What the documents look like
Regional dealer stock replenishment runs as a daily rolling order flow, which differs in rhythm from seasonal centralized ordering. The main material set is made up of replenishment requests, inventory turnover sheets, outbound notes and payment plans. These come from dealer ERP records, the brand ordering system, warehouse stock and sales collection records, and they update on a weekly and monthly cycle. Three of these traits shape the document parsing and chunking step directly.
The materials carry matching relationships across subject, period and quantity basis. The dealer name, item and requested quantity in a replenishment request need to form a checkable field chain with the actual outbound record on the outbound note and the arrival arrangement in the payment plan. If chunking separates requested quantity from approval status, or outbound quantity from receipt status, a comparison cannot judge consistency inside one data unit. The material types also mix structured tables with natural language notes: an inventory turnover sheet is mostly numeric, while a replenishment request may carry a text remark. Chunking needs to keep header rows bound to their data rows. A payment plan involves a time point and an amount, which sit in different fields from the date and quantity on an outbound note, so chunking should not force the two into a single semantic unit.
Constraints at the document chunking step
Parsing starts by checking the source structure against the parsed result and retaining a document identity and version that can point back to the source. Chunk completeness means checking paragraphs against the actual material structure, so a condition, a note and the content it constrains are not split apart. Data and index need to be kept distinct: one group of source data can map to multiple indexes. Sample retrieval should run separately with terminology, with an identifier and with natural language, to see how segmentation affects a real question. After a material change, the actual training and index state needs checking; a successful upload does not by itself mean retrieval has updated.
Hypothetical paper exercise: a regional dealer submits a replenishment request reading "Dealer: East China Sample Trading; Item: Type A filter; Requested quantity: 200 units; Request date: 10 March 2025; Remark: for week 2 March store replenishment." The outbound note in the same batch reads "Outbound note no.: CK-20250310-017; Dealer: East China Sample Trading; Item: Type A filter; Outbound quantity: 180 units; Outbound date: 12 March 2025." The draft sentence to be judged is "This dealer's March replenishment request was fully shipped at 200 units." The comparison target is the requested quantity and outbound quantity fields for the same subject, the same item and the same period.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Reason and comparison object |
|---|---|---|
| Search mode | Hybrid search | Replenishment requests carry both item names and identifiers, so semantic and keyword hits both matter |
| Reference cap | Counted in tokens, starting value set to cover the full text of both materials | Compared with the combined token total of one replenishment request plus one outbound note, to avoid cutting key fields |
| Minimum relevance | Starting value set at a middle level | Compared with semantic or rerank scores to filter low-relevance fragments |
| Question optimization | Enabled or not according to the actual dialogue | Compared with a single clear question when following up on replenishment status across turns |
| Chunking method | Set separately by material type | Turnover sheets bound by header, replenishment requests kept whole as one request form |
| Post-update recheck | After upload, check training and index state | Compared with what retrieval actually returns, to confirm the new chunks can be recalled |
Three common errors
- Treating a requested quantity and an outbound quantity as a direct contradiction. The input is a request of 200 units and an outbound of 180 units, and the comparison is between explicit mutually exclusive values in the same field. Two different numbers indicate partial shipment and do not amount to a contradiction; the outbound note needs to be checked for a stated partial-shipment note.
- Treating a successful upload as retrieval already updated. The input is a retrieval run right after a material change, and the comparison is between training state and index state. A successful upload and an available index are different facts, so the actual training completion state needs checking.
- Using a semantic recall result in place of authorization verification. The input is a replenishment request fragment returned by retrieval, and the comparison is whether that fragment contains an authorization signature status. A retrieval hit does not mean approval is complete; signature, deal reached and contract effective are three different facts.
How to verify the setup
- Input the replenishment request text and the outbound note text from the hypothetical exercise, and compare the requested quantity and outbound quantity fields for the same subject, the same item and the same period. If the retrieval result returns both materials and the fields can be located, and chunking has kept header rows bound to data rows, the check condition is met.
- Input the draft sentence "This dealer's March replenishment request was fully shipped at 200 units," and compare the quantity statement in the draft against the 180 units actually stated on the outbound note. If the comparison flags the quantity mismatch and does not judge partial shipment as a contradiction, the check condition is met.
- Input a retrieval request after a material change, and compare the upload record against the actual training and index state. If retrieval returns the new chunk content and it can point back to the source version, the check condition is met; if it returns an empty result, record it as pending verification rather than judging retrieval as failed.