What the documents look like
Brand omnichannel stock transfer work follows goods moving between stores and warehouses. It does not handle marketing outreach. The main data sources are ERP inventory, store POS, e-commerce orders and WMS. The documents handled day to day are transfer requests, inventory snapshots, store shortage lists and transfer logistics notes. In cross-border work these materials often exist in parallel language versions: one transfer batch may carry a Chinese request and an English logistics note at the same time. Material updates follow a daily or weekly rhythm.
Parsing and chunking must preserve each document's own language, identifiers and version so that later retrieval can match terminology, identifiers and natural-language questions through separate paths. A single group of source data can map to several vector indexes, and retrieval recalls that source data when any one index is hit, with the vector score taken as the highest score. Semantic retrieval can use a vector model for cross-language understanding. Full-text retrieval depends on keyword hits and suits identifiers, model numbers and proper nouns. Hybrid retrieval fuses semantic and full-text results through RRF, and ReRank then reorders candidate text. Image retrieval requires a corresponding index to have been created at ingestion time and depends on a vision model or a vector model that supports images; that path does not run on every request.
Constraints at the document chunking step
Chunking must keep each material's conditions, quantities and constrained object complete. When a paragraph's structure and meaning are complete and single, precision improves; shortening index content raises precision while narrowing retrieval range, and adding several indexes to one chunk raises the number of recall paths. Because content length and semantic richness pull against each other in one vector, multiple vector mapping is the documented way to keep both data completeness and semantic richness.
A hypothetical paper exercise sample is used below for checking only. A brand supply chain specialist receives a Chinese transfer request reading: "Request to transfer 120 units of SKU-A from the East China warehouse to the Shanghai store, to cover the store shortage from 15 March to 20 March; request status is pending review." The same batch includes an English logistics note reading: "Transfer of 120 units of SKU-A from East China Warehouse to Shanghai Store, logistics status is pending pickup." The draft sentence to be judged is: "The transfer request has been approved and the logistics note shows it has been dispatched." This sample checks whether parsing and chunking results can support that judgment. No test, deployment or approval has taken place.
Configuration and implementation checks
The table lists documented settings or implementation checks relevant to this page. Values are starting points for this hypothetical exercise, used for comparison against retrieval observations.
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Materials contain identifiers and natural language; compare whether semantic and full-text recall both cover the sample terms |
| Minimum relevance | 0.5 | Exercise starting point; observe whether the sample is still recalled after filtering, then compare with 0.3 |
| Reference limit | 2000 tokens | Controls content referenced in one search; compare whether sample chunks enter the reference range intact |
| Question optimization | Enabled | Sample contains reference and short phrasing; compare retrieval terms before and after enabling |
| Result reordering | Enabled | Reorders candidates when there are many; compare ReRank score against vector score |
| Image retrieval | Per model capability | Sample has no image input; check ingestion index status only, and do not run the image retrieval path |
The reference limit uses tokens as its documented unit. Minimum relevance is a filter threshold, not a final ordering rule, and it applies only under semantic retrieval or when result reordering is used. RRF is a fusion step for combining result paths, not a switch. Image retrieval, question optimization and reordering are optional paths and do not all run on every request.
Three common errors
- Treating a successful upload as an updated retrieval state. Input: the sample transfer request after upload. Compare the upload record against the actual training and index state. Observable result: if training has not completed, the chunk cannot be recalled and the query returns nothing for it; the upload record alone does not show recallability.
- Treating a semantic recall score as a permission or entity filter. Input: the "pending review" status in the sample. Compare returned content against the authorization status field. Observable result: a relevance score does not exclude unauthorized records, so returned content may include records outside the requester's authorization scope; relevance is not authorization.
- Treating "has been approved" in the draft sentence as a verified fact. Input: the sample's "pending review" status and the draft sentence. Compare the same field for the same subject and period. Observable result: pending review and approved are mutually exclusive values in the same field, so the draft sentence is unsupported by the source text and remains pending verification.
How to verify the setup
- Chunk completeness check. Input the sample transfer request text. Compare whether each chunk after splitting retains SKU-A, 120 units, 15 March to 20 March and the pending review status. Observable result: the condition and quantity appear in the same chunk as the object they constrain; if a quantity or date range is separated from its object, record the gap.
- Multilingual retrieval check. Input "SKU-A transfer" and the corresponding Chinese query separately. Compare whether returned chunks point to the same group of source data. Observable result: the identifier and terminology are hit in both queries; if one query returns nothing or points to different source data, record it as a gap rather than assuming recall.
- Status judgment check. Input the sample source text and the draft sentence. Compare "pending review" and "has been approved" for the same subject, same period and same field. Observable result: the two values are mutually exclusive in that field, so the draft sentence is not supported by the source text and remains pending verification. Missing evidence stays missing.
These checks observe retrieval behavior and failure branches; they do not guarantee a first-hit result, full recall, or an empty result for unrelated queries. A pending request is not approval, and an offered option is not execution.