What the documents look like
Multi-store procurement and supplier material for a platform store matrix revolves around four document types: store authorization lists, price control tables, cross-territory complaint forms, and monthly sales reports. Brand e-commerce leads, channel operations staff, and dealer e-commerce teams spend more time reconciling cross-store authorization and price order than on single-store traffic efficiency. Material updates follow a daily and monthly rhythm, but the feature on this page does not depend on batch synchronization or transaction-triggered mechanisms.
Three facts directly affect the vector model and index step. First, within the same batch of material, authorization lists emphasize subject and authorization scope, price control tables emphasize model and price fields, complaint forms emphasize factual description, and monthly sales reports emphasize period and quantity. Field definitions differ widely. Second, queries involving numbers, models, and proper nouns tend to return overly broad results under semantic retrieval; full-text retrieval and hybrid retrieval suit this kind of exact matching better. Third, one original data block can be associated with multiple indexes, and index content must be checked separately from original text content.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Verify the actual model and input type first. Libraries using different vector models cannot be associated together; the handling method is to check identifiers, exclude non-matching ones, and record actual observations, without running cross-model retrieval to compare missed recalls. Index content is built from complete and focused information, and retrieval coverage versus index length must be weighed against samples. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time; looking only at the query side is insufficient.
Hypothetical paper exercise sample (paper material, not a real record):
Material one (store authorization list excerpt): The subject is a brand e-commerce department, the objects are three platform stores, the authorization scope is online sales of the brand's household water purifier series, and the authorization valid period is January 1, 2025 to December 31, 2025.
Material two (price control table excerpt): The suggested retail price field for model A-100 is marked "pending channel department review," and the review status field is "incomplete."
Draft sentence to be assessed: "The authorization of the brand e-commerce department for the three platform stores expired on December 31, 2025, and the price of model A-100 has been approved by the channel department."
Assessment points: Material one states the authorization valid period. The draft sentence's expiration conclusion matches the period field, but the material does not record renewal or termination facts. Material two only shows that review is incomplete; the approval conclusion has no support. Both points require comparison of the same subject, same period, and same field.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Select two libraries using the same vector model | Check the model field of each library identifier and exclude those with inconsistent models |
| Search mode | Hybrid retrieval | Compare retrieved source text item by item against pure semantic retrieval results, observing hits for number-type queries |
| Reference upper limit | Set to a trial starting value | Chunk lengths in mixed knowledge bases vary widely; compare reference counts against different starting values for stability |
| Minimum relevance | Set to a trial starting value | This value takes effect only under semantic retrieval or result reranking; compare filtered entries against different starting values |
| Result reranking | Enable ReRank per settings | Reranking requires a model and must be enabled per settings; compare candidate ordering before and after reranking |
| Question optimization | Decide whether to enable per scenario | Question optimization adds one model call before retrieval; compare multi-turn follow-up and explicit questions |
The reference upper limit uses tokens as its unit and does not use top k. RRF is a hybrid result fusion process and is not written as a toggle switch. Semantic recall and relevance do not handle permission or entity isolation.
Three common errors
- Forcibly associating knowledge bases across different models. Input: the model identifiers of two libraries. Comparison: whether the two identifiers match. Observable result: association is executed despite mismatched identifiers. Correct handling: check identifiers and exclude non-matching ones.
- Treating a pending-review status as completed approval. Input: the "incomplete" review status field in the price control table. Comparison: the draft sentence's "has been approved" against the field value. Observable result: the draft sentence exceeds the status recorded in the field. The conclusion has no support.
- Writing authorization expiration directly as authorization termination. Input: the valid period field in the authorization list. Comparison: the draft sentence's "expired" against the period field. Observable result: the period field supports the expiration statement, no corresponding field supports termination facts, and the evidence gap must be retained.
How to verify the setup
- Input hypothetical sample material one and the authorization portion of the draft sentence. Compare the subject, object, and valid period fields. The condition is met when the draft sentence matches the period field and does not fabricate termination facts.
- Input hypothetical sample material two and the price portion of the draft sentence. Compare the review status field against the "has been approved" statement. The condition is met when the approval conclusion is found unsupported and the gap is recorded.
- Input the same set of number-type queries. Compare the retrieved source text returned by hybrid retrieval against pure semantic retrieval. The condition is met when the full-text path is observed to hit numbers while the semantic path returns broad results, without promising first-hit or complete recall on that basis.
Empty-result handling can use an explicit paper input of [] to check downstream design; it cannot claim that a fictional rule retrieval definitely returns []. Conclusions not stated or supported only by related-stage evidence remain unsupported or pending verification.
This feature has verified public technical facts (used to identify related content in documents; not required to be copied as a fixed five-row table; the body is written independently): vector model — knowledge bases associated for retrieval must use the same vector model, verify the actual model and input type first; original data and indexes — one original data block can be associated with multiple indexes, and index and original text content are checked separately; index content — build indexes from complete and focused information, and weigh retrieval coverage against index length using samples; image index — image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time, not only on the query side; version and result review — after model or index adjustments, compare retrieved source text using the same set of questions, and do not promise results based on vector scores alone.