What the data for this category looks like
Data sources for this category include public regulatory disclosure APIs, third-party credit data sources, and enterprise-submitted archival materials. Update cycles fall into two categories: batch synchronization and real-time pulling. Regulatory data updates on a calendar day basis, while enterprise-submitted data updates upon submission. Document structures typically mix structured fields and unstructured attachments. Structured fields include standard industrial and commercial fields such as unified social credit code, establishment date, business scope, and others. Unstructured attachments include scanned audit reports and PDF qualification certificates. For field units: registered capital is measured in ten thousand yuan, revenue data is measured in yuan. Some enumeration fields must match standard values for the financial due diligence industry.
What constraints do these characteristics impose on workflow orchestration
Multi-source heterogeneous data sources require workflow configuration for multi-data source permission verification and format conversion nodes, to avoid parsing failures caused by field mismatches. Differences in update cycles require setting up switching logic between scheduled full pulls and event-based incremental pulls, to prevent repeated pulling of expired data or missing real-time submitted information. Mixed document structures require configuring parallel processing nodes for structured extraction and unstructured parsing, to adapt to processing needs of different data types. Standardization of fields and units requires preset unified mapping rules, to avoid unit confusion or enumeration value mismatches in subsequent report generation steps.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
trigger_mode | "Scheduled full pull + event-based incremental pull" hybrid mode | Adapts to the update rhythm of regulatory data updated daily and enterprise-submitted data triggered in real time, avoids invalid pulls |
rag_similarity_threshold | 0.75–0.85 | Balances recall precision and coverage for professional financial text in due diligence reports |
workflow_timeout | 900 seconds | Covers the full process time of multi-source data pulling, parsing, and report generation |
structured_field_mapping | Preset standard field mapping rules for industrial and commercial/credit data | Unifies field names and units of multi-source heterogeneous data, eliminates format differences |
error_retry_max_times | 2–3 times | Addresses pulling failures caused by temporary interface fluctuations, avoids direct workflow termination |
parse_attachment_max_size | 50 MB | Adapts to conventional size limits for attachments such as qualification certificates and audit reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Workflow terminates directly after reaching the knowledge base search node, with no subsequent output. Cause: Multi-data source permission verification rules are not configured, causing some API calls to return null values and triggering node interruption.
- Cannot pass historical conversation content as a variable to the code running node. Cause: The historical context storage switch for the workflow is not enabled, or the
chat_historysystem variable is not correctly referenced in the code node. - The workflow prompts "Cannot convert undefined or null to object" during execution. Cause: The structured field mapping rules do not cover all required fields, causing the data cleaning node to output an empty object.
How to confirm proper configuration
- Manually trigger the workflow once, check the running logs of each node, confirm that the output results of data source pulling, field mapping, and RAG recall meet expectations.
- Adjust the value of
rag_similarity_threshold, verify that the number and relevance of recall results meet business requirements. - Simulate a temporary interface exception scenario, confirm that the workflow will automatically retry according to the configured number of retries, and will not terminate directly.
- Import a standard due diligence report attachment, confirm that the parsing node can correctly extract structured fields and unstructured content.
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.