What This Category's Data Looks Like
Data sources for joint-stock bank marketing content include internal marketing material libraries, customer manager script libraries, online activity page copy, credit card benefit description documents, and more. Updates follow two rhythms: fixed cycles, such as quarterly themed marketing refreshes, and temporary triggers, such as limited-time promotions and activity supplementary updates. Documents include fields such as title, applicable customer group tags, product type, activity rules, and contact information. Some materials contain tables and images. Field units are mostly character counts, date formats, and user tier tags. There is no unified single-document length standard.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
First, mixed structured and unstructured content from multiple data sources requires vector models to support multimodal input and semantic encoding of structured fields. Second, the high frequency of temporary updates requires indexes to support incremental refresh, avoiding performance losses from full reconstruction. Third, the finance sector has many domain-specific terms. Vector models must support precise matching of financial semantics. Fourth, multi-field filtering is required. Indexes must support recall filtering by fields like product type and applicable customer group, improving matching accuracy for marketing content.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | doubao-embedding-finance or voyage-finance-2 | Adapts to financial domain terminology semantics, improving recall accuracy for marketing content |
chunk_size | 800-1200 characters | Matches the typical length of joint-stock bank marketing copy, avoiding semantic fragmentation |
index_refresh_strategy | incremental update + weekly full update | Adapts to high-frequency updates for temporary marketing activities and regular synchronization of quarterly materials |
recall_top_k | top 8-12 entries | Balances coverage of marketing content and retrieval accuracy |
filter_fields | ["product_type", "applicable_crowd"] | Supports filtering recall results by product type and applicable customer group fields |
similarity_threshold | 0.75-0.85 | Filters low-match irrelevant marketing materials, improving recommendation effectiveness |
embedding_batch_timeout | 600 seconds | Prevents timeout when processing large batches of marketing materials |
The parameter values provided on this page are common starting points for configuration setup. 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
- Phenomenon: Calling the multimodal embedding interface returns an
Invaliderror code with no valid response body. Cause: No correct input parsing rules are configured for marketing materials with tables and images, preventing the model from generating vectors normally. - Phenomenon: After upgrading the version, the voyage index becomes unavailable, returning a
400 status code no bodyerror. Cause: The index's API request verification parameters were not updated synchronously, or the interface format requirements of the new model version were not adapted. - Phenomenon: Rebuilding the knowledge base index takes too long, or incremental updates do not take effect. Cause: No incremental update rule configured to filter by update timestamp, leading to incorrect full reconstruction operations.
How to Confirm the Configuration Is Correct
- Submit a single marketing copy test. Check that the
embeddingfield in the interface response is not empty, and the returned status code is 200. - Trigger an incremental update task. Verify that the number of newly added materials in the index matches the statistical count of materials to be updated.
- Configure a filter rule for a specified product type. Retrieve relevant marketing content, confirm that the recall results only include materials of the matching type.
- Adjust the similarity threshold. Verify that the match accuracy of the recall results conforms to the preset filtering logic.
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.