What the documents look like
Central chain procurement and distribution material follows head-office purchasing and delivery rather than store-level display. The working set includes centralized procurement contracts, replenishment plans, distribution orders and store requisition orders. Supporting data arrives from ERP purchasing, WMS, store POS and supplier delivery records, updated daily or weekly. Compared with price and display auditing, this category does not examine store presentation. Retrieval targets purchasing commitments, distribution execution and requisition status. Update frequency does not change what retrieval does; it only affects when a comparison is made.
A single requisition order may carry a status field, a quantity, a requesting store and a delivery wave. A centralized procurement contract may carry a contract number, a validity window, a material identity and a supplier. These fields are the material identities that a retrieval result must be compared against, and a requisition status is not the same fact as an approval record.
Constraints at the retrieval step
The knowledge base search node can associate one or more libraries that use the same vector model. Libraries built on different vector models cannot be associated together. The vector model identifier must be checked first, non-matching libraries excluded, and the actual observation recorded. Retrieval is a recall action and does not constitute authorization. A relevance score is a filter threshold, not a category, entity or permission filter, and it does not exclude unrelated businesses or unauthorized records.
Semantic retrieval, full-text retrieval and hybrid retrieval have different conditions. Numbers, models and proper nouns suit full-text retrieval; natural-language questions suit semantic or hybrid retrieval. Hybrid retrieval merges multiple result channels through RRF, and RRF is a fusion process rather than a switch that can be turned on. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used. The reference cap is measured in tokens. Question optimization performs coreference resolution and question expansion and adds an extra model call. Image retrieval requires an image input and the corresponding model capability; reranking requires a model and must be enabled by configuration. The reference output is an array whose length can be zero, so the output path continues even when nothing is retrieved.
Hypothetical paper exercise sample. The source text reads: "The head-office supply chain center signed centralized procurement contract JC-2024-017 with Supplier A, covering carton procurement from 1 March 2024 to 30 June 2024; the distribution center compiles store requisition orders into a monthly replenishment plan before the 25th of each month." The draft sentence under review reads: "Supplier A completed all carton deliveries in April 2024." The source states the contract period and how the replenishment plan is generated, and states no delivery completion fact. The draft sentence is unsupported and requires separate verification against delivery data.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the procurement and distribution libraries sharing one vector model | Check the vector model identifier, exclude non-matching libraries, and do not run cross-model retrieval |
| Search mode | Begin with hybrid retrieval for observation | Compare against semantic and full-text retrieval on number-type and natural-language questions |
| Reference cap | Start at a value that can hold one complete replenishment plan paragraph | Compare with a smaller value and observe whether the replenishment plan is truncated |
| Minimum relevance | Start at a middle value | Label relevant and irrelevant samples first, then compare with higher and lower values |
| Question optimization | Enable in multi-turn follow-up scenarios | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enable by configuration when a model is available | Compare candidate text order before and after reranking and observe relevance score changes |
These values are hypothetical starting points for this exercise, not official default values.
Three common errors
- Treating a retrieval hit as business authorization. Input a requisition-order retrieval request, compare the retrieval result with the authorization status field, and if the requisition content is returned while no approval record exists, the result only shows the material is recallable and does not show the requisition was approved.
- Treating a pending review as missing operation permission. Input a replenishment plan record, compare its review status field, and when the status is pending review record it as requiring verification rather than judging it as lacking permission.
- Merging signing, cooperation reached and contract effectiveness into one fact. Input centralized procurement contract material, compare the signing date, effectiveness clause and performance record fields, and when any field is absent the fact remains unsupported.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence, compare the delivery completion wording in the draft against the contract period and replenishment plan generation method stated in the source, judge it unsupported because the source states no delivery completion fact, and record the evidence gap.
- Input a cross-vector-model association request, compare the vector model identifiers of each library, exclude the library when identifiers differ, and record the actual observation without running cross-model retrieval to compare missed recalls.
- Input a clearly labelled paper empty result [], check how the downstream design receives an empty reference, and observe whether the output path continues, without claiming that a fictional rule retrieval necessarily returns [].
All three verification items use the hypothetical sample as input. Expected results require actual execution before observation, and this page does not claim any test has been performed.