What the data for this category looks like
Data for advertising and marketing financial report analysis comes from real-time delivery logs of advertising platforms, exposure and conversion reports from media monitoring agencies, marketing budget ledgers from brand owners, and publicly disclosed financial reports for the marketing segments of listed companies. Data update rhythms vary: daily delivery data updates daily or hourly, while quarterly financial report data updates in batches on a fixed schedule. The document structure mixes structured tables and unstructured text. Structured fields include delivery amount, impressions, conversion rate, ROI, and similar metrics. Unstructured content includes advertising creative copy, campaign review reports, delivery time slot configurations, and other content.
Constraints on vector models and indexing from these characteristics
Mixed structured and unstructured data types require indexes to support joint retrieval of numeric fields and text vectors. Differentiated update frequencies require adaptive strategies for full and incremental indexes, to avoid wasting resources on full index rebuilds. Multiple field types and units require unified field mapping rules, to prevent vector retrieval bias caused by inconsistent units. Advertising material text lengths vary widely, from short titles to dozens of pages of review reports, so a flexible segmentation strategy is needed to accommodate different text lengths.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Advertising and marketing financial reports include short delivery titles (tens of characters) and long review reports (thousands of characters). This range balances segmentation accuracy and retrieval efficiency |
vector_db_incremental_sync | Enabled | Daily delivery data updates daily. Full index rebuilds take significant time. Incremental sync only updates changed data |
embedding_model | bge-large-zh-1.5 or a compatible model with the same embedding dimension | The advertising and marketing industry contains many specialized terms and Chinese context content. This model meets semantic understanding requirements. The embedding dimension must match the index database configuration |
recall_top_k | Top 8–12 results | Financial report analysis needs to cover multi-channel delivery data. Too many recalled results increase context redundancy, while too few miss critical metrics |
structured_field_mapping | Automatic mapping by field name | Advertising and marketing financial reports include multiple structured fields. Automatic mapping avoids manual configuration errors |
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 mistakes
- The symptom is that search results do not return relevant auxiliary delivery data. The cause is failing to include auxiliary marketing data in the vector indexing scope, or failing to enable the vectorization switch for auxiliary data.
- The symptom is irrelevant cross-data-group matches in recall results. The cause is failing to enable multi-vector association mapping in index configuration for version v4.8.7, leading to incorrect binding of multiple vectors for a single data entry.
- The symptom is a "dimension mismatch" error when loading the index on the server, or obvious semantic deviation in recall results. The cause is inconsistent embedding dimensions between the vector model used locally and on the server, with no unified model configuration.
How to confirm correct configuration
- Upload a test file containing structured delivery data and unstructured review reports. Check whether the index construction log displays the complete process of segmentation, vectorization, and index writing.
- Enter a query containing marketing specialized terms. Verify that the recall results include structured data and text content for the corresponding fields.
- Submit test data for incremental updates. Check that the index only updates new or changed entries, and does not perform a full rebuild.
- Check that the embedding dimension of the vector model configuration matches the configuration dimension of the index database, to avoid dimension mismatch issues.
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.