What this category's data looks like
The data for rural commercial bank yield and market daily reports comes from the same-day transaction flow and asset ledger of the local core business system. The update rhythm is to complete the summary after the end of each business day, and generate the official document on the morning of the next working day. The document structure uses structured tables grouped by statistical cycle, including fields such as statistical date, institution code, business category, average return, and weighted return. Units for return-related fields are annualized return benchmark values, and percentage notation is not used.
Constraints imposed by these characteristics on model access and configuration
Structured data sources require selecting a model adapter that supports structured table parsing first, to avoid format loss from general document parsing. Fixed field structures require configuring pre-defined field extraction mapping rules, to ensure accurate extraction of core fields such as statistical date and business category. The daily update rhythm requires configuring scheduled task parameters triggered every early morning, to match the data generation time window. Dispersed business entries require configuring a reasonable batch processing sharding threshold, to adapt to the processing load of single-batch data. Incremental synchronization requirements require configuring identification rules for data update markers, to avoid repeated loading of full historical data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_STRATEGY | structured_only | Adapts to the fixed structured table format of rural commercial bank daily reports, reducing format parsing errors |
SCHEDULER_CRON_EXPRESSION | 0 0 2 * * ? | Matches the time window for T+1 early morning generation of rural commercial bank daily reports, ensuring data is read only after summary is complete |
BATCH_PROCESS_SIZE | 400 items/batch | Adapts to the scale of single-day business data entries, balancing processing efficiency and memory usage |
FIELD_EXTRACT_MAPPING | statistical date:stat_date, business type:biz_type, weighted return rate:weighted_yield | Matches the standard field structure of rural commercial bank daily reports, ensuring accurate extraction of core fields |
RERANK_MODEL_MEMORY_LIMIT | 6 GB | Adapts to the video memory capacity of single consumer-grade graphics cards, avoiding re-ranking models from occupying system memory |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Meets the time required for full parsing of structured tables, preventing task interruptions mid-execution |
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 specific analysis. It is recommended to test on your own samples before finalizing.
Three common misconfigurations
- Symptom: Video memory usage continues to rise after the re-ranking model runs, eventually triggering an error about occupied system memory. Cause: No video memory usage threshold is configured for the re-ranking model, so the model does not actively release unused video memory resources.
- Symptom: The text extraction node in the workflow cannot select the deployed model. Cause: The model permission configuration for the node is not enabled, or the model is not adapted to the structured data extraction scenario.
- Symptom: Responses generated by the mounted local LLM model are unrelated to knowledge base content. Cause: The context window parameter of the model is not configured, causing input content to exceed the model's processing limit and preventing accurate association with knowledge base data.
How to confirm the configuration is correct
- Manually trigger a single day's daily report data parsing, and check if the extracted fields match the standard field structure of rural commercial bank daily reports.
- View the task scheduling log to confirm that the daily scheduled task triggers at the preset time and completes the full parsing process.
- Monitor hardware resource usage to confirm that the video memory usage of the re-ranking model does not exceed the preset limit, and no memory overflow issues occur.
- Test the broadcast content generated by the model to confirm that the extracted return data matches the values in the original document.
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.