What the documents look like
Central chain procurement documents follow head-office purchasing and distribution. They do not cover store display checks. The daily document set is made up of centralized procurement contracts, replenishment plans, distribution orders and store requisition forms. Data arrives from ERP purchasing, WMS, store POS and supplier delivery records. Updates follow a daily and weekly rhythm.
In cross-border work, one centralized procurement contract can exist in a Chinese version and a foreign-language version at the same time. Supplier delivery records and store requisition forms may also use different languages. These differences decide whether a citation can be traced line by line back to a specific document version. Semantic retrieval supports cross-language understanding, so a Chinese question can match a foreign-language knowledge point. That ability comes from the vector model itself and has no connection to permissions or entity isolation.
Constraints at the citation tracing step
The task at retrieval is to locate the source text behind an answer and keep it traceable. The knowledge base search node selects one or more knowledge bases that use the same vector model, and its output is an array of citations whose length may be zero. When the array is empty, the output path still runs through. The minimum relevance value is a filter threshold that takes effect only in semantic retrieval or when result reranking is used. It does not perform permission filtering or business entity isolation. Hybrid retrieval merges multiple result paths through RRF. RRF is a fusion process and has no corresponding switch.
Hypothetical drill sample: a chain supply chain center knowledge base holds an excerpt of a centralized procurement contract. The source text reads: "Party A commissions Party B to deliver 2,000 units of Type A insulated containers to the East China distribution center between 1 March 2025 and 31 March 2025, with the unit price subject to the attached quotation sheet." A separate store requisition record reads: "Store M-017 submitted a requisition for 120 units of Type A insulated containers on 5 March 2025, status pending review." A draft sentence reads: "The supplier has completed delivery of 2,000 units of Type A insulated containers under the centralized procurement contract." When judging this draft sentence, the contract text states only a delivery period and a quantity agreement; it does not state that delivery actually occurred. The requisition record shows a status of pending review, which does not mean approved or dispatched. The draft sentence turns a contract agreement into a completed delivery and is unsupported. Pending review does not show a lack of operating permission, and it does not equal approval.
Configuration and implementation checks
| Configuration item | Type | Relevant note for this page |
|---|---|---|
| Associated knowledge bases | Existing node behavior | Only knowledge bases with the same vector model can be selected; check identifiers first and exclude those that do not match, without running cross-model retrieval comparisons |
| Search mode | Configurable field | Semantic retrieval supports cross-language matching; exact queries for numbers and models suit full-text or hybrid retrieval |
| Minimum relevance | Configurable field | A 0-1 value that takes effect only in semantic retrieval or result reranking; it does not perform permission or entity isolation |
| Citation limit | Configurable field | Controlled in tokens, not top k; this hypothetical trial uses a starting value of 2,000 tokens because the sample contract excerpt and requisition together run about 1,500 characters, leaving margin, compared against the value shown in the interface |
| Citation content output | Existing node behavior | Array format, length may be zero; the output path still runs when the array is empty |
| Document timeliness check | Manual verification action | Test sources with the current version and a historical version; whether a citation is adopted still depends on review of the valid version |
Three common errors
- Treating the delivery period and quantity in the contract as evidence of completed delivery. The input is the centralized procurement contract excerpt and the draft sentence; the comparison is between the contract wording "commissions delivery" and the draft wording "has completed delivery"; the observable result is that both share the same parties and the same period while the fields differ, the contract does not state actual delivery status, and the draft sentence is unsupported.
- Reading the requisition status "pending review" as approved or dispatched. The input is the requisition record and the draft sentence; the comparison is between "pending review" and "completed delivery"; the observable result is that the status field values differ, the draft sentence lacks evidence for the approval and dispatch stages, and it is unsupported.
- Treating minimum relevance as a permission or entity isolation mechanism. The input is the retrieval parameter setting and the query request; the comparison is between the documented definition of minimum relevance and the permission control requirement; the observable result is that the document describes the value as filtering low-relevance results only, with no authorization role, so permissions need a separate system implementation.
How to verify the setup
- Trace citations line by line. The input is the contract source text and the requisition record from the hypothetical sample; compare each citation in the answer against the source on party, object, period and status fields; the condition is met when every citation can be matched to a sentence in the source and pending review is not written as approved.
- Check empty-result handling. The input is a paper input of []; compare the downstream node design for handling an empty array; the condition is met when the downstream can accept a citation array of length zero and continue execution, without claiming that a fictional rule retrieval must return [].
- Check exclusion of cross-model association. The input is two knowledge base identifiers that use different vector models; compare the range of knowledge bases the node allows to associate; the condition is met when only knowledge bases with the same vector model are associated, mismatched identifiers are recorded and excluded, no cross-model retrieval is run to compare missed recalls, and no runtime result of joint association is inferred.