What the data for this category looks like
Marketing content data for this category primarily comes from a brand’s own marketing material library. It includes product detail page copy, holiday promotion scripts, live stream snippets, packaging slogans, member activity notifications, and similar content. Update frequency varies based on new product launches, seasonal marketing campaigns, and channel promotion adjustments, with no fixed cycle.
Document structures fall into two categories: structured and unstructured. Structured materials include fields such as associated SKU numbers, release channels, and effective time periods. Unstructured materials are primarily plain text or formatted marketing copy. Fields include material themes, product flavors, specification parameters, and campaign intensity. Common units include character counts, SKU codes, and date formats.
What constraints these characteristics impose on vector models and indexing
Marketing materials for this category have wide variation in length. Short scripts are only tens of characters long, while long product detail pages can reach thousands of characters. This places constraints on the segment adaptation capabilities of vector models.
A high proportion of structured fields requires support for filtering search results by metadata such as SKU numbers and release channels.
No fixed update cycle requires indexes to support incremental synchronization, eliminating the need for full reconstruction and reducing resource consumption.
Marketing content has strong timeliness. The effective time period field must be included in index metadata to ensure that searches can filter valid materials by time range.
Some materials include product attributes such as flavor and specifications. Special vector mapping rules must be configured for these attribute fields to avoid semantic confusion.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers the length variation of snack food marketing copy, balances semantic completeness and retrieval efficiency |
embedding_model | Prioritize general vector models that support multi-length text, including Doubao-embedding | Adapts to diverse material types in this category, including short scripts and long product detail pages, to meet actual retrieval needs |
chunk_overlap | 100–150 characters | Preserves contextual continuity between segments, preventing semantic breaks when splitting long texts |
retrieve_top_k | 8–12 entries | Matches the single-batch material scale for snack food marketing scenarios, reduces invalid recall |
filter_metadata_enable | Enabled | Supports filtering by metadata such as SKU numbers and effective time periods to screen valid marketing materials |
index_refresh_mode | Incremental synchronization | Adapts to the feature of no fixed update cycle for this category, reduces resource consumption from full reconstruction |
The parameter values provided on this page are common 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 mistakes
- Scenario: Search queries unrelated to marketing content still return some materials. Cause: Metadata filtering is not enabled, or the
filter_metadata_enableparameter is not configured. Valid marketing materials are not screened by rules such as SKU and effective time period, resulting in recall of irrelevant historical content. - Scenario: Search results return a large number of irrelevant materials covering the entire knowledge base. Cause: The
retrieve_top_kparameter is not configured, or the value is outside the reasonable range. No filtering is performed using category-specific metadata such as SKU and theme. - Scenario: The interface shows that the index is not ready, and search requests cannot be initiated. Cause: The first index build has not completed, or full synchronization mode is configured but full data loading has not finished, resulting in the index not loading normally.
How to confirm correct configuration
- Review index construction logs to confirm that metadata fields for all marketing materials (such as SKU numbers and effective time periods) have been correctly extracted and associated with the vector index.
- Submit a test search, enter a query that includes category-specific attributes (such as a specific flavor or promotion activity), and verify that the returned results cover materials matching the corresponding theme.
- Check the vector model configuration to confirm that the selected model supports multi-length text input, and that model loading verification has been completed.
- Verify the index filtering function by setting filter conditions for SKU numbers or effective time periods, and confirm that search results only return materials that meet the conditions.
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.