What the Data for This Use Case Looks Like
Rural commercial bank investment research data primarily comes from local credit business ledgers, regional agricultural industry monitoring reports, policy documents released by local agricultural and rural affairs departments, and operation records of cooperating farmers. Credit ledgers are updated daily, industry monitoring reports are updated monthly, and policy documents are synced immediately upon release. Documents include structured business fields such as customer ID, loan amount, project type, semi-structured interview records, and plain-text policy clauses. The unit for amount fields is ten thousand yuan, time fields use the YYYY-MM-DD format, and most agricultural-related project types are local characteristic categories such as planting, aquaculture, and agricultural product processing.
Constraints Imposed by These Characteristics on Model Access and Configuration
Multi-source heterogeneous data structures require the model access link to support mixed adaptation of structured field extraction, semi-structured text parsing, and plain-text semantic understanding. Data sources with different update frequencies require configurable synchronization trigger rules to distinguish trigger conditions for daily incremental synchronization and on-demand full synchronization. The diversity of local characteristic agricultural-related project categories requires the model entity extraction configuration to support a custom tag system to adapt to regional exclusive industrial classifications. The fixed unit of amount fields requires configuration parameters to lock the output format to avoid automatic unit conversion by the model. The long-text attribute of semi-structured interview records requires adjusting the segmentation threshold to retain context integrity.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_segment_length | 800–1200 characters | Adapts to semi-structured interview records and long-text policy documents, avoiding splitting that disrupts context coherence |
structured_field_sync_interval | 1 hour | Adapts to the daily update rhythm of credit ledgers, supporting high-frequency incremental synchronization |
policy_doc_parse_mode | Plain text + clause extraction | Adapts to the structured extraction requirements of policy documents, retaining clause hierarchy |
custom_entity_tags | Local agricultural industry classification list | Adapts to entity recognition for regional exclusive project types, covering local categories such as planting, aquaculture, and agricultural product processing |
output_amount_unit | Retain original unit | Adapts to the output requirement that amount fields use ten thousand yuan as the unit, avoiding automatic model conversion |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing duration of large-volume credit ledger files, avoiding timeout interruptions |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and validation against internal test samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Residual <think> tags or unrecognized custom inference tags appear in model outputs. Cause: Label replacement rules for model output format were not configured correctly, or the recognition format of the target platform was not matched.
- Phenomenon: Custom system prompts do not take effect after selecting a model deployed via ollama. Cause: The prompt pass-through parameter was not enabled in the model access configuration, or the parameter configuration order was incorrect.
- Phenomenon: A 402 status code is returned when calling the model. Cause: A valid key was not correctly bound in the model access configuration, or the calling permission for the corresponding model was not activated.
How to Confirm Successful Configuration
- Upload a single credit ledger file, check if parsed fields include preset business fields, and verify field units meet preset requirements.
- Trigger an incremental synchronization task, check if the synchronization log includes newly added business record entries from the current day, confirming that the synchronization interval configuration is effective.
- Initiate a test conversation, input a query containing local agricultural-related projects, and check if the entity classification in the model output matches the custom tag system.
- Start a multi-turn conversation, confirm that the custom system prompt is correctly applied at the start of the conversation, and that no residual inference tags appear in the model output.
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.