Letter-of-credit documents: citation tracing for product questions

Review citation tracing for product questions in Letter-of-credit documents. Check sources, input boundaries, error handling and repeatable test evidence.

What the documents look like

Letter-of-credit document examination and presentation serves bank payment conditions and centres on document consistency. This differs from export tax rebate filing, where the focus is tax matching. Each transaction involves a letter of credit, commercial invoice, bill of lading, insurance policy and draft. The main sources are issuing bank notices, UCP600 clauses, freight forwarder bills of lading and bank presentation records. Errors lead to refusal of payment, deductions or delayed foreign exchange receipt, so any cited source must trace back to a specific document and clause. The data update frequency is per transaction. This page does not cover batch synchronization mechanisms.

A product question in this category typically asks which document field, clause or presentation requirement applies, and the answer needs to point to the exact source text. The knowledge base search node retrieves candidate passages, and the answering step must then check each answer sentence against the retrieved original wording.

Constraints at the citation tracing step

When the knowledge base search node links knowledge bases, only knowledge bases using the same vector model can be selected together for vector search. The output of cited content is an array, and its length can be zero. When no content is found, the output path still runs through. Retrieved results are passed to the answering step, where each answer sentence is checked against the cited original text. The minimum relevance score is a filter threshold and takes effect only in semantic retrieval or when result reranking is used. It does not perform permission or entity isolation. Hybrid retrieval merges multiple result paths through RRF. Reranking requires a model and must be enabled according to settings. Image retrieval requires image input and model capability. Optional paths do not mean every request executes all of them.

Hypothetical drill sample: The source text reads "Letter of credit number LC-HYP-001, issuing bank A Bank, valid until 30 September 2026, requiring presentation of commercial invoice, bill of lading and insurance policy." A second sentence reads "The consignee field of the bill of lading shows to order." The draft sentence to be judged is "Under this letter of credit, the consignee field of the bill of lading has been completed in accordance with the letter of credit requirements." This draft sentence concerns whether the consignee field completion meets the letter of credit terms. The source text does not state the specific requirements of the letter of credit for the consignee. The draft sentence is unsupported.

Configuration and implementation checks

Configuration itemHypothetical starting point for this trialReason and comparison method
Linked knowledge basesSelect knowledge bases with the same vector modelOnly the same vector model allows joint linking; check model identifiers and exclude non-matching ones
Search modeSemantic retrievalSuited to natural language questions; compare with full-text retrieval for number-type queries
Minimum relevance score0.5Hypothetical starting point; compare with 0.3 and 0.7 for changes in cited item count after filtering
Citation limit2000 tokensHypothetical starting point; compare with 1000 and 4000 for completeness of cited content
Question optimizationEnabledSuited to multi-turn follow-up; compare with disabled state for retrieval hits
Result rerankingEnabledSuited when candidate results are numerous; compare with no reranking for ranking scores

Three common errors

  1. Treating the minimum relevance score as a permission filter. Input a cited item with a semantic retrieval score of 0.4, compare whether that item falls within the authorized knowledge base scope, and observe whether unrelated business records are wrongly excluded. Relevance does not perform entity isolation.
  2. Treating an empty citation array as a workflow error. Input a paper output of [], compare whether the downstream design continues execution, and observe whether the flow is wrongly interrupted. An empty array length of zero is allowed, and the output path still runs through.
  3. Treating RRF as an on/off switch. Input a hybrid retrieval configuration, compare whether an RRF switch field exists, and observe whether the fusion process is mistaken for a configurable item. RRF is a multi-path result fusion process.

How to verify the setup

  1. Input the hypothetical source text "Letter of credit number LC-HYP-001, issuing bank A Bank, valid until 30 September 2026", and compare whether the draft sentence "Under this letter of credit, the consignee field of the bill of lading has been completed in accordance with the letter of credit requirements" obtains support from the source text. The source text does not state consignee requirements, so the draft sentence is unsupported, and the evidence gap must be recorded.
  2. Input a paper output of [], and compare whether the downstream node continues execution. When the citation array length is zero, the output path still runs through. If the downstream design interrupts, it does not match the documented description.
  3. Input two knowledge bases with different vector model identifiers, and compare whether joint linking is allowed. Only the same vector model allows joint linking. Exclude non-matching identifiers and record the actual observation. Do not execute cross-model retrieval to compare missed recalls.