What the documents look like
Channel receivables collection happens after credit sales. The work centres on credit exposure and cash recovery rather than on matching buyers with sellers. The main source materials come from finance systems, bank receipts, distributor orders and sales contracts. In day-to-day use, the relevant documents are the receivables aging table, the dunning letter, the credit agreement and the shipment freeze notice.
For store service and internal training, staff retrieve four things from these materials: the aging basis, the wording of a dunning letter, the current status of a credit limit, and the conditions stated in a freeze notice. Retrieval results shape what staff say in collection conversations and what trainers present as the working position. Source materials update weekly or monthly. That update rhythm does not change the retrieval configuration described here, and this page does not cover any synchronization mechanism.
The four document types differ in structure. An aging table carries numbers and periods. A dunning letter carries wording and named parties. A credit agreement carries an amount and a validity period. A freeze notice carries triggering conditions. Identifiers, amounts, dates and party names suit full-text or hybrid retrieval. Natural-language descriptions of collection strategy suit semantic retrieval. These differences matter because a single search mode does not serve all four document types equally well.
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 built on different vector models cannot be associated together. Before configuring the node, check the vector model identifier of each candidate base and exclude any that does not match. Record what was actually observed. This page does not describe what happens if mismatched bases are forced together.
The minimum relevance setting takes effect only under semantic retrieval or when result reranking is used. It is a filtering threshold. It does not perform permission filtering and does not isolate entities. A record that is semantically close but belongs to a different distributor is not excluded by this threshold.
The citation limit is measured in tokens and controls the length of content cited per search. Problem optimization performs coreference resolution and question expansion, and it adds an extra model call before retrieval. RRF is the fusion step applied to results from multiple retrieval paths after hybrid retrieval. Reranking requires a rerank model and must be enabled in the settings. Image retrieval requires an image input and the corresponding model capability. These optional paths in the overall retrieval flow do not run on every request.
Hypothetical paper exercise sample: A brand finance team holds a paper excerpt of a credit agreement. The excerpt reads: "Distributor A, credit limit RMB 500,000, valid until 31 March 2025; as of 31 March 2025, the receivables aging table shows Distributor A overdue amount of RMB 120,000." A draft sentence is submitted for review: "Distributor A's credit has lapsed and shipments should be stopped." The review requires the same party, the same period and the same field. The source excerpt states a validity end date of 31 March 2025. It does not state what happens after that date. The phrase "shipments should be stopped" in the draft sentence is not supported by the excerpt and remains pending verification.
Configuration and implementation checks
The settings below relate to the sample above. The values are starting points for this hypothetical exercise, not official defaults.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the credit agreement base and the aging table base that share one vector model | Check the vector model identifier first, exclude bases that do not match, and do not run a cross-model retrieval comparison |
| Search mode | Use full-text retrieval for "valid until 31 March 2025"; use semantic retrieval for collection strategy questions | Compare recall between identifier-and-date questions and natural-language questions |
| Citation limit | Set a token amount sufficient to cover one credit agreement excerpt | Test with source materials of different lengths to see whether content stays complete, then adjust |
| Minimum relevance | Start with a lower threshold to observe recall, then raise it gradually | Label relevant and irrelevant samples first, then compare filtered results |
| Problem optimization | Enable for follow-up questions containing references; disable for complete questions | Compare the original question with the optimized question and check whether the party name was altered |
| Result reranking | Enable the rerank model when candidates are numerous | Compare ordering before and after reranking, and confirm the model is configured |
Three common errors
- Treating a retrieval hit as authorization. The input is the draft sentence "Distributor A's credit has lapsed and shipments should be stopped." The comparison object is the credit agreement excerpt. The excerpt states only a validity end date and does not state any post-expiry handling. Treating the retrieval hit as grounds to stop shipments exceeds the authorization boundary. The correct action is to record the evidence gap and wait for human verification.
- Associating knowledge bases that use different vector models. The input is two bases with different vector model identifiers. The comparison object is the node's association requirement. If they are associated anyway, do not invent missed recalls or redundant results. Check the identifiers, exclude the non-matching base, and record what was observed.
- Treating minimum relevance as a permission filter. The input is a semantically similar record belonging to a different distributor. The comparison object is the minimum relevance setting. This threshold does not isolate entities. Concluding that unrelated parties have been excluded on this basis is a misreading.
How to verify the setup
- Input the hypothetical sample excerpt and the draft sentence. Compare the phrase "shipments should be stopped" in the draft sentence with the validity field stated in the excerpt. The excerpt does not state post-expiry handling, so the result is pending verification. It is not a negation and not a conflict.
- Input two knowledge bases with different vector model identifiers. Compare them against the node's association requirement. After checking the identifiers, exclude the non-matching base, do not run a cross-model retrieval, and record what was observed.
- Input an empty-result request using an explicit paper input of an empty array, and check the downstream design. Confirm the output path can still complete. Do not claim that a fictional rule search necessarily returns an empty array.
These verification steps are paper expectations and have not been executed. Retrieval and similarity scores do not carry authorization, approval or database write functions. Related business actions require separate system implementation.