What this category of data looks like
Data sources for this category include public research reports across cross-segment industries, third-party comprehensive industry databases, public financial reports of listed companies, and custom survey data. Update cycles vary by data source type, including quarterly, monthly, and irregular updates. Document structures include structured indicator fields such as industry revenue and enterprise market share, unstructured text content, associated cross-industry classification codes, enterprise subject identification fields, and units including currency units, percentages, and industry classification codes.
What constraints do these characteristics impose on database and operations work
The need to access cross-data sources requires databases to support multi-source synchronization protocols and scheduling configurations adapted to different update cycles. The requirement for mixed storage of structured and unstructured data requires configuring both a relational database for structured indicator fields and a vector database for unstructured text content. The dynamic field characteristics of cross-industry data require database table structures to support flexible expansion, avoiding hard-coded fields that restrict data access. The presence of multi-dimensional associated fields requires the establishment of multi-level indexes to ensure query efficiency. Differences in update frequencies across data sources require configuring differentiated scheduled synchronization tasks to avoid excessive operational resource usage from full synchronization.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
db_sync_schedule | Configure per data source type: set quarterly updated data sources to 0 0 2 * * 3, monthly updated sources to 0 0 2 1 * * | Matches actual update cycles of each data source, avoiding duplicate synchronization or delayed updates |
vector_store_batch_size | 200-500 items per batch | Balances single-batch ingestion performance and memory usage, preventing timeouts from overly large single submissions |
structured_db_table_prefix | invest_research_ | Distinguishes from other business database tables, avoids naming conflicts, and clarifies business ownership |
db_connection_timeout | 300 seconds | Adapts to query delays across industry data sources, reserves sufficient connection time to prevent interruptions |
dynamic_field_switch | Enabled | Adapts to custom fields for cross-industry data, allows dynamic expansion of table structures |
index_strategy | Create joint indexes based on enterprise identification, industry classification, and update time | Covers high-frequency query scenarios, improves query efficiency for multi-dimensional filtering |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- A 1064 syntax error is returned when executing multi-line SQL statements. The cause is that multi-line query support for database connections is not enabled; single-statement execution is allowed by default.
- After restoring a project backup, knowledge base and agent configurations fail to load properly. The cause is that only front-end project files were restored, and associated database backup files were not synchronized for recovery; business data is stored in the database.
- Cross-industry data queries time out. The cause is that layered indexes were not configured, and single-table indexes do not cover all required fields, leading to full table scans that consume excessive query resources.
How to confirm configurations are correctly applied
- Execute single-line and multi-line SQL statements, confirm no syntax error reports, and verify the multi-line query switch status for database connection configurations.
- Import a test dataset containing cross-industry structured and unstructured content, confirm data is fully stored and dynamic fields are not abnormally filtered.
- Execute combined queries based on enterprise identification and industry classification, verify the query process works normally, and confirm index configurations cover the query dimensions.
- Trigger a pre-configured scheduled synchronization task, confirm the task executes according to scheduling rules, and no failure logs 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.