What the documents look like
Channel receivables collection begins after credit sales, so the working material concerns credit limits and cash recovery rather than transaction matching. The records come from finance systems, bank receipts, dealer orders and sales contracts. The documents handled day to day include the receivables aging table, dunning letters, credit agreements and shipment freeze notices, with updates at a weekly or monthly cadence. Product and selection questions in this setting ask about model numbers, specifications, payment-term clauses and freeze conditions, so retrieval quality determines whether an answer can cite the correct clause. The same dealer's aging, credit and freeze status must be aligned by entity and period, and the retrieval step only returns candidate source text; it does not decide authorization status.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases with different vector models cannot be associated together, so the vector model identifier must be checked first and non-matching bases excluded. Semantic retrieval relies on vector distance, full-text retrieval suits numbers, model names and proper nouns, hybrid retrieval merges both paths through RRF, and Rerank performs a second ordering of candidate text. The minimum relevance setting takes effect only under semantic retrieval or when result reranking is used; it is a filter threshold and not a means of permission or entity isolation. The citation limit is measured in tokens, so documents of uneven length need practical testing to see whether content is complete. Question optimization performs coreference resolution and question expansion and adds one model call. The output citation content is an array whose length can be 0, and the downstream path still runs when nothing is retrieved.
Hypothetical paper exercise: the source material reads "Dealer A, credit agreement number A-001, payment term 60 days, as of 31 March 2025 the receivables aging table shows 45 days overdue, and the shipment freeze notice has not yet been issued." The draft sentence to be assessed is "Dealer A has triggered a shipment freeze and supply may be stopped." That sentence treats an unissued freeze notice as an executed state, so the assessment is recorded as unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | How to check |
|---|---|---|
| Associated knowledge bases | Associate only the receivables collection bases whose vector model identifiers match | Compare vector model identifiers across bases and exclude non-matching ones |
| Search mode | Run semantic, full-text and hybrid retrieval on the same question separately | Compare which clauses each of the three result sets corresponds to |
| Citation limit | Hypothetical starting point of 3000 tokens, because aging tables and credit agreement passages are long | Compare against a 1500-token result to see whether content is truncated |
| Minimum relevance | Hypothetical starting point of 0.4, after labelling relevant and irrelevant samples | Compare against 0.2 and 0.6 for false recalls and missed recalls |
| Question optimization | Enabled for multi-turn follow-ups, disabled for a single clear question | Compare the original and optimized question to see whether the subject was altered |
| Result reranking | Enabled when candidates are numerous and a rerank model is configured | Compare ordering and scores before and after reranking |
These values are hypothetical starting points for this exercise, chosen because of passage length and sample labelling, and they are not official defaults.
Three common errors
- Treating a retrieval hit as an authorization conclusion. For the input "Dealer A freeze status," retrieval may return the credit agreement clause "freeze permitted when credit is exceeded." Outputting "already frozen" confuses a clause permitting a freeze with the fact that a freeze notice has been issued. Check: enter that question, compare whether the retrieved source text contains a freeze notice issuance record, and record the result as pending verification when only the clause appears.
- Associating knowledge bases that use different vector models. Entering the same question and selecting two bases with different vector model identifiers, then expecting merged results, goes beyond what the documents describe. Check: compare vector model identifiers across bases, exclude non-matching ones, and record the range that can actually be associated.
- Using minimum relevance to filter unrelated business. For the input "Dealer B aging," raising minimum relevance and expecting records of other dealers to be excluded misuses the setting. Check: compare whether retrieved content still contains other entities; relevance does not perform entity isolation.
How to verify the setup
- Enter the hypothetical source material and the draft sentence "Dealer A has triggered a shipment freeze and supply may be stopped," then compare whether the retrieved source text contains a freeze notice issuance record. When the source states only "not yet issued," the assessment should return unsupported; an "already frozen" output does not satisfy the condition.
- Enter "credit agreement number A-001 payment term" and run full-text retrieval and semantic retrieval separately, then compare whether the clause with that number is retrieved. When full-text retrieval matches the number and semantic retrieval does not, precise queries are better served by full-text or hybrid retrieval, and a first-position hit is not guaranteed.
- Enter a paper question with no corresponding material and check whether the output citation array has length 0 while the downstream path continues. Use a paper input of an empty array to check the downstream design; do not claim that a fictional rule retrieval necessarily returns an empty array.