What the documents look like
In B2B platform matching, the operating parties are platform operations, suppliers, and enterprise buyers. The working documents are enterprise verification materials, inquiry and quotation sheets, electronic contracts, and platform settlement statements. Their sources include the B2B platform itself, the National Enterprise Credit Information Publicity System, payment records, and logistics traces. In cross-border and multilingual retrieval, one inquiry or quotation sheet may exist in several language versions, so citation tracing must let an answer point back to a specific document version.
Quotation sheets and electronic contracts sit at different stages. A quotation has a validity period; a contract has signing and effectiveness stages. Citation tracing has to distinguish the status written in the source material. A pending review cannot be treated as an approval, and a quotation validity period cannot be treated as a supply period. The brief gives a daily and per-order update frequency, but this page does not depend on batch synchronization or scheduled triggers; it checks one material version at a time.
Constraints at the citation tracing step
The knowledge base search node can associate one or more knowledge bases that share the same vector model, and it outputs citations as an array whose length may be zero. Retrieval does not carry authorization duties. Semantic recall and relevance scores reflect content similarity only; they are not a basis for judging enterprise identity, permission scope, or entity separation. Knowledge bases built on different vector models cannot be associated together, so the vector model identifier should be checked first and non-matching bases excluded.
Citation tracing compares each answer statement against the cited source text, confirming that subject, object, period, and field agree. In multilingual retrieval, semantic search supports cross-language understanding, but the displayed citation still follows the text actually recalled. Fluent translation does not replace source evidence.
Configuration and implementation checks
The following settings describe a hypothetical paper exercise. The numeric starting points are chosen for this exercise to compare against the sample source text; they are not official default values.
| Configuration item | Hypothetical starting point for this exercise | Comparison method |
|---|---|---|
| Associated knowledge bases | Associate only knowledge bases with the same vector model | Check the vector model identifier and exclude bases on a different model |
| Search mode | Hybrid search | Observe whether full-text recall hits a numbered field in the sample |
| Minimum relevance | Hypothetical starting point 0.4 | Compare against the sample's relevance score and observe whether it is filtered |
| Citation limit | Hypothetical starting point 2000 tokens | Compare against the sample material length and observe whether citations are truncated |
| Question optimization | Enabled | Observe the completed retrieval terms for a short multilingual question |
| Result reranking | Enabled | Observe rerank scores and ordering across candidate results |
The citation limit is measured in tokens and is unrelated to thinking budget. Result reranking requires a rerank model and must be enabled by configuration. Image retrieval requires image input and matching model capability. These paths do not necessarily run on every request.
Three common errors
- Treating a semantic relevance score as a permission filter. Input: an enterprise buyer query. Comparison: the recalled content against the querying subject. Observable result: a relevance score. The score does not judge enterprise identity or authorization scope, so using a similarity setting as an entity separation control is the error.
- Writing a pending review as approved. Input: a verification material marked pending review in the hypothetical sample. Comparison: the status word in the source against the drafted sentence. Observable result: whether the two agree. Pending review does not demonstrate approval, so supplying an authorization conclusion is the error.
- Treating a quotation validity period as a supply period. Input: the validity field in the sample quotation sheet. Comparison: that field against the period wording in the drafted sentence. Observable result: whether the field meaning was replaced. Quotation validity and supply period are different fields, so mixing them is the error.
How to verify the setup
Hypothetical paper exercise: a supplier submits enterprise verification material. The source text reads: "Subject name: Hongyuan Machinery Parts Co., Ltd.; verification status is pending review; submission date: March 1, 2024." A separate quotation sheet reads: "Quotation valid until April 15, 2024; supply period: to be agreed separately." The sentence to be judged is: "This supplier's verification has passed, and supply can be arranged at any time within the quotation validity period."
- Verification one: Input the verification source text above and the drafted sentence. Compare the source "pending review" against the drafted "has passed." The observable result is that the two disagree, so the drafted sentence is unsupported and the evidence gap is recorded.
- Verification two: Input the quotation source text above and the drafted sentence. Compare the source "supply period: to be agreed separately" against the drafted "supply can be arranged at any time." The observable result is that the drafted sentence adds a period conclusion not present in the source, so it is marked as needing confirmation.
- Verification three: Input a clearly written paper empty result array []. Compare whether the downstream answering step continues to execute with zero citations. The observable result is that the downstream step does not stop because of the empty array, so the empty-result handling design holds. This verification does not claim that a fictional rule search necessarily returns an empty array.
Retrieval checks should observe the content actually recalled and the cited source text. Failure branches include no hit, a score below the starting point being filtered, and citations being truncated by the limit. Knowledge bases on different vector models cannot be associated together; after checking identifiers, exclude non-matching bases rather than running a cross-model retrieval to compare missed recalls.