Data Profile for This Category
Data sources include regional agricultural-related economic monitoring documents released by local financial regulatory authorities, the bank’s own credit business ledgers, public operating data of regional leading agricultural enterprises, and county-level financial operation briefings released by the central bank. Update frequency: The bank’s credit data is updated daily, regulatory documents are updated monthly, and industry research data is updated weekly. Document structures include structured business indicator lists, semi-structured policy interpretation texts, and long-form regional industry analysis reports. Fields include agricultural-related loan issuance scale, credit customer operating data, and regional industrial development-related values. Units include ten thousand yuan, mu, head, person-times, and other standard units.
Constraints Imposed on Knowledge Base Retrieval and Recall
High proportion of structured business indicator documents: The retrieval link must support both structured field matching and vector semantic recall, to avoid missing precise indicator data when relying only on semantic recall. Large update rhythm differences across multi-source data: Incremental update tasks must be configured to run in batches by data type, to prevent full updates from occupying excessive business hour resources. Non-negligible share of long-text industry analysis reports: Core business-related context must be retained during text segmentation, to avoid semantic fragmentation that reduces retrieval matching accuracy. High share of region-specific data with limited public matching rate: Recall operations must prioritize local data entries related to the bank’s own credit customer groups.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Investment research documents for rural commercial banks are mostly long texts with built-in business logic. This range preserves complete semantic connections between credit indicators and policy clauses, avoiding segmentation that splits core information |
similarity_threshold | 0.72–0.80 | Public matching rates for regional niche business data are low. This range retrieves enough relevant local documents while filtering out irrelevant general industry content |
recall_top_k | Top 8–10 results | Investment research for rural commercial banks requires balancing local policies and the bank’s own credit data. Too many recalled results increase context processing overhead, while too few fail to cover all relevant information |
rerank_top_n | Top 3–5 results | Investment research decisions require precise alignment with core data. Retaining highly relevant entries after reranking improves retrieval accuracy |
incremental_update_interval | 2:00 AM daily | The bank’s credit data is updated daily, and regulatory documents are updated monthly. This interval enables batched synchronization of latest content by data type, avoiding resource occupation during business hours |
structured_field_mapping | Auto-match by business indicator name | Investment research data for rural commercial banks includes a large number of standardized business fields. Auto-mapping reduces manual configuration costs and improves structured retrieval accuracy |
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. Analyze specific issues on a case-by-case basis, and test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After configuring segmentation parameters, retrieved and recalled content deviates significantly from the core information of the original document. Cause: Business-related context was not retained during segmentation, leading to semantic fragmentation that cannot match the complete indicator logic required for investment research.
- Phenomenon: Even after setting
similarity_thresholdto 1, a large number of low-relevance reference contents are returned. Cause: The reranking mechanism was not enabled simultaneously, relying only on the original similarity ranking from vector recall without filtering out semantically similar but business-irrelevant entries. - Phenomenon: Retrieval response time exceeds 10 seconds, reaching 15 seconds in some scenarios. Cause: Incremental update was not configured, leading to full vector database retrieval, or structured retrieval shunting was not enabled, leading to excessive vector recall calculation volume.
How to Verify Proper Configuration
- Run a single structured indicator retrieval, and verify that returned results include matching business fields and corresponding values to confirm structured retrieval configuration is active.
- Initiate a semantic retrieval related to regional industries, and verify that the update time and data source of recalled results match the preset update rhythm to confirm incremental update configuration is active.
- Check retrieval response time, and compare time changes before and after configuration adjustments to confirm performance optimization configuration is active.
- Adjust the similarity threshold, and verify changes in the number and relevance of returned results to confirm threshold configuration meets business requirements.
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.