Data Characteristics of This Category
Investment platform marketing content includes compliant educational articles, wealth product promotion copy, event rule descriptions, and similar materials. Sources are the platform’s internal operational material library and compliance-reviewed published drafts. Updates trigger on demand during new product launches, marketing campaign starts, and compliance content adjustments. No fixed update schedule exists. Documents are mostly structured text with titles, main body, associated product codes, and publication timelines. Fields include core text content, associated product identifiers, and target customer group tags. Units are primarily characters and identifier numbers.
Constraints for Vector Models and Indexing
Marketing content includes both structured associated identifiers and unstructured text. Indexes must support combined filtering of metadata and vector recall to accurately associate promotion materials with corresponding products. Updates trigger on demand with no fixed schedule, so indexes must support incremental synchronization. This avoids resource waste from full index rebuilds. Content length varies widely, from short event copy to thousand-word educational articles. Vector models must support variable-length input. Some content has compliance requirements, so indexes must support pre-filtering of sensitive metadata. This ensures recalled content meets regulatory standards.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large or open-source embedding models with 768 dimensions or higher | Investment platform marketing content includes long educational articles and short event copy. High-dimensional vectors can more accurately capture semantic associations and structured identifier information |
chunk_max_length | 800–1200 characters | Adapts to the length range of marketing content, from short event copy to thousand-word educational articles, balancing semantic completeness and recall efficiency |
index_update_strategy | Incremental Synchronization | Marketing content updates have no fixed schedule. Incremental synchronization avoids resource waste from full index rebuilds |
metadata_filter_enabled | Enabled | Marketing content includes metadata such as associated product codes and publication timelines. Metadata filtering can narrow recall scope |
similarity_threshold | 0.75–0.85 | Adapts to semantic matching scenarios for marketing content, balancing recall accuracy for compliant content and promotion needs |
recall_top_k | Top 10–15 results | Covers multi-dimensional associated marketing materials while controlling computational overhead for single recall |
The parameter values provided on this page are common 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 Misconfigurations
- Issue: Conversation model calls function normally, but vector index tasks show no progress for extended periods. Logs display
embedding_task_timeouterrors. Cause:index_update_strategyis not set to incremental synchronization. Full index rebuilds time out due to accumulated marketing content. - Issue: Recalled results include expired marketing content, or fail to link to specified promoted wealth products. Cause:
metadata_filter_enabledis not enabled, and associated product codes are not included in metadata filtering configurations. - Issue: Vector matching accuracy for generated indexes is insufficient. Short event copy is incorrectly matched to unrelated long educational articles. Cause:
chunk_max_lengthdoes not align with the length range of marketing content, leading to truncated semantics or redundant context.
How to Verify Correct Configuration
- Manually upload a test marketing content item. Check index generation logs to confirm the specified embedding model is loaded, with no model call failure errors.
- After configuring metadata filtering rules, retrieve marketing content associated with a specific product code. Confirm recalled results only include promotion materials for the corresponding product.
- Adjust the chunk length parameter, then compare vector generation results for the same segment of marketing content. Confirm segmentation meets the length range requirements of the business scenario.
- Trigger an incremental index task, then check system resource usage. Confirm no high-load state associated with full index rebuilds occurs.
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.