What this category's data looks like
Data for urban commercial bank intelligent due diligence reports comes primarily from internal credit management systems, People's Bank of China credit reporting interfaces, industrial and commercial public disclosure platforms, audited annual financial reports of credit subjects, and operation ledgers. Update cycles include bulk initial pull and regular ongoing updates. The initial phase pulls large volumes of per-entity due diligence data per operation. Ongoing updates are synchronized quarterly. Document structure includes both structured fields and unstructured attachments. Structured fields include unified social credit code, registered capital (unit: ten thousand yuan), revenue over the past three years (unit: ten thousand yuan), credit balance, guarantor qualification level, and more. Unstructured attachments include audited report PDF files, credit report scans, and more.
What constraints these characteristics impose on vector models and indexing
Mixed structured and unstructured features require vector models to support both structured metadata encoding and unstructured text splitting and vectorization. This avoids metadata loss when only processing text. Large per-entity data volume during the initial pull requires indexes to support bulk vector import and sharded storage. This reduces memory usage during single import operations. The quarterly ongoing update rhythm requires indexes to support incremental update strategies. Only modified documents and vectors are synchronized, avoiding performance loss from full index reconstruction. Structured fields have clear associated units. The vectorization link must retain field semantic associations. For example, treat "registered capital 1000 ten thousand yuan" as a complete semantic unit. This avoids semantic distortion after splitting. Most unstructured attachments are PDF financial reports. The text extraction link must adapt to parsing accuracy across multiple document formats. This ensures text integrity for vectorization.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the average length of financial report paragraphs and credit terms in urban commercial bank due diligence reports, balances text semantic integrity and vector dimension overhead |
chunk_overlap | 100–150 characters | Prevents semantic breaks across chunks, adapts to the contextual coherence of long sentences and related clauses in due diligence reports |
vector_store_index_type | IVF_SQ8 | Adapts to the bulk vector import needs of urban commercial bank due diligence reports, balances recall accuracy and query performance |
retrieval_top_k | Top 10 entries | Covers core related information for a single entity's due diligence report, avoids context overflow caused by excessive recall results |
similarity_threshold | 0.75–0.85 | Filters low-relevance due diligence data fragments, adapts to the rigor requirements of urban commercial bank risk audits |
incremental_update_interval | 7 days | Matches the quarterly ongoing update rhythm, balances data freshness and resource usage for index updates |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Issue: PostgreSQL-related service startup failure occurs when deploying the vector database. Cause: Directly using the official Milvus Compose file without removing the built-in PostgreSQL mount configuration, resulting in local port occupation or permission verification failure.
- Issue: 503 error is returned when connecting to the Qwen3-Embedding-8B model deployed via VLLM. Cause: The configured model interface address does not include the correct port and API path, or the model service has not enabled external access permissions.
- Issue: Unable to locate the specific components that make up the disk space occupied by the knowledge base. Cause: Detailed storage path display is not enabled in system settings, and storage directories for original files, chunked text, and vector data are not differentiated.
How to confirm proper configuration
- Run the vector model test script, input a section of structured fields and unstructured text from an urban commercial bank due diligence report, and verify that the returned vector dimensions match the model configuration.
- Import a complete single-entity due diligence report dataset, and check that the vector database's index shard count matches the configured shard parameters.
- Trigger an incremental update task, and confirm that only modified documents are updated. Logs for full index reconstruction are not part of this check.
- View the system storage monitoring panel, and confirm that original files, chunked text, and vector data are stored in their respective specified directory paths.
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.