What the data for this category looks like
Data sources for chemical pharmaceutical smart due diligence include National Medical Products Administration approved public documents, publicly available clinical trial reports from pharmaceutical companies, compound patent texts from patent databases, and annual operating reports. Data update timing aligns with clinical trial phases, approval announcements, and annual report disclosure cycles.
Document structure mixes structured fields and long-form text. Structured fields include compound generic name, brand name, target of action, and indication scope. Units include nmol/L for activity data and cases for clinical trial sample sizes. Long-form text sections cover trial design details and safety monitoring records, with individual documents reaching tens of thousands of characters.
Constraints imposed on workflow orchestration by these characteristics
The mixed structure of chemical pharmaceutical due diligence data requires workflows to include both structured field extraction nodes and long-text segment parsing nodes. The large volume of long text in individual documents requires workflows to set reasonable segment length thresholds to avoid exceeding model context limits during single processing.
Dispersed multi-source data requires workflows to integrate multiple nodes to pull data from different channels, and match each data source’s update cycle to set scheduled execution logic. Specialized unit rules for fields require workflows to add unit validation steps to ensure units for activity data, sample sizes, and other fields align with industry standards.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to single-segment content after long pharmaceutical document segmentation, matches context window limits of mainstream large language models |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for long clinical trial reports and patent documents, prevents mid-execution timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading packaged due diligence files containing multiple attachments for chemical pharmaceuticals |
segmentLength | 1000–1500 characters | Balances accuracy of long-text parsing and context utilization, prevents model understanding errors caused by overly long single segments |
fieldValidationEnabled | Enabled | Validates format compliance of activity data units and sample size units |
maxRetries | 3 retries | Addresses interface fluctuations when pulling multi-source data, reduces probability of single node execution failure |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: A single user accesses the workflow multiple times. The context of the first AI model call only retains 1 round, and no historical conversation retention is present in subsequent calls. Cause: The context configuration is not bound to the session ID, or the session context storage rules do not adapt to the round retention requirements for multi-round calls.
- Phenomenon: After enabling the workflow file upload node, calling the document parsing tool to resolve the uploaded file address returns a
400 Bad Requesterror. Cause: TheCUSTOM_READ_FILE_URLenvironment variable is not configured correctly, or port permissions for file reading are not enabled during local deployment, preventing the parsing tool from accessing the local path of the uploaded file. - Phenomenon: The classification results output by the workflow question classification node do not match the actual compound patent category. Cause: Specialized fields from the patent database are not used as input data sources for the AI model, and only generic text content is used for classification.
How to confirm correct configuration
- Upload a standard chemical pharmaceutical clinical trial report, review the segmented parsing results of the workflow, confirm that the segment length meets preset requirements, and adjust parameters via the corresponding configuration items.
- Trigger multi-round call tests, check whether the context retention rounds of AI model calls meet the configuration rules, and adjust via context-related configuration items.
- Simulate multi-source data pulling, verify whether the field validation node blocks input with incorrect formats, and adjust logic via field validation-related configuration items.
- Upload a large packaged file, confirm that the upload node does not trigger size limits, and adjust thresholds via upload size-related configuration items.
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.