What the documents look like
For product questions in Letter-of-credit documents, this page examines FastGPT hybrid retrieval. Compare the material received with the sources used in the answer. The examples are hypothetical.
Constraints at the hybrid retrieval step
The proposed commercial-invoice example says the product description differs qualitatively from an order requirement, without supplying that order’s wording. First inspect whether the order was actually provided and what the invoice candidates support. Retain the product-nature comparison in the full question. A statement that a difference exists cannot replace the originals from both sides, and ranking cannot itself produce a documentary-review conclusion. Until the original order is obtained, the answer should state that only the invoice and the sample description can be read. A missing comparison source and an unsatisfactory candidate order are different problems. Raising the invoice passage’s rank cannot resolve the first one.
Configuration and implementation checks
| Item or implementation check | Input or applicable condition | Traceable check |
|---|---|---|
| Fused candidates | Invoice description and the original order still needed | A difference summary does not replace both source statements. |
| Complete question | Industrial-grade and consumer-grade sensor qualifications | Do not reduce both grades to an unqualified sensor. |
| Minimum relevance | Inspect filtering under semantic or reranking conditions | Check recall scope if only one side remains. |
| Citation token limit | Product nature and corresponding document identity | Inspect any trimmed grade qualification. |
| Missing-source retest | No detailed technical specifications | Name or grade does not establish model-requirement evidence. |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
Hybrid retrieval | Vector and full-text results use RRF fusion. |
| Reranking | Reranking reorders candidates using the full question. |
Minimum relevance | Filtering applies to semantic search or reranking. |
Citation limit | Citation content is limited by tokens. |
Question optimization | Compare subjects and restrictions after optimization. |
Three common errors
“Industrial-grade sensor” and “consumer-grade sensor” come from the invoice and letter-of-credit attachments respectively. Reranking should not reduce both descriptions to an unqualified “sensor.” Inspect both candidate passages for their product-nature conditions and compare the completeness of final citations. If only one side is recalled, investigate source scope and retrieval first. Where both exist, examine order and token trimming for a mistaken claim that the documents agree.
How to verify the setup
Model and quantity information need not contain technical specifications. Without those parameters, similar product names or grade terms cannot establish conformity with a detailed requirement. After obtaining the corresponding technical material, repeat the question and trace its path into candidates and citations. Check minimum relevance under semantic or reranking conditions, and compare question optimization for retained subject and restrictions. This test does not replace professional verification of applicable document versions and documentary requirements.
| Material context | Scope |
|---|---|
| Document types | Letter of Credit, Commercial Invoice, Bill of Lading, Insurance Policy and Bill of Exchange |
| Candidate sources | Issuing Bank Notice, UCP600 Clauses, Freight Forwarder Bill of Lading and Bank Document Submission Records |
| Technical baseline | FastGPT v4.17.2 |