What the documents look like
Brand member private-domain operations center on customer records and repeat-purchase outreach rather than store scheduling. For the inventory procurement and supplier materials task, the relevant documents include procurement requests, supplier quotations, supply terms and goods-receipt records, alongside member tag tables, outreach plans, coupon rules and privacy authorization records. The brief marks these source and update characteristics as approximate, and the update frequency it gives is daily or weekly. Where that frequency does not affect parsing or chunking, it can be left out of the implementation.
Parsing and chunking must preserve the constraint relationships among supplier name, material, quantity, quotation validity period and approval status. Member data and procurement materials may sit in the same knowledge base, and search runs against the whole base; a collection is a classification layer and does not affect search results. Document identity and version determine whether a later citation can point back to the source text, so a chunk should keep a condition, its explanation and the content it constrains together.
Constraints at the document chunking step
Parsed output must separate original data from retrieval indexes. One set of original data can map to several index entries, and a shorter, more precise index raises precision while sacrificing some retrieval range. Retrieval is a recall process and does not perform authorization checks; a similarity score is not a permission or entity filter. Knowledge bases built on different vector models cannot be associated with each other, so the check is to compare identifiers and exclude non-matching entries, then record what was observed. Image retrieval requires the corresponding image input and model capability, and reranking requires a model and must be enabled in settings; an optional path in the overall flow does not mean every request executes all paths. The citation cap is measured in tokens, and the minimum relevance threshold takes effect only under semantic retrieval or result reranking. Question optimization adds one model call before retrieval, which can improve multi-turn retrieval and increase latency.
Hypothetical paper exercise: A brand member operations team receives a supplier document whose source text reads: "Supplier A submitted a quotation for Material A on 1 March 2025, unit price 12 yuan, quotation valid until 31 March 2025, approval status pending review." A draft sentence reads: "Supplier A's quotation for Material A has been approved and procurement can proceed at 12 yuan." The comparison is between the approval status in the source text and the wording of the draft sentence. The source states pending review, the draft states approved, and the two values are mutually exclusive on the same field, so the draft sentence is unsupported. This exercise is for paper comparison only and does not indicate that any approval or procurement action occurred.
Configuration and implementation checks
| Check item | Value or state in this page's exercise | Comparison method |
|---|---|---|
| Document identity and version | Exercise sample marked Supplier A, 1 March 2025 | Compare subject and date against the draft sentence item by item |
| Segment completeness | Quotation, validity period and approval status in one segment | Check whether a condition and the content it constrains stay in the same segment |
| Original data and index | Exercise assumes a text index was generated for this segment | Compare the index identifier with its original-data correspondence |
| Search mode | Exercise starting point is hybrid retrieval | Compare recalled content against semantic retrieval and full-text retrieval separately |
| Citation cap | Exercise starting point is 2000 tokens | Compare returned content length against smaller and larger values |
| Minimum relevance | Exercise starting point is 0.3 | Compare the number of results after filtering against a higher threshold |
The values above are starting points for this paper exercise, chosen so that different settings can be compared and observed. They are not official defaults.
Three common errors
- Writing a pending-review status as approved. The input is the exercise source text and the draft sentence; the comparison is the approval status field; the source says pending review and the draft says approved, the two are mutually exclusive on the same field, and the draft sentence is unsupported.
- Treating a successful upload as an updated retrieval index. The input is the material change record and the training or index status; the comparison is the upload time against the actual index state; if the index state has not updated, retrieval may still return the earlier content.
- Treating a retrieval score as a permission filter. The input is one retrieval request and the returned records; the comparison is the returned content against the access scope; a high or low score does not change whether a record belongs to the current scope, and where authorization information is missing the gap remains.
How to verify the setup
- Use the exercise source text and the draft sentence, submit the draft sentence as a retrieval input, and compare whether the returned content includes the source approval status. If the returned content contains pending review and the draft sentence remains unsupported, the chunk has preserved the constraint relationship.
- Search separately with a term, an identifier and a natural-language question, using Material A and Supplier A as inputs, and compare whether the returned segment is complete. If quotation, validity period and approval status return in the same segment, segment completeness meets the check condition.
- After a material change, check the training and index status by comparing the change record and the index identifier against the upload time and the actual index state. If the index state has updated, retrieval can reflect the new content; if it has not, record the gap and wait for confirmation.
A retrieval check records observations and failure branches; it does not guarantee a first-hit result, full recall, or an empty result for an unrelated query. An empty result can be checked against a clearly labelled paper input of an empty list to test downstream design, without claiming that a constructed rule necessarily returns an empty list. Where a knowledge base uses a different vector model, first compare identifiers and exclude non-matching entries rather than running a cross-model search to compare missed recall.