Data Characteristics for This Category
Data for OA process initiation in the biopharmaceutical sector typically originates from internal ERP, CRM, or custom business systems. This data has a relatively low update frequency, mostly created on demand or synchronized daily. Process document structures are primarily structured or semi-structured, such as leave requests, expense reports, or procurement applications, and usually contain fixed form fields. Field types are diverse, including text (e.g., reason for application, approval comments), dates (e.g., application date, start date), numerical values (e.g., amount, quantity), and enumerations (e.g., approval status, process type). Numerical fields may have specific units, such as currency units (Yuan) or quantity units (boxes, bottles). Date fields must adhere to consistent date format standards, such as YYYY-MM-DD or YYYY/MM/DD.
Constraints Imposed by These Characteristics on Workflow Orchestration
The relatively fixed structure and low update frequency of OA process data mean that real-time data source requirements are generally not high for workflow orchestration. A combination of scheduled synchronization and event-triggered updates can be used. Structured form data simplifies model understanding and field extraction, reducing the complexity of unstructured text processing. However, enumeration types and numerical fields with units require accurate identification and conversion by data parsing and validation nodes within the workflow to avoid ambiguity. For example, the approval process needs to precisely determine if an approval amount exceeds a limit, which requires the model to recognize the numerical value and its unit. Additionally, maintaining and updating process statuses requires precise API calls to backend systems to ensure data consistency.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 10 | OA process context is typically short. This retains core process information and avoids irrelevant interference. |
Chunk size (Segment Length) | 500 characters (characters) | Process form fields are usually short. This avoids excessive segmentation that could lead to semantic loss, while balancing retrieval efficiency. |
Recall count (Recall Count) | Top 5 entries (top 5 items) | Process-related information is concentrated. A small number of precise recalls is sufficient to cover key decision points. |
Similarity threshold (Similarity Threshold) | 0.75 | This ensures recalled process templates or historical approval comments are highly relevant, reducing misjudgments. |
PARSE_FILE_TIMEOUT_SECONDS | 60 seconds (seconds) | OA attachments (e.g., contracts, invoices) are typically moderate in size. This time is sufficient for parsing completion. |
maxToken | 2000 | This handles complex processes or approval comments, providing sufficient generation length to ensure complete responses. |
Three Common Mistakes
- AI node reply content lacks context association: This occurs when
maxContextis set too low or the context transfer mechanism in the workflow is misconfigured, leading to insufficient effective historical conversation information being received by the model. - Model fails to correctly understand file content after upload: This may be due to the file parsing component failing to correctly identify the file type, or the parsed text not being effectively passed to the large model, preventing the model from acquiring structured information from the file.
- Some components fail to execute when a workflow contains multiple code execution components: This is typically caused by incorrect mapping of input/output parameters between components, or environment variables not being correctly configured and referenced across all components, preventing the code from accessing necessary data or credentials during runtime.
How to Confirm Correct Configuration
- Submit a typical OA process initiation request. Check if the AI assistant accurately identifies the process type, key fields, and values, and generates a response that aligns with business logic. Also, verify if the response includes core fields such as
process numberandapplication amount. - Upload an OA attachment containing complex tables or multi-page text. Observe the workflow logs to confirm if the file parsing node successfully returns text content. Further check if the AI assistant accurately understands the attachment content, for example, if it can extract
contract amountorsupplier name. - Simulate abnormal data input or missing key parameters in the workflow. Verify if the error handling branch is correctly triggered and if the system returns the expected error code or prompt message, such as
400 Bad Requestormissing parameter. - For processes involving external system calls (e.g., creating an approval form), review the workflow execution logs. Confirm if the API call was successful and if the external system received and processed the request as expected, for example, by checking for a
200 OKstatus code from the external system.
The values provided are common starting points. Measure them against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.