What the data for this use case looks like
Marketing content data for rural commercial banks comes primarily from internal marketing asset libraries, including offline branch promotional brochures, official account posts, standard client manager communication scripts, and exclusive wealth product documentation. Data updates align with marketing campaign cycles, product iterations, and regulatory compliance requirements, with no fixed schedule. Document structures include topic, target customer group, compliance identifier, and main content. Most fields are text-based; some materials include auxiliary images, and core content is in plain text or rich text format.
Constraints imposed by these characteristics on vector models and indexing
Multi-source heterogeneous input types (short scripts, long product descriptions, compliance documents) require chunking strategies adapted to different text lengths to avoid semantic fragmentation or redundant recall. Non-fixed-cycle incremental update demands that the indexing system support incremental synchronization to avoid performance loss from full reindexing. Metadata fields such as target customer group and compliance filing number require the index to combine metadata filtering and vector recall to narrow retrieval scope. Some content includes localized colloquial expressions and financial jargon, requiring vector models adapted to Chinese industry-specific semantic encoding to improve recall accuracy.
How to configure settings
| Configuration Parameter | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Rural commercial bank marketing content includes short scripts and long product descriptions. This range balances the completeness of short text and semantic coherence of long text, avoiding semantic breaks from overly small chunks or redundant recall from overly large chunks. |
chunk_overlap | 100–150 characters | Repeated compliance prompts and customer group descriptions exist in marketing content. The overlap range preserves cross-chunk contextual connections to improve recall accuracy. |
INDEX_INCREMENTAL_SYNC | Enabled | Rural commercial bank marketing content updates have no fixed cycle and are mostly incremental. Enabling this parameter only syncs newly added or modified content, reducing indexing time. |
retrieval_top_k | Top 5–8 results | Recall for rural commercial bank marketing content needs to balance accuracy and screening efficiency. Too many results increase the user's screening cost, while too few may miss content suitable for the target scenario. |
filter_metadata_fields | ["适用客群", "合规备案编号"] | Marketing content requires precise recall based on customer group and compliance. Metadata filtering can greatly narrow the vector retrieval scope and improve retrieval efficiency. |
embedding_model | Model supporting Chinese financial industry jargon | Rural commercial bank marketing content includes financial jargon and localized expressions. Ensuring accurate semantic matching via vector encoding is required. |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: After calling the
pushdataAPI to upload content in chunk mode, the interface continuously displays the "Indexing" status with no data returned. Cause: TheINDEX_INCREMENTAL_SYNCparameter is not configured correctly, or required metadata fields are not specified during upload, causing indexing tasks to block. - Symptom: All returned retrieval results have low relevance and poor matching with query semantics. Cause: The selected
embedding_modelis not adapted to financial industry jargon, or thechunk_sizesetting is too small, causing semantic blocks to be fragmented and losing context of complete marketing scenarios. - Symptom: The last set of chunked content fails to complete indexing, with the log returning "Field format error". Cause: The uploaded content includes metadata fields that were not pre-configured, or
filter_metadata_fieldsdoes not cover all required fields, causing indexing verification to fail.
How to verify correct configuration
- Upload a single test marketing piece, check the chunking logs for the indexing task, confirm that the chunking results corresponding to the
chunk_sizeandchunk_overlapparameters meet expectations. - Initiate a retrieval request, check that the number of returned results matches the configured
retrieval_top_kparameter, and that the results include the configured metadata filtering conditions. - Simulate incremental content updates, check that the indexing system only syncs newly added content and does not trigger full reindexing, confirming that the
INDEX_INCREMENTAL_SYNCconfiguration is active. - Check the embedding model call logs, confirm that the returned vector dimensions match the preset vector index dimensions of the platform, with no encoding errors.
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.