What the documents look like
Channel receivables collection happens after credit sales. The work centers on credit limits and cash recovery, and the documents come from finance systems, bank receipts, distributor orders and sales contracts. Day-to-day material includes accounts receivable aging tables, dunning letters, credit agreements and shipping freeze notices. These materials are typically refreshed weekly or monthly, so the aging position, remaining credit balance and freeze status held in a knowledge base can lag behind current reality.
For product information and selection questions, the knowledge base answers questions such as which model may ship under which credit condition, and how an overdue position is handled. A retrieval hit returns the original wording of a historical document. It does not confirm the current receivable state, and it does not authorize a shipment decision.
Constraints at the vector indexes step
Knowledge bases that are linked for retrieval must use the same vector model. The practical first step is to check the actual model and its supported input types, rather than assume a shared label. One source data block can be associated with multiple indexes, so the index content and the original text are checked separately. Index content should be complete and focused; retrieval coverage and index length are weighed against the sample material at hand.
Image retrieval depends on what was built at ingestion time. Whether an image description or an image vector exists in the knowledge base is a separate question from what the query side sends. Semantic recall and relevance scores do not perform permission or entity isolation. The requirement that linked knowledge bases share one vector model does not extend to business-registration sites or other external systems.
RRF is a fusion step for combining results from multiple recall paths. It is not an independent switch. The optional paths shown in the overall flow diagram do not all run on every request. Image retrieval requires a corresponding image input and model capability. Reranking requires a rerank model and must be enabled in settings. Knowledge bases built on different vector models cannot be linked together; check the identifiers, exclude those that do not match, and record what is actually observed.
Configuration and implementation checks
The following is built around a clearly labelled hypothetical drill sample. Hypothetical source text: a brand signs a credit agreement with Distributor A setting a credit limit of one million yuan and a payment term of sixty days. An aging table as of a stated month shows Distributor A with an overdue amount of two hundred thousand yuan. A shipping freeze notice states that shipping is suspended for the portion exceeding the credit limit. Draft sentence to be assessed: "All shipments for Distributor A are currently stopped." This sentence does not match the source text, which freezes only the portion above the credit limit.
| Configuration item | Setting or check relevant to this page | Relation to the sample |
|---|---|---|
| Linked knowledge bases | Select bases that use the same vector model; check the model identifier and input type first | The sample materials must sit in one model's bases before they can be linked together |
| Index content | Build indexes from complete and focused information; check index and original text separately | The credit agreement and the freeze notice are indexed separately so they can be compared |
| Search mode | Choose semantic, full-text or hybrid search according to the question | Model numbers, amounts and dates are better served by full-text or hybrid search |
| Result reranking | Performs a second ordering when a model is available and the setting is enabled | Used to improve text relevance when there are many candidates |
| Reference limit | Each search cites at most n tokens of content | Hypothetical trial starting value 2000 tokens, chosen because the sample passages are short; compare with 1000 tokens to observe whether key clauses are truncated |
| Minimum relevance | A 0-1 value, effective only under semantic search or result reranking | Hypothetical trial starting value 0.4; compare with 0.2 to observe whether low-relevance fragments are filtered out |
Three common errors
- Treating a retrieval hit as an authorization confirmation. Input: the draft sentence "All shipments for Distributor A are currently stopped." Comparison: this sentence against the freeze notice wording "shipping is suspended for the portion exceeding the credit limit." Observable result: the two scopes do not match, so the retrieval result does not support "all shipments stopped," and the draft is unsupported.
- Linking knowledge bases that use different vector models. Input: two knowledge base identifiers, one using model A and the other using model B. Comparison: whether the model identifiers match. Observable result: different identifiers are excluded, and no cross-model retrieval is run to compare missed recalls.
- Treating a pending review request as already approved. Input: a credit adjustment request marked "pending review." Comparison: whether the request states an approval status and an effective date. Observable result: no approval is stated, so the item remains unverified and cannot be used to conclude that the credit limit has changed.
How to verify the setup
- Input: the draft sentence "All shipments for Distributor A are currently stopped" together with the freeze notice text. Comparison: whether the subject, object and status scope match. Observable result: the source freezes only the portion above the credit limit, so the draft exceeds that scope and is judged unsupported.
- Input: the model identifiers of two knowledge bases. Comparison: whether the identifiers are the same. Observable result: only matching identifiers may be linked; different identifiers are excluded and the observation recorded, without inferring a runtime outcome.
- Input: a paper empty result array [] together with the downstream citation design. Comparison: whether the downstream path continues with zero citations. Observable result: the output path can continue; no claim is made that a hypothetical rule search must return an empty array. After any model or index change, compare cited original text using the same set of questions, and do not promise an effect based on vector scores alone.