Data Characteristics for This Category
Deviation and Corrective and Preventive Action (CAPA) registration documents originate from an enterprise's quality management system records. Data updates are typically event-driven: they occur when a deviation happens or a CAPA process progresses. Document structures primarily consist of structured and semi-structured data. This includes deviation reports, investigation reports, root cause analyses, CAPA plans, implementation records, and effectiveness verification reports. Core fields include deviation number, occurrence time, involved product/batch, deviation description, root cause, CAPA type, planned completion date, actual completion date, responsible person, and verification results. Field values are often text descriptions, dates, numbers, and enumeration types.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The event-driven update mechanism for deviation and CAPA data requires workflows to flexibly respond to external triggers. This can involve receiving new data via API interfaces or periodically scanning specific storage locations. The semi-structured nature of these documents means that information extraction requires a combination of Named Entity Recognition (NER) and structured content parsing to accurately capture key fields. Numerous text description fields, such as deviation descriptions and root cause analyses, increase Natural Language Processing complexity. This demands high-precision text summarization and key information extraction capabilities. Referencing and associating historical CAPA data is a common requirement. This necessitates efficient associative querying and context management capabilities within the knowledge base to support decision-making and avoid duplication in workflows.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 4000 | Ensures sufficient contextual information for analysis when processing a single deviation report and related CAPA documents. |
Chunk size (Segment Length) | 800–1000 characters (characters) | Considering the length of text fields like deviation descriptions and root cause analyses, a moderate segment length helps maintain semantic integrity. |
Recall count (Recall Count) | Top 10 entries (top 10 entries) | Recalls enough historical deviation or CAPA cases from the knowledge base for reference or pattern recognition. |
Similarity threshold (Similarity Threshold) | 0.75 | A relatively high threshold aims to improve the relevance of recall results and reduce interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 entries) | After re-sorting recall results, focuses on the most relevant few entries, improving workflow processing efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds (seconds) | Allows ample file parsing time, considering some deviation documents may contain large amounts of text or images. |
Three Common Pitfalls
- Key field values are not extracted during code execution, such as
resultTimesortrafficFlowCounts. This typically occurs due to inaccurate output variable path configuration or a mismatch between the model's output format and expectations. - Knowledge base search results are empty, even if relevant information exists. This might be due to an inappropriate document segmentation strategy, causing key information to be split, or a
Similarity threshold(Similarity Threshold) set too high, failing to recall valid results. - Historical variables are lost after variable updates. This indicates a lack of clear storage and management mechanisms for global or persistent variables in the workflow design, leading to variable re-initialization with each execution.
How to Verify Correct Configuration
- Simulate submitting a complete deviation report. Check if the workflow accurately identifies and extracts core fields such as deviation number, occurrence time, deviation description, and root cause.
- Execute test cases involving knowledge base queries. Verify if the knowledge base recalls relevant historical CAPA cases for a specific deviation scenario. Check if the relevance of the recalled results meets expectations.
- Run workflows containing complex logic. Observe if variable transmission and updates between different nodes align with design requirements. Specifically, verify correct storage and retrieval of persistent variables.
These values are common starting points and should be measured against specific 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.