What data for this category looks like
Data sources for joint-stock bank investment research knowledge bases include in-house macroeconomic monitoring systems, interbank business dynamic bulletins, public industry research reports, and regulatory policy documents. Update frequencies fall into four categories: real-time (interbank offered rates, exchange rate market data), daily (daily credit issuance reports), weekly (industry research report updates), and monthly (regulatory policy compilations). Document structures include structured reports with fixed unit fields such as 100 million yuan and BP, dozens of pages of unstructured research reports, and short-text policy summaries. Data fields cover three categories: macroeconomic indicators, credit business data, and interbank cooperation information. No unified short-text format exists across these fields.
Constraints on Database and Operations Workflows
Multi-source heterogeneous data formats require databases to support both structured queries and unstructured vector retrieval. This adds complexity to storage and indexing. Different update frequencies create uneven read-write loads. High-frequency writes for real-time market data and bulk imports for research reports compete for system resources. Fields include built-in fixed units. Format validation must be run during data ingestion to prevent dirty data from entering the knowledge base. Investment research data contains industry-sensitive information. Regular backups and permission controls are required. Operations must balance performance and compliance requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
vector_store_type | milvus | Supports hundreds of billions-level vector indexing, adapts to the high-frequency retrieval needs of investment research scenarios |
dataset_chunk_size | 800–1200 characters | Ensures semantic integrity of long investment research texts, avoids breaking professional content logic during segmentation |
api_request_timeout | 600 seconds | Covers parsing and ingestion time for large research reports, prevents task interruption from timeouts |
max_upload_file_size | 1000 MB | Supports upload requirements for single large industry research reports |
similarity_threshold | 0.75–0.85 | Filters low-relevance retrieval results, adapts to the professional nature of investment research content |
db_backup_schedule | 2:00 AM daily | Avoids peak business hours, reduces impact on investment research services |
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. Specific issues require individual analysis. Testing against local samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: High persistent disk read-write activity after Docker deployment. Single-node daily disk write volume exceeds business expectations. Cause: Vector database log flushing frequency is not restricted, and disk cache compression for vector indexes is not enabled.
- Phenomenon: Database connection errors during Milvus deployment, container startup fails. Cause: Configuration segments for pgvector and OB are mixed in the Compose file. Redundant dependencies are not stripped per deployment architecture.
- Phenomenon:
Authentication failederror during database connection. Cause: Authentication parameters for different databases are mixed in the configuration file. Username and password configurations do not meet target database requirements.
How to Confirm Configuration is Complete
- View the database monitoring dashboard, verify that vector retrieval and data ingestion loads match preset business peaks.
- Upload a standard-sized industry research report, confirm parsing and ingestion processes have no abnormal interruptions.
- Run a full data validation task, confirm ingested data field formats comply with preset rules.
- Check container runtime logs, confirm no startup or runtime warnings caused by configuration errors.
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.