What the documents look like
Community group-buy leader operations revolve around community pickup nodes and commission settlement. The working materials are leader agreements, commission tables, pickup lists and community announcements. These materials update daily or per group, and they come from the group-buy backend, leader chat groups, order details and after-sales records. This page addresses one task only: retrieval and recall inside knowledge-base search when leader agreements and transaction materials are being compared. A retrieval hit and a form value are not authorization to act.
Three industry facts shape this task. First, leader operations manage community nodes and commissions rather than cold-chain inspection, so the documents under comparison are contractual and settlement records, not quality-control records. Second, the material set is small in type but high in turnover: agreements, commission tables, pickup lists and announcements are refreshed on a daily or per-group rhythm, which means a comparison can easily be run against a version that no longer matches the current settlement period. Third, the consequences of getting a comparison wrong are leader attrition, mis-shipment or missed shipment, and community complaints, so a comparison result that overstates what a document supports carries real operational weight.
For leader agreements, signing, cooperation being reached, and the contract taking effect are three separate facts. A comparison must check them separately and must not let one stand in for another.
Constraints at the retrieval step
Knowledge-base search treats the library as the smallest search unit; collections exist only to classify and manage data and do not affect search results. The knowledge-base search node can associate one or more knowledge bases 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.
Semantic search, full-text search and hybrid search suit different materials. Queries built on numbers, models and proper nouns are better served by full-text or hybrid search. Minimum relevance takes effect only under semantic search or when result reranking is used; it is a filter threshold, not the final ranking rule, and it does not perform permission or entity isolation. RRF is the fusion step that combines multiple recall paths in hybrid search, not a switch that can be turned on. The reference upper limit is measured in tokens and controls how much content a single search may cite. When question optimization is enabled, it performs coreference resolution and question expansion and adds an extra model call.
Image retrieval is not a separate capability outside the knowledge base. It depends on whether the current model can accept images and on whether an index was built at ingestion time. The overall flow diagram lists optional paths; not every request executes all of them.
Configuration and implementation checks
The table below lists documented settings and implementation checks relevant to this page's task, organized around the hypothetical drill sample in the next section.
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the leader agreement library and commission table library whose vector model identifiers have been verified as identical | Compare the vector model identifiers across libraries, exclude non-matching ones, and do not run cross-model retrieval |
| Search mode | Use hybrid search for fields such as agreement numbers and commission ratios | Compare against pure semantic search results and observe hit behavior on number-type queries |
| Reference upper limit | Set to 1200 tokens | Hypothetical starting point for this trial; compare content completeness using agreement and commission fragments of differing length, then adjust |
| Minimum relevance | Set to 0.35 | Hypothetical starting point for this trial; label relevant and irrelevant samples first, then compare what survives filtering |
| Question optimization | Enable when following up on leader agreement clauses across turns | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enable when candidates are numerous and a rerank model is available | Compare rerank scores with vector scores and observe ranking differences |
These values are trial starting points, not documented defaults. Each needs a stated reason and a comparison method before any adjustment.
Three common errors
- Treating a retrieval hit as an authorization basis. Hypothetical drill sample: the leader agreement text reads "This agreement takes effect on the date both parties sign it," while the draft sentence reads "This leader is authorized to carry out pickup for this group." Signing, cooperation being reached, and the contract taking effect are three distinct facts. Retrieving the agreement text does not show that signing was completed or that the contract took effect. The draft sentence is unsupported and the evidence gap must be recorded.
- Treating minimum relevance as a permission or entity filter. Hypothetical drill sample: retrieval returns a fragment of a leader's commission table with a relevance of 0.82, and the draft sentence reads "This leader is entitled to view all commissions for this group." Relevance is a similarity value and does not perform permission isolation. The draft sentence remains unverified and requires a separate permission system check.
- Associating knowledge bases across different vector models. Hypothetical drill sample: the leader agreement library and the pickup list library have different vector model identifiers but were associated together anyway. At this point only the identifiers are checked and non-matching ones excluded, with the actual observation recorded. No inference about missed recall, redundancy or any other runtime outcome follows from this.
How to verify the setup
All three checks below use the hypothetical drill sample just given, and each states what is input, what is compared, and what condition is met.
- Input the agreement text "This agreement takes effect on the date both parties sign it" and the draft sentence "This leader is authorized to carry out pickup for this group." Compare whether the agreement text states that signing was completed and that the contract took effect. The text provides no signing-completion record, and performance of the effective condition remains to be checked. Authorization for pickup in this group also requires a corresponding scope basis and cannot be derived from signing or effectiveness alone. Record each missing item separately.
- Input the retrieved commission table fragment and the draft sentence "This leader is entitled to view all commissions for this group." Compare whether the retrieval result contains a permission field. If the fragment contains only commission ratios and settlement cycles and no permission field, the draft sentence is judged unverified and requires a separate permission system check. Do not claim that retrieval itself grants authorization.
- Input the vector model identifiers of the pickup list library and the leader agreement library. Compare whether the two identifiers match. If they do not match, exclude the non-matching one, do not run cross-model retrieval, and record the actual observation. An empty result can be checked downstream using an explicit paper input of [] to confirm the downstream design, without claiming that a fictional rule's retrieval always returns an empty array.
These checks state expected conditions rather than completed tests. A pending request is not approval, an offered option is not execution, and an unsigned termination is not a completed termination.