What the data for this category looks like
Data for duty-free intelligent due diligence reports comes primarily from customs offshore duty-free supervision ledgers, duty-free business entity registration documents, official announcements of offshore duty-free policies, and supplier qualification review materials. Data update cadence falls into two categories: policy content updates irregularly per regulatory requirements, while operational data syncs monthly or quarterly. Document structure includes three modules: policy clauses, operational compliance data, and qualification certificates. Core fields include policy effective date, business entity registration number, offshore traveler shopping limit, product category code. Limit fields use yuan as the unit. Date fields follow the YYYY-MM-DD format.
What constraints these characteristics impose on knowledge base retrieval and recall
Irregular updates to policy content require the knowledge base to use a high-frequency sync mechanism, to avoid recalling expired regulatory clauses. Multi-field attributes in operational data require retrieval to support precise field matching, to prevent semantic retrieval from mixing up registration information of different business entities. Long-text policy documents require a proper chunking strategy, to avoid breaking the logical connections between clauses. Unit and format requirements for structured fields need automatic field matching during retrieval, to prevent retrieval errors caused by mismatched limit units. Multi-format support for qualification documents requires adapting parsing rules for different document types such as PDF and scanned files.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large | Duty-free due diligence reports contain a large number of specialized policy terms and structured operational data. This model has stronger ability to understand long-text semantics and field associations. |
chunk_size | 800–1200 characters | Duty-free policy documents are mostly continuous long clauses. Too long chunking will lose contextual connections, while too short chunking will break logical links between policies. |
recall_top_k | Top 8–12 results | Due diligence reports need to cover multi-dimensional content including compliance, operations and qualifications. Too many recalled results will increase context processing load, while too few will miss key information. |
similarity_threshold | 0.72–0.78 | Duty-free related retrieval requires precise matching of policy clauses and operational data. A threshold that is too low will introduce irrelevant content, while a threshold that is too high will miss relevant entries. |
PARSE_FILE_MAX_SIZE | 200 MB | Duty-free due diligence reports may contain multi-page qualification scanned documents and policy compilation PDFs. This setting supports complete parsing of large files. |
parse_file_timeout_seconds | 300 seconds | Parsing large policy compilation PDFs takes a long time. The default timeout period is insufficient to complete parsing. This value adapts to most large file scenarios. |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After multiple consecutive conversations, the relevance of content recalled by the knowledge base decreases significantly. The same query can accurately recall relevant content after opening a new conversation. Cause: The context of multiple consecutive conversations is appended to the retrieval input, diluting the original query and reducing the accuracy of semantic matching.
- Phenomenon: After changing the
embedding_modelparameter, the accuracy of recall results from the existing knowledge base decreases, with no obvious error prompts. Cause: The vector index of the existing knowledge base was generated based on the old embedding model, and no vectors were regenerated to adapt to the vector space of the new model. - Phenomenon: When using PostgreSQL as the vector storage, the number of valid content recalled in a single query is insufficient, and duplicate recalls occur. Cause: PostgreSQL's vector index has limited approximate retrieval accuracy for high-dimensional vectors, and cannot adapt to the retrieval requirements of multi-field associations in duty-free due diligence reports.
How to confirm proper configuration
- Upload a single standard duty-free due diligence report sample, and check whether the extracted fields after parsing cover the three core types of content: policy clauses, operational data, and qualification documents.
- Enter a query containing a specific policy effective date or business entity registration number, and confirm that the recalled results contain the corresponding field information.
- Modify the
embedding_modelparameter, trigger a full reindex of the knowledge base, and check whether the index task status shows completed. - Initiate multiple consecutive queries, and verify that subsequent recalled content is not excessively disturbed by previous conversation contexts.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.