What the documents look like
Certificate of origin work serves import-country tariff treatment and customs clearance. The materials are produced on a per-shipment rhythm: the certificate of origin application, the commercial invoice, the packing list, and production cost records. These papers commonly carry product names, specifications, and origin-composition descriptions in a Chinese-and-foreign-language pairing, so retrieval has to handle Chinese questions and English entries within the same search.
A difference from letter of credit document examination is that certificate of origin work serves import-country clearance and is not necessarily bound by a letter of credit. The retrieval scope should therefore stay inside the application materials themselves. Letter of credit terms do not become a default basis for this task.
A hypothetical paper exercise sample is used throughout this page. The source text for comparison reads: "Exporter: a machinery company in East China; Commodity: hydraulic pump; Origin composition: domestic share 60%; Application date: March 2025; Destination country: Thailand." The draft sentence awaiting judgment reads: "This batch of hydraulic pumps can enjoy Thailand tariff preference." The source text states a composition share and a destination country. It does not state the corresponding tariff heading or the preference clause. The draft sentence is unsupported by the supplied wording, and the evidence gap should be recorded.
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 with different vector models cannot be associated together. The vector model identifier of each library should be checked first, and any library whose identifier does not match should be excluded. A cross-model search should not be run as a way to compare missed recalls.
Semantic retrieval works through vector distance and supports cross-language understanding, which suits a Chinese question matching an English knowledge point. Full-text retrieval depends on keyword hits and suits precise lookups such as numbers, models, and proper nouns. Hybrid retrieval uses vector retrieval and full-text retrieval together and merges the two result sets through the RRF formula. RRF is a fusion process, not a separate switch that can be turned on. Because hybrid retrieval produces a wide scope and cannot be filtered directly by similarity, a rerank model is usually needed to reorder results and to filter by the rerank score.
The minimum relevance value is a 0-1 number that filters out low-relevance results. It takes effect only under semantic retrieval or when result reranking is used. It is a filtering threshold, not a final ordering rule. The reference cap is expressed in tokens per search, not as a top k count. Question optimization performs coreference resolution and question expansion before retrieval and adds one model call, which usually improves multi-turn retrieval but also adds latency. Retrieval itself does not carry authorization. A retrieval hit does not mean the business action is permitted or has been approved.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only libraries holding certificate of origin applications, invoices, packing lists, and cost records whose vector model identifiers match | Check the vector model identifier of each library and exclude any that do not match |
| Search mode | Start with hybrid retrieval | Compare recalled content for the same test question against semantic retrieval alone and full-text retrieval alone |
| Reference cap | Hypothetical starting value 2000 tokens | Compare whether reference content stays complete across short and long materials, then adjust |
| Minimum relevance | Hypothetical starting value 0.4 | Label relevant and irrelevant samples first, then compare hit changes after filtering |
| Question optimization | Enable for multi-turn follow-ups or short questions | Compare the original question with the optimized question and check whether the subject was altered |
| Result rerank | Enable when candidates are numerous and a rerank model is available | Compare ordering and scores before and after reranking, and confirm the relevance filtering effect |
Three common errors
- Associating knowledge bases that use different vector models. Input two libraries whose vector model identifiers differ, compare the identifier fields, and observe whether the non-matching library is excluded. Associating them without checking identifiers is a configuration error.
- Treating minimum relevance as a permission or entity filter. Input a threshold of 0.4 under semantic retrieval, compare returned content before and after filtering, and observe whether unrelated business records can still enter the results. This parameter filters low relevance only.
- Treating a retrieval hit as authorization or approval. Input the draft sentence above, compare whether the source text states the corresponding preference clause, and observe whether a "can enjoy preference" conclusion appears. When the clause is not stated, the result should be recorded as unsupported.
How to verify the setup
- Input the hypothetical source text and the draft sentence above. Compare the subject, commodity, composition share, and destination country in the source text against the conclusion in the draft sentence, and observe whether the draft sentence goes beyond the source scope. A conclusion beyond that scope is unsupported.
- Input a Chinese question that should retrieve an English entry. Compare the returned content from semantic retrieval and from hybrid retrieval, and observe whether the corresponding English knowledge point is hit. If it is not hit, record the gap and do not infer a system error cause.
- Input a paper query with no corresponding material. Compare whether the returned reference array length is 0, and observe whether the downstream chain still runs. The empty-result handoff can be checked against a clearly stated paper input of [] for downstream design. Do not claim that a fictional rule query necessarily returns [].