What data for this category looks like
Intelligent due diligence report data for investment platforms mainly comes from public listed company financial reports, industry regulatory agency disclosure documents, due diligence materials submitted by project parties, and third-party credit data sources. Update rhythms vary by data source type. Public financial reports update quarterly or annually. Regulatory disclosure documents sync in real time. Materials submitted by project parties update as needed. Documents usually include main qualification fields, three-year financial indicator fields, compliance penalty record fields, and risk rating fields. Financial indicators mostly use ten thousand yuan as the unit. Compliance records use counts as the unit. Risk ratings use standardized level identifiers.
What constraints do these characteristics impose on workflow orchestration
The multi-source and scattered nature of due diligence data for investment platforms requires configuring multi-data-source parallel pull nodes in workflows, to avoid incomplete due diligence reports caused by missing data from a single source. Differences in update rhythms across data sources require setting scheduled trigger and incremental synchronization nodes, only pulling updated data to reduce redundant computation. Fields have multiple unit types and standardized identifiers, so workflows need to add format verification nodes to conduct compliance checks on financial indicator units and risk rating formats. Long document structures require configuring segment splitting and merging nodes in workflows, to adapt to memory limits for large text processing.
How to set configurations
| Configuration Item | Recommended Value | Basis |
|---|---|---|
maxContext | 8000–12000 characters | A single due diligence report text usually contains multi-paragraph cross-page information, so sufficient context must be retained to correlate risk points and financial data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Due diligence reports often include multiple financial report attachments and industry research reports, with long parsing times. 600 seconds covers parsing needs for most large documents |
Recall count | Top 8–12 entries | Due diligence data is multi-source and fields are scattered. Too many recalls introduce redundant information from irrelevant projects, too few miss key compliance records |
Similarity threshold | 0.75–0.85 | Need to distinguish qualification differences between similar industry entities, avoid confusing and matching compliance penalty records of different entities |
Trigger Mode | Incremental trigger | Due diligence data update frequencies vary. Incremental trigger only processes new or updated data sources, reducing duplicate computation costs |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Due diligence reports often include multiple high-definition financial report scans and industry database export files, so large file upload support is required |
The parameter values given 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 specific analysis. It is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Workflow runs and displays a failure status, but the model test returns normal results, and there are response logs in the model backend. Cause: The workflow does not have an exception capture branch configured for the data source pull node. No retry logic triggers after data source pull failure, causing the process to stop directly.
- Phenomenon: After calling the workflow, the conversation log fields from yesterday and today are empty. Cause: The workflow does not have a conversation history persistence node configured, or the storage period of the node is set too short, causing historical data to not be effectively retained.
- Phenomenon: Node prompt words in the workflow do not execute in the expected order. Cause: Parallel nodes in the workflow do not have execution dependencies set, causing subsequent nodes to start before preceding nodes finish, leading to chaotic prompt word call order.
How to confirm correct configuration
- Manually trigger the workflow, check the return results of the data source pull node, confirm that all configured data source fields are included, and verify that field units match expectations.
- View the log panel after running the workflow, confirm that the execution order of each node matches the configured dependency relationship, with no skipped or prematurely executed nodes.
- Upload a standard due diligence report, check that the output of the parsing node completes structured splitting, with no garbled characters or missing fields.
- View the logs of the conversation history storage node, confirm that recent conversation records are all properly retained, with no empty fields.
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.