Data Characteristics for This Category
Deviation and CAPA (Corrective Action and Preventive Action) product data originates from quality management systems. Sources include incident reports, investigation records, root cause analyses, and corrective/preventive action plans with their execution verifications. This data typically exists as structured or semi-structured documents, such as investigation reports, risk assessment reports, and change control records. Update frequency is real-time, tied to incident occurrence, investigation progress, and action completion. Document structures include fields like incident number, occurrence time, description, classification, root cause, action content, responsible person, planned completion date, actual completion date, and verification results. Field values are often enumerations, datetime objects, text descriptions, or identifiers linking to other records.
Constraints from "Tool Calling and Plugins" for These Features
Deviation and CAPA data is real-time and highly structured. This requires tool calling and plugins to respond quickly to queries and accurately parse structured information. Data accuracy is critical due to quality management implications, demanding high confidence and traceability from tool results. Text description fields (e.g., deviation description, root cause analysis) often contain specialized terminology and contextual information, requiring strong semantic understanding from the model. Action plans and execution verification may involve data interaction with other systems (e.g., LIMS, MES). This necessitates cross-system integration capabilities for tool calling to retrieve or update relevant statuses. For critical fields like dates and responsible persons, plugins must support precise filtering and matching to ensure query accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_timeout_seconds | 60 seconds | CAPA queries often aggregate data from multiple systems; allow sufficient API response time. |
max_tokens_output | 1024 | Deviation and CAPA reports can contain detailed root cause analyses and action descriptions, requiring a larger output space. |
retrieval_top_k | 5 | Ensure sufficient relevant documents are recalled in complex queries to improve result completeness. |
similarity_threshold | 0.75 | Deviation event descriptions use specialized terminology; a higher similarity threshold helps focus on relevant results. |
stream_response | true | For queries requiring aggregation of multi-source information, streaming responses improve user experience. |
enable_follow_up_questions | true | The deviation handling process is complex; allow users to ask follow-up questions for detailed queries and deeper analysis. |
Three Common Mistakes
- Tool calling returns empty or incomplete data: The API parameter mapping in the plugin configuration is inaccurate, causing upstream systems to fail to recognize or return errors.
- Date or responsible person information in query results does not match: The model fails to correctly identify date formats or personnel names when parsing user intent, preventing accurate transmission of query conditions to the tool.
- When faced with complex queries, the model fails to call the correct tool or combine tools: Tool descriptions are unclear, or inter-tool dependencies are not fully reflected in model training, preventing the model from effectively planning multi-step operations.
How to Verify Configuration
- For typical deviation queries (e.g., "query severe deviations from last week," "XX department's CAPA action progress"), verify that tool calling returns correct and complete relevant records.
- Randomly select key fields from CAPA records (e.g., root cause, planned completion date). Ask questions to verify if the model can accurately extract and present this information from the tool's returned results.
- Simulate cross-system query scenarios, such as requesting quality data for a batch product associated with a deviation. Verify that the tool chain correctly triggers and aggregates information from different data sources.
The values provided 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.