What the documents look like
Closure settlement handles exit and asset recovery, the reverse direction from head-office site selection and store opening. The main materials are closure approval forms, inventory clearance sheets, lease termination agreements, and asset count sheets, drawn from POS inventory, fixed-asset systems, lease contracts, and financial ledgers. Updates are irregular and per store; this page does not derive batch synchronization or real-time trigger mechanisms from that pattern.
Supplier records and procurement records support closure-stage reconciliation of unsettled payables, return-of-goods responsibility, and the basis for deposit offset. Field completeness varies across these materials, and one supplier may have separate contract numbers, settlement periods, and authorization states at different stores. The vector model and index step handles exactly this material: clearance sheets, count sheets, and termination agreements are stored as original data blocks, and retrieval results point back to verifiable source text.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model; check the actual model and input type first. One original data block can be associated with multiple indexes, and index content is checked separately from original text content. Indexes are best built from complete and focused information, and retrieval coverage against index length is weighed against samples. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time; the query side alone does not determine it.
Semantic recall and the minimum relevance score do not handle permission or entity isolation. A similarity score is not an entity filter, and retrieval itself does not constitute authorization. Closure materials involve deposits, franchise fees, and employee settlement, so retrieved content still requires manual checking of authorization state. After a model or index change, compare cited source text using the same question set; do not make commitments about outcomes from vector scores alone.
Hypothetical paper exercise sample
The following is a paper hypothetical sample used to explain the checking method. It was not tested against real records.
Source text one: "Store A closure approval form, hypothetical number ZD-2024-001, approval status pending review, application date 1 March 2024, scope limited to that store's inventory clearance."
Source text two: "Procurement contract between Supplier Jia and Store A, hypothetical contract number CG-2023-017, agreed settlement period January 2023 to December 2023, return-of-goods responsibility clause in Article 5."
Draft sentence to be judged: "Store A has completed closure approval, and Supplier Jia's payable has been settled."
Judgment points: the approval form states the status as pending review, while the draft sentence says approval is complete; the same field holds mutually exclusive values, so the sentence is unsupported. Whether the payable is settled is not stated in the given source text; that is an evidence gap, and it cannot be judged as settled or unsettled.
Configuration and implementation checks
The table below covers this sample and lists stated settings or implementation checks relevant to this feature and the primary task.
| Configuration item | Hypothetical starting point and reason | Comparison method |
|---|---|---|
| Vector model | Assume one shared model, because associated knowledge bases must use the same vector model | Check model identifiers across bases and exclude inconsistent ones |
| Index content | Assume separate indexes for clearance sheets and contract clauses, because indexes should be focused | Compare cited source text against the corresponding material using the same question set |
| Original data to index mapping | Assume one original data block maps to multiple indexes | Check whether index fields match original data content |
| Search mode | Assume semantic search first, switching to full-text or hybrid search for identifier-type questions | Observe hits separately for identifier queries and natural-language queries |
| Minimum relevance | Assume a starting value of 0.3, because low-relevance results need filtering | Compare cited source text for the same question set before and after adjustment |
| Citation cap | Assume a tokens-based setting, because the citation cap is controlled in tokens | Observe whether returned citation length and coverage change |
The minimum relevance score takes effect only under semantic search or when result reranking is used. After hybrid search the range is large, and reranking with score-based filtering is usually needed. RRF is the fusion process for hybrid results and is not a switch setting.
Three common errors
- Treating pending review as completed approval. The input is the approval form source text; compare the value of the approval status field. If the source says pending review while the draft sentence says approval is complete, the sentence is unsupported. This error writes an incomplete state as a completed one.
- Treating a retrieval hit as authorized execution. The input is the cited content returned by retrieval; compare it against authorization fields. If the cited content contains no authorization conclusion, the retrieval result is reference material only and does not support deposit refund or payable settlement. This error conflates retrieval with business authorization.
- Associating knowledge bases across different vector models. The input is the model identifier of each knowledge base; compare whether the identifiers match. If they differ, exclude that base and do not run cross-model retrieval to compare missed recalls. This error merges retrieval scope without checking identifiers.
How to verify the setup
- Verification one: input hypothetical sample source text one and the draft sentence; compare the approval status field value. If the source says pending review while the draft sentence says approval is complete, judge the sentence unsupported and preserve the evidence gap. Expected result is unsupported; not actually tested.
- Verification two: input hypothetical sample source text two; compare the settlement period field. If the draft sentence says the payable is settled while the source does not state settlement status, judge it as to be verified, and do not write it as settled or unsettled.
- Verification three: input an identifier-type query and a natural-language query; compare whether cited source text points to the corresponding material. If the identifier query misses under semantic search but hits under full-text or hybrid search, record the observation; do not state that a first-position hit or full recall will occur.
Empty-result handling can be checked against downstream design using an explicit paper input of an empty array; do not claim that retrieval under an invented rule returns an empty array. Retrieval checks record observations and failure branches, and do not state that unrelated queries return empty.