What the documents look like
On-demand retail fulfillment processes online orders by the minute, so inventory procurement and supplier records arrive in a high-frequency, fragmented form. Store inventory, platform backend orders, picking lists, out-of-stock reports and delivery exception notes form the main data sources. Procurement and supplier materials commonly appear as arrival notes, quotation sheets, supply confirmations and return records. These materials update frequently, per order or at minute-level intervals, and document parsing and chunking must preserve fields that can be traced back to the source text: supplier name, item object, quantity, date and authorization status.
The structure of these materials directly shapes chunk boundaries. A supply confirmation may contain the item, unit price, validity period and signature status in one passage. When a condition is separated from the content it constrains, retrieval results lose the basis for judgment. Original data and retrieval indexes need to be distinguished: one group of original data can map to multiple indexes, and the number of indexes does not change the original data itself. Semantic recall and relevance scores measure text similarity only. They do not carry supplier qualification, authorization or entity isolation duties. Retrieval libraries that use different vector models cannot be associated together; identifiers must be checked first and non-matching entries excluded.
Constraints at the document chunking step
A hypothetical paper exercise sample: an arrival note states "Supplier A supplied 20 cases of mineral water to Store A on 10 March 2025, unit price 2 yuan, quotation valid until 10 April 2025. This batch has been signed for; procurement approval status is pending review." A draft sentence reads "Supplier A has obtained procurement approval and can continue supplying within the validity period." The materials available for checking contain the subject, object, quantity, date and status. The draft sentence turns pending review into approved, and extends the quotation validity period into a supply term.
Chunking must keep conditions and the content they constrain in the same block. The approval status field and the quotation validity field each need to stay attached to the supplier and item they describe. Retrieval checks observe what the chunking returns; they do not convert a similarity score into an authorization decision. A pending approval is not an approval, an offered option is not execution, and an unsigned termination is not a completed termination.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this exercise | Comparison and notes |
|---|---|---|
| Chunk boundary | Keep complete sentences for supplier, item, date and status | Compare sentence by sentence against the source; check whether conditions stay in the same block as the content they constrain |
| Index and original data distinction | One group of original data can map to multiple indexes | Check which original data an index field points to; confirm merging takes the highest score |
| Search mode | Start with semantic retrieval; use full-text or hybrid for numbers and supplier names | Observe chunk hit differences across terminology, number and natural-language queries |
| Reference limit | Starting point set to cover the sample's full token volume | This starting point is chosen for this paper exercise; compare against actual sample length and adjust |
| Minimum relevance | Starting point set to a mid value in the 0-1 range | Applies only in semantic retrieval or with result reranking; compare against the sample's relevance score |
| Post-update recheck | After material changes, check actual training and index status | A successful upload is not evidence that retrieval has updated |
Three common errors
- Writing pending review as approved. The sample source states "procurement approval status is pending review," while the draft sentence states "has obtained procurement approval." Input is these two sentences; compare the approval status field; the observable result is a status mismatch, and the draft sentence is not supported by the source.
- Treating quotation validity as a supply term. The sample source states "quotation valid until 10 April 2025," while the draft sentence states "can continue supplying within the validity period." Input is the validity field and the supply wording; compare whether they describe the same fact; the observable result is that quotation validity does not indicate a supply term.
- Using a similarity score to exclude an unrelated supplier. Input is the retrieval result for Supplier A and another supplier; compare the score and the supplier identifier; the observable result is that the score does not constitute entity or authorization filtering, and identifiers must be checked separately.
How to verify the setup
- Input the sample source text and the draft sentence; compare the approval status field; observe whether "pending review" and "approved" show inconsistent values. If the source does not state an approval outcome, record it as unsupported. A separate paper input of [] can check how the downstream design handles an empty result; do not claim that a fictional rule necessarily returns empty.
- Input the three query types "Supplier A," "mineral water" and "10 March 2025"; compare whether the chunks retain subject, object and date. Observe hits for number-type queries under full-text or hybrid retrieval; record a failure branch as a miss or a missing chunk.
- Input the same supplier record after a material change; compare upload status with actual training and index status. Observe whether retrieval returns updated content; when not updated, record it as pending verification, and do not treat a successful upload as retrieval having updated. Image retrieval requires image input and model capability; reranking requires a model and enabled settings; these paths do not necessarily execute on every request.