What the Data for This Category Looks Like
Duty-free marketing content data comes primarily from brand duty-free channel product filing materials, offline store event planning assets, online promotion copy templates, and member benefit notifications. Update frequency fluctuates with brand marketing cycles. Updates are more frequent during holiday promotions and new product launches. Daily maintenance follows a steady rhythm. Each document includes standard fields: product SKU code, duty-free quota, activity start and end dates, applicable store scope, and target customer group tags. Quota units are Renminbi yuan. Date fields use ISO 8601 format. Copy length varies widely, from short social media posts to long detailed page descriptions.
Constraints Imposed on Vector Models and Indexing
The mixed structured and unstructured nature of duty-free marketing content requires vector models and indexing to support text vector association with metadata field retrieval. This avoids accuracy issues caused by relying solely on text vectors. Update frequency shifts with marketing cycles. Bulk data imports during peak periods create pressure on index construction speed. An elastic scaling index architecture is required. Marketing copy length varies widely, so vector models must support diverse input lengths. This prevents sparse short-text vectors or long text exceeding the model context window. Fields include clear numeric and date types. Corresponding metadata filtering rules must be configured during indexing. This supports subsequent recall result filtering by quota, validity period, and other fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers the length of most duty-free marketing copy, avoids single segments exceeding vector model input limits, and ensures semantic integrity of individual segments |
similarity_threshold | 0.65–0.8 | Adapts to the semantic similarity differentiation of duty-free marketing content, filters low-relevance recall results, and retains valid matching content |
RECALL_TOP_K | Top 10–15 results | Provides an appropriate number of candidate recall sets, avoids excessive redundant results interfering with final ranking, and balances retrieval efficiency and accuracy |
INDEX_BUILD_BATCH_SIZE | 500–1000 items/batch | Balances index construction speed and server resource usage, and adapts to bulk data import requirements during peak periods |
METADATA_FILTER_ENABLED | Enabled | Supports filtering retrieval results by structured fields such as SKU and validity period, improving accurate matching efficiency |
VECTOR_MODEL_CONTEXT_WINDOW | Configured per official model specifications | Matches the input length limit of the selected vector model, avoiding semantic loss caused by text truncation |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Retrieval returns similarity values outside the 0-1 range, such as over 10000, and some requests return a
400 Invalid Vector Dimensionerror. Cause: Vector model dimension parameters are not configured correctly, resulting in a mismatch between the vector dimension of input text and the index dimension. - Phenomenon: After importing a text dataset, the dataset status remains "Indexing" for a long time with no progress updates. Cause: Index construction batch size is set too large, exceeding server memory or CPU processing limits, causing the construction process to block.
- Phenomenon: After configuring a custom vector model channel, retrieval requests still call a large language model instead of the vector retrieval interface. Cause: The correct vector model calling channel is not specified in the retrieval configuration, or the API endpoint configured for the channel does not correctly point to the vector model service.
How to Verify Proper Configuration
- Import a single test duty-free marketing copy, check the vector generation log, and confirm that the segment length matches the configured
chunk_sizerange. - Send a retrieval request, check the similarity value range of the returned results, and confirm that normalization processing has been completed.
- Try adding metadata filtering conditions such as SKU and validity period, and check whether retrieval results correctly match the filtering rules.
- Bulk import 100 test data entries, check whether index construction progress is completed within a reasonable time frame with no blocking.
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.