What the data looks like
Investment research data for rural commercial banks comes from multiple sources: internal credit ledgers, regional economic research briefings, regulatory policy documents, local agricultural industry monthly operation reports, and corporate credit reports. Different data types have distinct update cadences. Credit ledgers update daily. Regulatory policy documents release quarterly or on an ad-hoc basis. Regional research briefings update irregularly.
Documents include both structured fields and unstructured text. Structured fields include customer ID, loan balance, and industry classification, with units such as ten thousand yuan and hundred million yuan. Unstructured text mostly consists of regional industry analysis and policy interpretation paragraphs. Some briefings contain local agricultural terminology and charts.
Constraints for Vector Models and Indexing
The mixed structured and unstructured structure of the data creates specific requirements for vector models and indexing workflows.
Vector models must support encoding both structured fields and natural language text. This avoids semantic bias from using a single model for all data types.
The indexing workflow must support flexible switching between incremental and full indexing. This accommodates daily credit ledger updates and irregular research briefing updates.
Vector models must adapt to regional industry vocabulary. This prevents semantic misunderstanding of local agricultural terms by generic models.
The indexing workflow must support image vector extraction. This covers all types of investment research documents, including those with charts.
The data has moderate volume but uneven update frequencies. This requires stable indexing performance to avoid service freezes during batch indexing.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Rural commercial bank investment research documents often contain regional industry terminology and long paragraphs of credit analysis. This length preserves context integrity and avoids splitting of terminology |
chunk_overlap | 100–150 characters | Mixed structured fields and unstructured text. Overlapping sections reduce semantic loss across segments |
index_batch_size | 20–30 items per batch | Rural commercial bank investment research data has uneven update frequencies. Small batch sizes reduce memory usage for incremental indexing and avoid indexing lag |
similarity_threshold | 0.72–0.78 | Regional industry terminology has high semantic similarity differentiation. This range filters irrelevant cross-regional industry documents |
recall_top_k | Top 8–12 results | Rural commercial bank investment research focuses on local business. Too many recalls introduce redundant cross-regional data, while too few result in insufficient coverage |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing some large regional economic research briefings takes a long time. This duration avoids parsing timeout failures |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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
- Issue: Full indexing tasks throw
ETIMEDOUTerrors, or take significantly longer than the preset threshold. Cause: Batch indexing parameters are not aligned with the incremental update characteristics of rural commercial bank data. Using large-batch full indexing leads to excessive memory usage. - Issue: When creating a knowledge base, the text understanding model dropdown does not display the added ollama qwen2.5 and bge-m3 models. Cause: Vector models and text understanding models are not separately bound to the vector recall and text generation links of the knowledge base, or the model deployment ports are not open to the service network segment.
- Issue: After uploading a research briefing containing agricultural industry charts, no corresponding vector index is generated. Cause: Automatic image indexing configuration is not enabled, or vector extraction for regional agricultural terminology in the charts is not supported.
How to Confirm Proper Configuration
- Run an indexing task for a single test document, and verify that the generated segmented vectors match the semantic meaning of the original document to meet business requirements.
- Simulate an incremental update scenario by submitting daily newly added credit ledger data, and verify that the indexing task completes within the expected duration.
- Check the model binding configuration, and confirm that the vector model and text understanding model correspond to the recall and generation links of the knowledge base respectively.
- Upload a test document containing charts, and verify that corresponding image vector indexes are generated.
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.