What the data for this category looks like
Data sources for duty-free intelligent due diligence reports include customs off-shore duty-free supervision ledgers, qualification filing documents of duty-free business entities, official documents of off-shore duty-free policies, and monthly operation statistical reports. Update cycles fall into two categories: operation ledgers are updated daily, while policy documents are updated quarterly or upon special adjustments. The document structure includes structured fields and unstructured text. Structured fields include unified social credit codes of business entities, monthly sales volume (unit: ten thousand yuan), number of off-shore passenger receptions, and compliance inspection numbers. Unstructured content includes policy explanations, compliance rectification descriptions, and the like. The length of individual documents varies widely, ranging from hundreds of words of qualification descriptions to tens of thousands of words of annual compliance reports.
What constraints do these characteristics impose on the "vector models and indexing" link
The multi-source data characteristics of duty-free due diligence reports impose multiple constraints on the vector model and indexing link. First, the data includes structured operation ledgers and unstructured policy documents, which require simultaneous support for structured field vector generation and semantic indexing of non-segmented text, raising requirements for mixed indexing compatibility. Second, the update cycles differ significantly: real-time updated operation data and periodically updated policy documents need to trigger index updates in batches to avoid performance pressure from full index reconstruction. Third, the document length varies widely, with short fields and long texts coexisting, requiring flexible text segmentation strategies to avoid retrieval deviations caused by improper semantic cutting. In addition, some data includes qualification certificate images, which require additional support for image vector indexing and updating, further increasing the complexity of the indexing architecture.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed semantics of short fields and long policy documents in duty-free due diligence reports, balancing semantic integrity and retrieval efficiency |
vector_normalize | Enabled | Adapts to some embedding models that do not perform normalization, avoiding score deviations during vector generation for multiple fields of duty-free data |
retrieve_top_k | Top 8–12 results | Covers multi-dimensional retrieval requirements including compliance, sales volume, and passenger flow, avoiding excessive recall of irrelevant content |
similarity_threshold | 0.72–0.80 | Filters content with large semantic differences between operation and policy categories in duty-free data, improving retrieval accuracy |
index_update_interval | 1 hour (operation ledgers) + weekly (policy documents) | Matches the update cycles of the two data types, updating in batches to reduce resource consumption from index reconstruction |
max_context_token | 32000–50000 tokens | Accommodates the complete retrieval context of due diligence reports, adapting to query requirements for large-format documents |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After switching the knowledge base vector model, the page shows no progress, and the model selection pop-up cannot be opened again. Cause: The metadata association of the old index was not cleared first, and switching the model directly caused index configuration conflicts.
- Phenomenon: The number of tokens passed in a single knowledge base query exceeds the system limit, triggering a timeout error. Cause: The total number of tokens in the retrieval context was not limited, and all retrieval results were passed in at once, resulting in excessive data pushed in a single request.
- Phenomenon: Indexed qualification documents in image format cannot be retrieved in search results after update. Cause: The
image_vector_enableparameter was not enabled, or the update rules for image vector indexing were not configured in version 4.8.23 or higher.
How to confirm the configuration is complete
- Enter the knowledge base management page, check the vector model configuration module, and confirm that the settings of each parameter match the preset configuration.
- Initiate a simulated retrieval, check whether the number of recalled results matches the
retrieve_top_ksetting, and whether the similarity score falls within the preset range. - Upload a new duty-free operation ledger document, check whether the index update progress bar progresses normally, and confirm that the newly uploaded content can be retrieved after completion.
- Try switching the vector model, confirm that the model selection pop-up can be opened normally and the switch can be completed without progress stagnation.
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.