Vector Models and Indexing for Rural Commercial Bank

What This Category’s Data Looks Like

Data for rural commercial bank intelligent due diligence reports comes from internal credit approval archives, financial statements of local agricultural business operators, People's Bank of China credit inquiry results, compliance materials submitted to regulatory authorities, and paper records and photos from on-site verification. The system triggers updates in real time as individual credit businesses progress, or runs quarterly bulk updates of due diligence data for existing customers. Documents include structured fields and unstructured attachments. Structured fields include customer unified social credit code, credit limit, days past due, loan purpose, and similar items. Unstructured attachments include pre-loan investigation reports, scanned balance sheets, on-site verification photos, and similar items. Field units follow financial regulatory standards: credit limit uses ten thousand yuan as the unit, and days past due uses calendar days as the unit.

Constraints Imposed on Vector Models and Indexing

Rural commercial bank due diligence reports contain both structured fields and multi-modal unstructured attachments. Vector models must support vector generation for both text and image OCR results, and indexes must support mixed data types. The system triggers updates in real time for individual businesses. Indexes must support incremental updates to avoid system resource consumption from full reconstruction. Document lengths vary widely, from short reports of a few pages to complete financial statements of dozens of pages. An adaptive chunking strategy is required to avoid semantic fragmentation or vector redundancy. Fields include enumerated and numerical content. Standardized mapping must be completed before vector generation to ensure consistency in the vector space and improve matching accuracy.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Adapts to the mixed document length of rural commercial bank due diligence reports, balancing semantic completeness and vector generation efficiency
`image_index_enabled`	Enabled	Due diligence reports include unstructured attachments such as on-site verification photos and scanned financial statements, so multi-modal indexing must be enabled
`recall_top_k`	Top 8–12 results	Covers multi-dimensional key information of due diligence reports, avoiding recall of excessive low-relevance content
`similarity_threshold`	0.72–0.85	Adapts to similarity matching for structured and unstructured mixed data, filtering irrelevant due diligence materials
`incremental_index_enabled`	Enabled	Due diligence reports are updated in real time for individual businesses, and incremental indexing reduces system resource usage
`embedding_batch_size`	32–64	Balances vector generation speed and memory usage, adapting to the average size of rural commercial bank documents

The parameter values provided on this page are common starting points for configuration setup. 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 Mistakes

Issue: No image indexing related configuration options appear when creating a knowledge base in the v4.9.0 local deployment. Cause: This feature requires commercial edition compiled packages; the community edition does not enable the multi-modal indexing module by default.
Issue: Uploaded due diligence report scans do not generate vector indexes, and the backend logs show a parse_image_failed error. Cause: The image_ocr_api_key parameter is not configured, or the OCR model call timed out.
Issue: The vector model returns a 400 status code when calling OneAPI, and indexes cannot be generated. Cause: OneAPI is not configured with correct interface parameters for the vector model, or the embedding_timeout setting in FastGPT is too short.

How to Confirm Proper Configuration

Navigate to the knowledge base management interface, check for configuration switches related to multi-modal indexing, and confirm they match the preset image_index_enabled configuration.
Upload a due diligence report sample with scanned attachments, wait for indexing to complete, and check if the document parsing details page includes text fragments from image OCR.
Initiate a vector recall test, and verify that the number of returned results matches the recall_top_k setting.
Check the system monitoring dashboard, confirm there are no abnormal fluctuations in vector model call success rates, and no persistent embedding_connection_failed error messages.

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.