What the documents look like
Price and display auditing happens during day-to-day operations, and its materials focus on whether standards continue to be executed. For the inventory procurement and supplier materials task, the documents that need to enter the knowledge base include store visit photographs, price tag lists, display standards, and rectification notices, together with supply records, quotation sheets, and acceptance vouchers from the procurement and supplier side. These materials are updated weekly or monthly, so retrieval faces multiple batches and multiple sources coexisting across versions.
Three industry facts directly affect vector model and index implementation. First, material formats are mixed: store visit photographs are images, while price tag lists and rectification notices are text, and the two require different ingestion paths. Second, subject and object must remain distinguishable: the price tag and display requirements for the same store can differ across promotion periods, and an index that does not retain store, promotion period, and supplier qualifiers cannot locate retrieval results to a specific object. Third, authorization status and retrieval results are separate: rectification notices, quotation sheets, and acceptance records in the materials each sit at a different circulation stage, and a retrieval hit only means the content is related, not that the material has been approved or executed.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Confirm the actual model and input type first. Bases on different vector models cannot be associated together; the handling is to check the model identifier, exclude those that do not match, and record the actual observation, without running cross-model retrieval to compare missed recalls.
One source data block can be associated with multiple indexes, and the index and the original content are checked separately. Index content should be complete and focused, and retrieval coverage and index length are weighed against the sample. Image retrieval also depends on whether an image description or image vector was generated at ingestion time; whether the query side uploaded an image is not the only factor.
Retrieval paths hold according to the conditions listed in the documents: image description retrieval requires an available vision model, image vector retrieval requires a vector model that supports image input, and result reranking requires a rerank model enabled according to settings. Hybrid retrieval merges multiple result paths through RRF, and RRF is a merging process, not a switch to be turned on. Minimum relevance takes effect only in semantic retrieval or when result reranking is used; it is a filtering threshold and does not perform permission or entity isolation. The citation limit uses tokens as its unit, and empty reference output is returned as an array whose length can be 0.
Hypothetical paper exercise sample: a regional supervisor at a chain brand submits a store visit record in a paper exercise. The original text reads: "Week 2 of March 2025, store A07 refrigerated cabinet price tag shows 9.9 yuan, the headquarters price tag list for that promotion period marks 8.9 yuan, and the store display standard requires this item to be placed on the second layer." In the same batch of materials there is a draft sentence awaiting determination: "Store A07 has completed rectification, and the price tag has been changed to 8.9 yuan." That draft sentence carries no signature, photograph, or acceptance voucher and is in a pending verification state.
Configuration and implementation checks
| Configuration item | Relevant setting or check on this page | Correspondence with the sample |
|---|---|---|
| Associated knowledge bases | Select knowledge bases with the same vector model; check the model identifier and input type first | The price tag list and display standard must fall within knowledge bases of the same model |
| Index content | Build a complete and focused index for the source data block; check the index and the original text separately | The store visit record index should retain store, promotion period, item, and price fields |
| Image index | Check whether an image description or image vector was generated at ingestion | Whether store visit photographs can be retrieved depends on the ingestion index, not the query side |
| Search mode | Choose semantic, full-text, or hybrid retrieval according to the query type | Price amounts and promotion period numbers are precise queries suited to full-text or hybrid retrieval |
| Citation limit | Set a hypothetical exercise starting value in tokens and compare it with the reference original text length of the same question group | A smaller starting value makes it easier to observe whether the cited content covers the complete record |
| Minimum relevance | Takes effect only in semantic retrieval or result reranking, as a filtering threshold | Used to observe whether low-relevance fragments are filtered, not to isolate stores or suppliers |
Three common errors
- Treating a retrieval hit as an authorization status. In the sample, the draft sentence "Store A07 has completed rectification" carries no signature or acceptance voucher, and a retrieval hit on that sentence only shows the content is related; the pending verification state remains unchanged. The incorrect practice is to judge rectification complete based on the hit.
- Treating a vector score as a permission or entity filter. Minimum relevance is a filtering threshold, and a similarity setting does not exclude unrelated businesses or unauthorized records. The incorrect practice is to use a relevance threshold in place of checking the store or supplier scope.
- Treating RRF or image retrieval as a fixed step executed on every request. Image retrieval requires image input and the corresponding model capability, and reranking requires a model enabled according to settings. The incorrect practice is to assert that image vector retrieval participated when no image input was present.
How to verify the setup
- Input the sample original text "Week 2 of March 2025, store A07 refrigerated cabinet price tag shows 9.9 yuan, the headquarters price tag list for that promotion period marks 8.9 yuan," and compare whether the reference original text returned by retrieval contains the store, promotion period, item, and both price values. When the returned content lacks any field, record it as not satisfied and do not fabricate the missing field.
- Input the draft sentence "Store A07 has completed rectification, and the price tag has been changed to 8.9 yuan," and compare the retrieval result with whether a signature, photograph, or acceptance voucher exists in the materials. When no voucher is attached, record it as pending verification and do not judge rectification complete.
- Use a clearly defined paper input of an empty array to check the downstream design, and observe whether the empty reference output path runs through. When retrieval returns an empty result, record the actual observation and do not claim that a certain rule necessarily returns an empty array.
After a model or index adjustment, compare the reference original text using the same question group, and do not promise an effect based on the vector score alone. Error codes, automatic business actions, or effective clauses not stated in the materials remain unknown, and evidence gaps are recorded.
This page addresses only: chain store price and display auditing / inventory procurement and supplier materials / vector model and index. Which materials, operations, and requests sit at which authorization status during operational handover cannot be treated as authorization execution through retrieval or forms.