Knowledge Base Retrieval and Recall for Deviation and CAPA

Data Characteristics

Deviation and Corrective and Preventive Action (CAPA) data in clinical trial pre-screening primarily originates from internal quality management systems, audit reports, investigation records, root cause analysis documents, and approved CAPA plans. These documents are typically in PDF, Word, or structured database record formats. Data update frequencies vary; minor deviations or localized CAPAs may update weekly, while CAPA plans and progress for significant systemic issues might update monthly or quarterly. Document structures usually include fields such as event description, date of occurrence, impact assessment, root cause, corrective actions, preventive actions, responsible person, completion deadline, and status. Some data may contain images, charts, or scanned documents, requiring OCR for text extraction. Field content is mostly textual descriptions, alongside standardized information like dates, project numbers, and responsible person names.

Constraints on Knowledge Base Retrieval and Recall

The heterogeneous nature, strong textual descriptiveness, and semi-structured characteristics of deviation and CAPA data impose specific requirements on knowledge base retrieval and recall. First, documents contain numerous technical terms and abbreviations, demanding precise matching or semantic understanding to improve recall accuracy. Second, text fields like event descriptions and root cause analyses vary greatly in length and contain critical causal relationships, requiring robust text segmentation strategies to avoid splitting key information. Unidentified text in scanned documents and images can lead to retrieval omissions. Furthermore, the dynamic update nature of CAPA plans necessitates an efficient incremental update mechanism for the knowledge base to ensure timely retrieval results. Varying standardization levels across fields require flexible configuration for field-based filtering and sorting, such as fuzzy matching for the "responsible person" field.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`800-1200 chars`	Accommodates the length of deviation and CAPA descriptions and analysis text, ensuring contextual completeness.
`Overlap Length`	`150 chars`	Maintains semantic coherence at chunk boundaries, preventing loss of critical information.
`Recall Count`	`Top 8`	Covers multiple potentially relevant deviation or CAPA records, increasing recall rate.
`Similarity Threshold`	Calibrate by test	Balances precision and breadth of recall; adjust based on actual data.
`Rerank Return Count`	`Top 5`	Reranks initial recall results to improve the ranking of core outcomes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses parsing time for large or complex documents (e.g., audit reports).

Common Pitfalls

The knowledge base training remains in "training" or "rebuilding" status for extended periods, preventing index switching. This typically occurs due to file parsing timeouts or stuck segmentation tasks, especially when processing PDF documents with numerous images or complex tables.
After a query, no knowledge base files are cited. This often happens when Similarity Threshold is set too high, preventing slightly less relevant document segments from being recalled, or when document segmentation is too fine-grained, leading to individual segments lacking sufficient semantic information.
In context citations, document formats are not rendered correctly as Markdown. This usually results from the knowledge base not correctly recognizing or converting the original stored document format, preventing the application of appropriate rendering rules during display.

Verification Steps

Upload representative deviation reports and CAPA plan documents. Check the knowledge base document status to ensure all documents have been successfully parsed and segmented.
Conduct tests using a series of simulated clinical trial pre-screening questions. Verify if the recall results include the expected deviation and CAPA records and evaluate the relevance of the recalled items.
Examine the distribution of Similarity scores in the recall results. Combine with manual evaluation to determine the reasonableness of the Similarity Threshold, ensuring highly relevant content is effectively recalled.
Validate document segments in context citations. Confirm their content completeness, correct format rendering, and inclusion of key field information.

Note: The values provided are common starting points. Measure them against your own samples to determine optimal settings.

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.