What data for this category looks like
Data for rural commercial bank intelligent due diligence reports comes primarily from internal credit management systems, enterprise credit disclosure platforms, agricultural and rural competent authorities’ agricultural-related business filing data, and third-party industrial and commercial information interfaces. Data updates are triggered per individual due diligence task, generated on demand, or updated in batches weekly. Document structure is split into five modules: basic enterprise information, operating flow details, agricultural-related business proportion, credit history records, and guarantee association status. Fields include unified social credit code, agricultural-related revenue proportion, credit limit (ten thousand yuan), non-performing loan ratio, and others. Some fields require adaptation to special operating data formats of county-level agricultural-related entities.
What constraints these characteristics impose on workflow orchestration
The multi-source and scattered nature of rural commercial bank due diligence data requires workflow orchestration to connect multiple cross-system call nodes, and adapt to authentication and return formats of different interfaces. Validation logic for agricultural-related business-specific fields requires additional specialized judgment rules during data cleaning, to prevent non-standard agricultural-related operating data from entering subsequent processing. The dual requirements of on-demand generation and batch updates require workflows to support both single-task trigger and batch node execution modes, while enabling dynamic parameter adaptation for different tasks. Non-standard data formats from county-level entities also require adding pre-processing nodes for field standardization, to unify field names and units across all data sources.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
batchTaskMaxConcurrency | 2–5 | Rural commercial banks perform due diligence for county-level enterprises across multiple services. Batch task concurrency should not be too high, to avoid triggering third-party interface rate limits |
nodeErrorRetryTimes | 2–3 | Third-party credit and industrial and commercial interfaces have occasional fluctuations. Limited retries can reduce task failure rates |
globalVariableAppendMode | append | Batch execution nodes need to cyclically append child item results to global variables, to avoid overwriting historical data |
toolCallModel | qwen3.5-plus | Due diligence reports require multi-round logical reasoning and field validation. This model supports complex content integration needs |
parseFileTimeoutSeconds | 900 seconds | Rural commercial bank due diligence reports may include multiple agricultural-related operating attachment documents. Sufficient parsing time is required |
rateLimitThreshold | 100 requests per minute | Adapts to the official call rate limit rules of qwen3.5-plus, to avoid triggering 429 errors |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Tool call nodes return the
429 Request rate increased too quicklyerror. Cause: TherateLimitThresholdparameter is not configured, or its value exceeds the official rate limit threshold of the model, leading to too many model requests being initiated in a short period. - Symptom: After a batch execution node completes, the global variable only retains the result of the last child item. Cause: The
globalVariableAppendModeconfiguration is not set toappend. The default overwrite mode replaces results from each loop. - Symptom: After a model request fails, the workflow terminates directly, without generating error logs or fallback results. Cause: No error capture branch is added after the model call node, and the retry logic for
nodeErrorRetryTimesis not configured, making it impossible to handle occasional interface fluctuations.
How to confirm correct configuration
- Initiate a single-enterprise due diligence test task, check the running logs of tool call nodes, confirm no
429related errors occur, and verify that therateLimitThresholdvalue matches the rate limit rules of the currently used model. - Initiate a batch due diligence test task, check the stored content of the global variable after completion, confirm that return results of all subtasks are included, and that no situation where only the last item is stored occurs.
- Manually trigger an abnormal model call request, confirm that the workflow executes the preset retry logic or error branch, and that no direct task termination occurs.
- Upload a due diligence report document containing multiple attachments, confirm that the file parsing node completes parsing within the preset duration, and that no timeout error is triggered.
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.