What this category of data looks like
Sources of large state-owned bank yield and market data include official mobile banking applications, publicly disclosed pages on official websites, and regulatory authority bank interest rate reporting platforms. Data is updated at fixed daily times to reflect product yield changes from the previous trading day. Documents are presented in structured table format. Each document includes fields such as product unique identifier, product category, yield indicators, update date, remaining term, and additional related fields. Most fields are numeric or enumeration types, with no complex nested structures. The number of data rows per page is kept within a manageable range.
What constraints these characteristics impose on model access and configuration
Since data sources include both official APIs and static public tables, the access phase requires configuring multi-source data parsing and adaptation rules to accommodate differences in field naming across large state-owned banks. The fixed daily update schedule requires configuring scheduled pull trigger cycle parameters to match data timeliness and update frequency. The structured field structure requires configuring field mapping rules to align raw data fields with standardized fields required by the model, preventing information misalignment. The limited length of per-page documents reduces context recall pressure, but field filtering rules must be configured to exclude invalid fields. Official APIs also have access frequency limits, so current limiting threshold parameters must be configured to avoid triggering risk control intercepts.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
pull_interval_seconds | 86400 seconds | Matches the fixed daily update schedule for large state-owned bank yield data |
field_mapping_template | Calibrated based on actual testing | Accommodates differences in field naming across large state-owned banks and aligns with standardized fields required by the model |
recall_top_k | Top 3 entries | Per-page data documents have limited length; a small number of recalls is sufficient to cover core yield and market information |
api_request_limit | 10 requests per minute | Complies with default access frequency limits for large state-owned bank official APIs to avoid triggering risk control |
parse_timeout_seconds | 30 seconds | Structured data parsing takes relatively little time; the timeout threshold adapts to the overall pull workflow |
rerank_model_enable | Disabled | Structured data fields are clearly defined; information sorting can be completed without a reranking model |
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 Configuration Errors
- Symptom: A
[FATAL] failed to get remote resourceerror occurs when starting the data pull service. Cause: The server does not have external network access permissions, so it cannot pull data from large state-owned bank official data sources. - Symptom: Yield information output by the model contains a large number of punctuation marks such as
#and*. Cause: Rich text filtering rules are not configured, or the model's default markdown format output is not disabled. - Symptom: Yield data cannot be parsed normally after the reranking model is configured. Cause: The dependent environment for the rerank model is not deployed correctly, or model interface parameters do not match the data source format.
How to Confirm Successful Configuration
- Manually trigger a data pull, check for success identifiers in the pull logs, and verify that the number of returned fields matches the document structure of the target large state-owned bank.
- Check the number of results recalled by the model to confirm it matches the configured recall limit, with no excess invalid fields.
- Initiate a simulated conversation to verify that the output content has no extra punctuation marks and that yield information matches the official data source.
- Wait for one full pull cycle to confirm that the scheduled task triggers automatically and no error 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.