What the documents look like
Channel receivables collection happens after credit sales. The core work is credit exposure and cash recovery, not matchmaking between buyers and sellers. The daily materials are the receivables aging table, dunning letters, credit agreements, and shipment freeze notices. Order fulfillment and after-sales question handling require quoting these materials when a distributor asks about outstanding balances, shipments, or payment terms. Citation tracing means every answer must point back to the specific source text.
Data comes from the finance system, bank receipts, distributor orders, and sales contracts. Update frequency is weekly or monthly. Version differences between documents directly determine whether a citation holds. A credit agreement that expired, an aging table from a prior period, or a freeze notice that was never issued can each make a quoted sentence unsupported. The retrieval step does not decide authorization. A search result is not a shipment release, a credit adjustment, or a collection conclusion. A similarity score does not perform permission filtering or entity isolation.
Constraints at the citation tracing step
The knowledge base search node can associate one or more knowledge bases that share the same vector model. Output is an array of references, and its length can be zero. An empty array does not mean the workflow has failed or stopped. The reference limit uses tokens as its unit. The minimum relevance setting takes effect only during semantic retrieval or result reranking. RRF is a fusion process for mixed results, not an on-off switch. Image retrieval requires image input and corresponding model capability. Reranking requires a model and must be enabled by configuration. Optional paths shown in a flow diagram do not mean every request executes all of them.
Hypothetical drill sample: A distributor credit agreement states a credit limit of 800,000 yuan, payment terms of 60 days, signing date 1 March 2024, valid until 28 February 2025. As of 31 December 2024, the receivables aging table shows the distributor overdue by 120,000 yuan, of which 70,000 yuan is aged 31 to 60 days. A draft sentence under review reads: "This distributor has exceeded the credit limit and shipment should be frozen." This sentence lacks the same-period order encumbrance figure and the shipment freeze notice text. The judgment is unsupported.
Configuration and implementation checks
The table below centers on the sample above. It lists only settings or implementation checks already described in the official documents. The values are hypothetical starting points for this drill, used for comparison against actual aging table fields. They are not official defaults.
| Configuration or check item | Hypothetical starting point | Reason and comparison object |
|---|---|---|
| Associated knowledge bases | Receivables aging table base and credit agreement base | Both must share the same vector model to be associated together; verify identifiers first |
| Search mode | Hybrid retrieval | Agreement numbers and amounts are exact queries; payment term descriptions are semantic queries |
| Reference limit | Hypothetically set to 2000 tokens | Compare against a single aging table record length to avoid truncating amount fields |
| Minimum relevance | Hypothetically set to 0.5 | Takes effect only during semantic retrieval or result reranking; compare against rerank score |
| Result reranking | Enabled | Second-pass ordering when candidate entries are numerous; compare against rerank score |
| Reference output handling | Check each answer against cited source text | Verify retrieval output is passed to the answer step; empty arrays still flow through |
Three common errors
- Treating a retrieval hit as an authorization conclusion. Input is the draft sentence above and the retrieved credit agreement. Compare the agreement validity period against the assertion in the draft sentence. The observable result is that the agreement does not state freeze conditions, so the sentence remains unsupported.
- Forcing knowledge bases with different vector models into joint association. Input is the model identifier of the aging table base and the model identifier of the credit agreement base. Compare whether the two identifiers match. The observable result is that a mismatched base is excluded, and cross-model retrieval is not executed to compare missed recalls.
- Using a similarity score to infer entity isolation. Input is the minimum relevance setting and one unrelated distributor record. Compare the score against the filter threshold. The observable result is that a low score means low relevance only, not that the record was excluded by permission.
How to verify the setup
- Input the source text above and the draft sentence. Compare the draft sentence assertion against the credit limit, payment terms, and validity period stated in the source. The observable result is that when the source does not state a credit limit breach, the sentence is judged unsupported, and the evidence gap is preserved.
- Input a clearly paper-based empty array
[]. Compare the search node output against the downstream answer step design. The observable result is that when the reference array length is zero, the downstream step still continues execution. Do not claim that a fabricated rule retrieval will necessarily return an empty array. - Input the current version and a historical version of the credit agreement. Compare the version to which the cited text belongs against the validity period field. The observable result is that when a historical version falls outside its validity period, that citation is pending verification. Whether to adopt it still depends on review against the valid version.