What the data for this category looks like
Data sources for general comprehensive intelligent due diligence reports include self-submitted due diligence materials from enterprises, publicly disclosed filing documents from regulatory authorities, and verification data from third-party compliance service providers. Updates are triggered by major operational or compliance events of the due diligence target, with no fixed schedule. Each single document has a multi-chapter structure, with fields including basic subject information, operational data, risk inspection items, compliance records, related party transactions, and more. Some fields have clear numerical identifiers, such as operational data fields marked with specific amount units, and compliance record fields marked with rectification count units. Document lengths vary widely; some long documents include cross-chapter associated data descriptions.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Decentralized data sources and non-standardized structures require multi-turn dialogue to support context association across multiple sources, to avoid information loss caused by limitations of a single data source. No fixed update schedule means prompts must explicitly require the large model to prioritize the latest submitted due diligence data, and avoid calling cached old versions. Diverse field types without unified templates require multi-turn dialogue to support targeted field-based follow-up questions. Prompts must explicitly specify the range of fields to extract, to prevent the large model from generating content unrelated to the due diligence scenario. Long document lengths require the dialogue context window to adapt to multi-chapter content, to avoid losing critical information due to context truncation.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
recallTopK | Top 8-12 entries | General comprehensive due diligence reports have many fields and scattered content. Sufficient recall volume covers dispersed information across relevant chapters |
similarityThreshold | 0.72-0.80 | Balances retrieval precision and recall completeness, avoids missing associated data under non-standardized structures caused by overly high thresholds |
maxContext | 12000-15000 characters | Adapts to long-text context of multi-chapter due diligence reports, preserves historical follow-up logic in multi-turn dialogue |
chatHistoryMaxTurns | 6-8 turns | Controls dialogue context length, avoids model output deviation caused by excessive historical information, adapts to scenario requirements for targeted follow-up questions |
customSystemPrompt | "Only answer based on the provided due diligence report content, prioritize extracting numerical values and descriptions of specified fields" | Clearly defines the model's output boundaries, adapts to the non-standardized content structure of general comprehensive due diligence reports |
apiTimeout | 600 seconds | Adapts to time requirements of long document parsing and multi-source retrieval, avoids interrupting the dialogue process due to timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on independent samples before finalizing settings.
Three common mistakes
- Symptom: The knowledge base retrieval returns empty results during dialogue, but the retrieval works normally when tested in the knowledge base backend. Cause: Cross-source retrieval context association rules are not configured, or the
recallTopKvalue is set too low, causing historical follow-up questions in multi-turn dialogue to interfere with the matching logic of current retrieval. - Symptom: The
404 Invalid URL (POST /api/chat/completions)error is triggered during dialogue. Cause: The large model interface call address is not configured correctly, or interface permissions are not enabled, causing the dialogue process to fail to call the large model to generate replies normally. - Symptom: The workflow stops after the large model generates a reply, with no subsequent nodes triggered and the dialogue ends directly. Cause: The downstream node trigger rules for the dialogue process are not configured, or context parameters are set unreasonably, causing critical information to be truncated and unable to be passed to downstream workflow links.
How to confirm the configuration is correct
- Initiate multi-round targeted follow-up questions, ask for specified field content from different chapters in sequence, and check whether each retrieval can hit the information from the corresponding chapter.
- Simulate a data update scenario, manually modify the content of a document in the knowledge base, and verify whether the dialogue retrieval can prioritize returning the latest updated content.
- Check the dialogue operation logs, and confirm that the actual call values of core configuration items match the preset configuration values.
- Trigger a full-process workflow test, and verify whether the complete link from knowledge base retrieval, large model reply to downstream node execution runs normally.
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.