What the Data for This Category Looks Like
Data sources for professional services intelligent due diligence reports include enterprise industrial and commercial registration information, annual and quarterly financial reports, public announcements from regulatory authorities, industry-specific research reports, and more. Update cycles vary: industrial and commercial information is updated regularly per local regulatory requirements, financial reports are updated when their associated reports are released, and regulatory announcements are updated in real time. Document structures typically include four core modules: basic due diligence subject information, core business data, compliance risk points, and related party transaction details. Fields include the unified social credit code, revenue amount (unit: ten thousand yuan), risk level classification, due diligence completion date, and more. Single document lengths vary widely, so it is recommended to count or test with your own samples before finalizing settings.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources and inconsistent update cycles require that multi-turn dialogue contexts are limited to the current due diligence project only, to avoid interference from cross-project or irrelevant data. Long document lengths require controlling the context window size to prevent truncation or errors caused by exceeding the model’s token limit. Fields have clear unit and classification rules, so prompts must explicitly specify the format and units of extracted fields to avoid the AI mixing up business data of different subjects. Real-time updated regulatory announcements require that the latest release time of data is verified synchronously during dialogue to ensure the timeliness of due diligence content.
How to Configure the Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single professional services due diligence reports often exceed 5000 characters, so sufficient context must be retained to support multi-turn follow-up questions while avoiding context overflow |
recallTopK | Top 6–8 entries | Due diligence report-related data is scattered across multiple documents, so enough entries must be recalled to cover core fields such as risk points and financial report data |
similarityThreshold | 0.72–0.78 | Filter low-relevance industry general information, retain financial reports and regulatory records directly related to the due diligence subject |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Due diligence report documents often include multi-page compliance attachments, so sufficient time is needed to complete full-text parsing |
clearThinkTag | Enabled | Clean up thinking tags in AI output to avoid mixing them into the final due diligence report content |
customUid | Bind the unique identifier of the due diligence project | Differentiate session histories of different due diligence projects to avoid cross-project context confusion |
promptTemplate | Customize according to the due diligence field list | Explicitly specify the required fields, units and context scope to meet the structured output requirements of professional services due diligence reports |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test with your own samples before finalizing.
Three Common Mistakes
- Returning full session history when calling the API, without limiting results to entries matching the specified customUid. This occurs because customUid is not bound as a retrieval dimension in session storage configuration, or the parameter is not included during retrieval for filtering.
- The code node successfully cleans think tags during debugging, but thinking content remains in formal operation. This occurs because automatic think tag cleanup is not enabled in the AI dialogue node configuration, or the code node logic does not adapt to the output format of the production environment.
- Irrelevant historical context interfering with multi-turn dialogue related to the current due diligence subject. This occurs because the context association scope is not limited, or the current due diligence project identifier is not bound during recall.
How to Verify Correct Configuration
- Initiate a test dialogue with a preset test customUid, call the session history interface, and verify that the returned results only include dialogue records associated with that customUid.
- Upload a standard due diligence report document, trigger multi-turn follow-up questions, and check that the field format and units in the AI output meet preset requirements, with no irrelevant content present.
- Run a workflow that includes a code node, view the final output result, and confirm that no think tag content remains.
- Adjust the
similarityThresholdvalue to verify that the relevance of recall results changes as expected.
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.