What this category’s data looks like
Intelligent due diligence report data for investment platforms comes from several sources: publicly disclosed financial reports, industrial and commercial public records, industry research reports, the platform’s own transaction data, and offline due diligence drafts. Update frequencies vary by source. Financial reports are updated quarterly or annually. Industrial and commercial records are updated when entity information changes. Research reports and transaction data are updated in real time or near real time. Documents include both structured fields and unstructured text. Structured fields cover entity name, registered capital, revenue scale, asset-liability ratio, and other metrics. Units include ten thousand yuan and natural date formats. Unstructured sections mostly consist of long text such as due diligence interview records and compliance statements.
Constraints imposed by these characteristics on model integration and configuration
Fixed units and format requirements for structured fields require configuration of structured output validation rules during model integration. This prevents mismatches between units and fields. Differing update frequencies across data sources require configuration of dynamic data source recall trigger logic. This adapts to the update cycles of different data types. Processing long-text due diligence drafts requires that the model context window fits single document lengths. It also requires configuration of segmentation and reranking rules to avoid information loss from truncated long text. Nested relationships between associated fields require enabling entity association parsing capabilities during model integration. This ensures logical consistency across fields.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Adapts to the long-text splicing needs of due diligence drafts and financial reports, avoids truncation of core information |
structuredOutputSchema | Configured as a structured template containing entity name, registered capital, revenue scale, and asset-liability ratio | Matches the fixed fields and unit requirements of due diligence reports, ensures model output can be directly mapped to business fields |
recallSourceInterval | Tiered by data source type: financial report data uses 3 days, industrial and commercial data uses 1 day, research report data uses 1 hour | Adapts to the update frequencies of different data sources, ensures timeliness of recalled data |
parseChunkSize | 1000–1500 characters | Balances long-text parsing accuracy and context window usage, adapts to the paragraph length of due diligence drafts |
rerankTopN | Top 8 entries | Reduces redundant recall of unstructured drafts, focuses on highly relevant due diligence content |
fieldAssignmentCheck | Enabled | Validates the match between model output fields and preset templates, avoids missing field assignments or format errors |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: Model analysis produces correct text, but no values are assigned to business fields. Cause: Mandatory validation rules for
structuredOutputSchemaare not configured, and model output does not strictly match the preset field structure. - Symptom: No retrieval results appear after enabling the reranking model in the application, while knowledge base testing functions normally. Cause: No matching parameters are configured between
rerankTopNand the number of recall entries in the application, causing reranked results to be filtered out. - Symptom: The model fails to start for an extended period after deployment. Cause: The
maxContextparameter is not adjusted based on the average length of due diligence data, leading to insufficient context window and triggering a timeout.
How to confirm correct configuration
- Upload a standard due diligence draft document, trigger model analysis, and verify that output fields exactly match the preset template.
- After enabling the reranking model, compare the number of results from knowledge base testing and in-application retrieval to confirm the reranking logic is working correctly.
- Simulate update scenarios for different data sources to verify that the time range of recalled data aligns with configuration requirements.
- View model invocation logs to confirm that structured output fields have no format errors or missing assignments.
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.