as operating permission. In the sample, retrieving a chunk related to W-0715 shows only that the content was recalled; it does not show that any handover or outbound action was approved. The input is the retrieval result; compare it against the authorization record. The observable result is that the retrieval result contains no authorization field, so it is judged not to constitute authorization evidence.
- Treating a successful upload as an updated index. After material changes, the actual training and index state must be checked. The input is the changed wave picking list; compare it against the index state in
dataset.datas. When training completion is not confirmed, the observable result is that the matter remains unverified.
How to verify the setup
- Use sentence one and sentence two of the sample source text as input, and compare the draft sentence "O-8801 has completed checking and can be handed over for outbound" against the source on subject, object, status, and effective scope. If the comparison shows the signature field and authorization field are absent from the source, the draft is judged unsupported. If the comparison cannot locate the corresponding fields, record the evidence gap.
- Search using order number O-8802 and using a natural-language question separately, then compare the recalled chunks for the two question forms. If the identifier query hits the chunk for shipping label S-8802 and the natural-language query hits a checking record chunk, the two question forms cover different passages. If one question form returns no hit, record it as an observed failure branch without inferring a cause.
- Use an empty input
[]to check the downstream handling design, and compare the system's returned content under an empty result against the expectation. If the return is empty, record the empty-result handling path. If the return is not empty, record the actual observed content without claiming that a fictional rule must return empty. - After a material change, compare the index state of the chunks before and after the change. If the index state shows training is not complete, retrieval results still point to the old chunks, and the matter is recorded as unverified. If the index state shows completion, record the retrieval content actually observed. All of these comparisons use this hypothetical sample as input, and no actual test is claimed.