What the documents look like
Commission settlement and commission deduction happen after a transaction closes. The reconciliation focus falls on valid promotion, after-sales deduction, and commission reversal following refunds. Source material comes from the Selected Alliance backend, order after-sales data, the finance system, and creator contracts. The documents handled day to day include commission setting records, settlement statements, refund deduction details, and invoices.
In cross-border work these materials may be written in different languages. The same settlement can differ across language versions in field order, currency notation, and fee names. Parsing and chunking need to preserve a document identity and version that can point back to the source text, so later reconciliation can trace a figure to the document it came from. Settlement and deduction cycles here run weekly and monthly, which affects how often the reference material changes rather than how any single chunk is built.
Constraints at the document chunking step
Once multilingual material enters the knowledge base, whether a segment stays complete directly affects retrieval quality. When a condition, an explanation, and the content it constrains are split apart, a retrieved fragment may carry only an amount while missing the settlement period or the deduction reason it belongs to.
Original data and retrieval indexes need to be kept distinct. One group of original data can map to multiple indexes. Multi-vector mapping lets a long text map to several vectors, and recall of any one vector recalls that group of data. Semantic retrieval supports cross-language understanding, so a Chinese question can match an English knowledge point, but the similarity score is a mathematical distance. It does not perform permission or entity isolation. Full-text retrieval suits exact queries for numbers, models, and proper nouns. Hybrid retrieval merges multiple result paths through RRF. Reranking requires a model and must be enabled in settings. Image retrieval requires corresponding image input and model capability. These paths do not all run on every request.
Hypothetical paper test sample: a refund deduction detail reads, "Settlement period March 2025, creator ID HYP-0417, order HYP-ORDER-2201 was refunded, deduction amount 120 USD, deduction reason: after-sales refund." The draft sentence to be judged is, "Creator HYP-0417 completed settlement in March 2025, and the 120 USD deduction has been posted." The check target is whether this draft sentence matches the source text, and whether the deduction has actually been posted.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this test | Reason and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains both an identifier and natural language; compare recall against semantic retrieval alone |
| Reference limit | Hypothetical starting value 2000 tokens | The documentation controls reference volume in tokens; compare against sample segment length |
| Minimum relevance | Hypothetical starting value 0.5 | The documentation states this value takes effect under semantic retrieval or reranking; observe remaining segments after filtering |
| Question optimization | Enabled | The draft sentence contains a reference; compare retrieval terms before and after optimization |
| Result reranking | Enabled per settings | Reranking requires a model; compare rerank score against vector score |
| Training status check | Manual review | After material changes, check actual training and index status; treat upload success and retrieval freshness as separate facts |
Three common errors
- Treating "completed settlement, 120 USD deduction posted" in the draft sentence as a fact the source text supports. The source text states only that a refund occurred and records a deduction amount. It does not state that settlement was completed or that the amount was posted. This is unsupported.
- Treating a successful upload as proof that retrieval has updated. The documentation calls for checking actual training and index status. Upload success and training completion are different facts.
- Using a similarity score to exclude unrelated business or unauthorized records. Relevance is a filtering threshold, not a permission or entity filter. Retrieval itself does not constitute authorization.
How to verify the setup
- Input the sample source text and the draft sentence, then compare whether the draft sentence contains "completed settlement" or "posted" when the source text does not. If either appears, judge the sentence unsupported and record the evidence gap. Do not claim the test has been run.
- Search the term "refund deduction" and the identifier "HYP-ORDER-2201" separately. Observe whether the segment retains the settlement period, deduction reason, and amount together. If a condition and the content it constrains are split apart, adjust the chunking. Do not assert that a specific query is guaranteed to return a first hit or full recall.
- After a material change, compare upload status against actual training and index status. If the two disagree, record the gap. Use a clearly paper-based empty input to check downstream design; do not claim that a fictional rule search returns an empty result.
Across different vector models, libraries cannot be jointly associated. Check identifiers first and exclude non-matching entries. Do not run cross-model retrieval to compare missed recall. The document flow chart lists optional paths; this does not mean every request executes all of them.