What the Data for This Category Looks Like
The data for duty-free research reports mainly comes from brokerage retail trade track research report databases, public industry association monitoring reports, and customs port public statistical data. The update rhythm fluctuates with industry policy nodes, with intensive releases following major policy adjustments, and routine quarterly and semi-annual updates as the daily standard. Each individual document includes sections on industry macro environment, policy details, regional duty-free store operation data, leading brand trends, and revenue estimation modules. Fields include publishing institution, publication date, regional duty-free sales revenue, average daily in-store passenger flow, and number of covered brands. Numeric fields uniformly use general economic statistical units.
Constraints on Vector Models and Indexing
The multi-source, scattered nature of duty-free research report data requires vector models to support mixed-format encoding, balancing structured operation data and unstructured policy interpretation text. Periodic large-scale indexing tasks, caused by fluctuating update rhythms, need incremental index trigger mechanisms to balance resource usage. Documents contain a large number of high-value structured numeric fields; relying solely on full-text vectors cannot accurately match retrieval needs related to numeric values, so additional vector mapping rules for structured fields must be configured. The presence of long text modules requires a chunking strategy that adapts to the natural paragraph boundaries of research reports, avoiding splitting that destroys logical connections.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The natural paragraph length of duty-free research reports mostly falls between 500-1500 characters. This range preserves the logical integrity of paragraphs |
enable_structured_vector | Enabled | Research reports contain a large amount of structured operation data. Separately generating structured field vectors improves the accuracy of numeric retrieval |
recall_top_k | 15–20 results | The retrieval requirements for segmented tracks of duty-free research reports have high precision demands. A sufficient candidate set must be retained for subsequent reranking |
rerank_top_k | 5–8 results | Final returned results must be kept at a reasonable length while covering core relevant research reports |
incremental_index_trigger | Triggered by document update timestamp | The update rhythm of duty-free research reports fluctuates with policy nodes. Incremental indexing reduces resource consumption from full indexing |
vector_batch_size | 32–64 | Batch processing improves indexing efficiency while avoiding memory overflow |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are influenced by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to perform testing with one’s own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Reranked results are not displayed in online recall tests, and the number of returned results matches that when reranking is disabled. Cause: The
rerank_model_api_keyparameter is not filled correctly, or the input dimension of the reranking model does not match the vector dimension generated by the index. - Phenomenon: Index generation speed is slow, or the relevance of structured field retrieval results is low. Cause: The
enable_structured_vectorparameter is not enabled, and structured operation data in research reports is only encoded using full-text vectors. - Phenomenon: An interface error is returned when calling the vector model, and the log shows protocol incompatibility. Cause: Adaptation parameters for multimodal vector model calls are not configured, and the model's interface rules are not aligned with the current system.
How to Verify Successful Configuration
- Check the knowledge base vector configuration interface to confirm that the filling status of key parameters such as
enable_structured_vectorandrerank_model_api_keymatches the preset configuration. - Run an indexing task for a single test research report, and verify whether vector data for the corresponding structured fields is generated through system logs.
- Initiate a retrieval test that includes structured keywords such as regional duty-free sales revenue and average daily passenger flow, and confirm whether the relevance of returned results meets expectations.
- Simulate an update trigger action for a research report, and confirm whether the incremental indexing task starts according to the preset rules.
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.