What the documents look like
Member private-domain operations handle materials built around customer data and repeat-purchase outreach: member tag tables, outreach plans, coupon rules, and privacy authorization records. In cross-border work, one material commonly carries several language versions, and semantic retrieval can match across languages, for example a Chinese question reaching an English knowledge point. The brief gives daily or weekly update frequency, with member tag tables and outreach plans changing more often. These traits shape chunking: each segment should keep conditions and their constraints together, so an authorization scope is not separated from the outreach action it governs. The materials come from CRM systems, enterprise messaging, POS consumption records, and mini-program behavior data, and the consequences of error include outreach complaints, uncontrolled coupon cost, and member loss.
Constraints at the document chunking step
One group of data can map to multiple vector indexes, and a hit on any one index recalls that group of original data, so chunk quality directly affects recall precision. Segmentation should keep semantics complete and single; a condition, its explanation, and the content it constrains should not be split apart. Full-text retrieval suits exact queries such as numbers, models, and proper nouns, semantic retrieval suits natural-language phrasing, and hybrid retrieval merges the two paths through RRF. Retrieval is a recall process and does not perform authorization decisions; the status inside a privacy authorization record must be checked against the source text, and a retrieval hit does not mean outreach authorization exists.
Hypothetical paper exercise sample: the source text reads "Member tag table, version A, applicable period January to June 2024, tag 'high-frequency repurchase' defined as three or more purchases within the last 90 days. Outreach plan draft sends coupons to members holding this tag, authorization status column marked 'pending review'." The draft sentence to be judged reads "This outreach plan has obtained authorization and may send coupons to high-frequency repurchase members." The check compares the authorization status field value with the draft sentence wording: the source states "pending review," the draft states "has obtained authorization," and the same subject, same period, same field carry mutually exclusive values, so the sentence is unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | Materials contain numbers and natural-language questions; compare candidates from the semantic and full-text paths |
| Minimum relevance | 0.5 | Hypothetical starting point; compare filtered candidate count and relevance against 0.3 and 0.7 |
| Reference limit | 3000 tokens | Hypothetical starting point; compare reference completeness against 2000 and 4000 |
| Question optimization | Enabled | Multi-turn follow-up needs reference resolution; compare retrieval hits before and after enabling |
| Result reranking | ReRank enabled | Secondary ordering when candidates are many; compare rerank scores against vector scores |
| Chunking method | Segment by material structure | Keep conditions and constraints intact; compare split segments against complete segments |
These values are hypothetical trial starting points for this page and are not official defaults. Image retrieval requires image input and matching model capability, and reranking requires a model enabled according to settings. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used, and it acts as a filter threshold rather than a final ordering rule.
Three common errors
- Treating a retrieval hit as approved authorization. Input is a privacy authorization record entry marked "pending review"; compare the retrieval result with the authorization status field. If the recalled content is used directly for outreach, retrieval and authorization execution have been conflated.
- Treating a successful upload as an updated index. Input is a changed material file; compare the upload state with the training and index state. If retrieval is assumed updated without checking the actual index state, the post-change recheck is missed.
- Treating minimum relevance as a permission or entity filter. Input is a retrieval request spanning business lines; compare the relevance score with record ownership. If raising relevance is assumed to exclude unauthorized records, the parameter's role is misjudged.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence; compare the authorization status field value "pending review" with the draft wording "has obtained authorization." If the sentence is judged unsupported, the check passes; if it is judged authorized, record a judgment error.
- Input a term, a number, and a natural-language question as separate retrievals; compare recalled content with the source segmentation. If conditions and constraints remain attached and can be traced back to the source, chunking meets the requirement; if a condition is separated from the content it constrains, record a segmentation defect.
- Input a changed material and check the training and index state; compare the upload state with the actual index state. If the two disagree and the case is recorded as pending verification, the recheck is complete; if upload success alone is taken as retrieval updated, record a missed-check risk.
An empty result can use the explicit paper input [] to check downstream design, and this does not mean a fictional rule retrieval necessarily returns empty. Libraries built on different vector models cannot be associated together; verify the identifier first, exclude non-matching ones, and record the actual observation.