What This Category's Data Looks Like
The data sources for rural commercial bank marketing content are marketing material libraries from internal operation systems, product documents from credit management systems, and customer communication script collations from branch outlet feedback. Update cadence follows a fixed monthly schedule, with temporary additional updates during major marketing campaigns. Document structure is split into structured product entries and unstructured campaign materials. Structured entries include product category, target customer group, application requirements, supporting marketing scripts, and campaign period. Unstructured materials include campaign poster descriptions and offline presentation scripts. Fields include product category, target customer group, application requirements, marketing scripts, campaign period. Units: campaign period uses calendar days, and the quota in application requirements uses ten thousand yuan as the unit.
Constraints Imposed on Knowledge Base Retrieval and Recall by These Characteristics
High proportion of structured product entries requires the retrieval link to support field-level precise matching, to avoid irrelevant content recall caused by relying solely on full-text retrieval. Update cadence includes both regular and temporary types, requiring the knowledge base to support flexible switching between incremental and full updates, to adapt to monthly batch updates and temporary campaign additional update needs. Marketing scripts are mostly colloquial, requiring the retrieval link to support synonym expansion and semantic matching, to cover similar expressions. Target customer groups focus on local county-level areas, requiring the retrieval link to add regional dimension filtering, to avoid recall of cross-regional products. Unstructured materials include multi-format files, requiring the parsing link to be compatible with common formats such as PDF and Word, while retaining text structure and layout information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall TopK | 3-5 entries | Rural commercial bank marketing content targets local customer groups, and marketing recommendations for a single touchpoint should not be excessive to avoid information overload |
similarity threshold | 0.72-0.85 | Marketing scripts are mostly colloquial, so it is necessary to balance semantic matching accuracy and recall coverage, to avoid false recall of cross-regional or irrelevant products |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Some marketing campaign PDFs include multi-page posters and text descriptions, requiring sufficient parsing time to complete format conversion and text extraction |
TEXT_SPLITTER_CHUNK_SIZE | 800-1000 characters | Marketing scripts and product documents are mostly short paragraphs. Excessively long segments will cause semantic fragmentation, while excessively short segments will increase retrieval noise |
LOCAL_VECTOR_DB_ENABLE | Enabled | Some marketing data of rural commercial banks involves internal customer information, so it is necessary to ensure that data does not leave the local environment to avoid sensitive information leakage |
INCREMENTAL_UPDATE_TRIGGER | Triggered when the number of new documents ≥10 | The volume of materials updated monthly is stable, and incremental updates can reduce resource consumption of full parsing, adapting to regular update cadence |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Knowledge base retrieval takes more than 15 seconds, even for single-document retrieval. Cause: The
TEXT_SPLITTER_CHUNK_SIZEparameter was not adjusted. Excessively long segments lead to overly high vector dimensions, increasing retrieval computation load. - Phenomenon: After offline deployment, knowledge base queries return unorganized raw text or irrelevant content. Cause: The
LOCAL_VECTOR_DB_ENABLEconfiguration was not enabled, relying on external cloud service vector calculation, causing local data synchronization exceptions. - Phenomenon: When inputting a question with no matching content in the knowledge base, the AI still generates fabricated responses. Cause: No matching content trigger rule was configured, or the rule was not correctly bound to the retrieval link.
How to Verify Proper Configuration
- Upload a rural commercial bank marketing script document, check segment length against the
TEXT_SPLITTER_CHUNK_SIZEconfiguration interval via parsing logs to confirm text processing meets expectations. - Initiate a local retrieval test, check the response time of the retrieval interface via the system monitoring panel to verify time consumption meets business requirements.
- Input a question that does not exist in the knowledge base, verify via the test conversation interface whether the AI returns empty content or a preset no-match prompt to confirm the trigger logic takes effect.
- Perform an incremental update operation, confirm only newly added marketing documents were synchronized via the vector database document count to verify the update rule takes effect.
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.