Data Characteristics
Supplier audit documents in biomedical R&D primarily originate from audit reports, quality agreements, supplier qualification files, manufacturing batch records, and change control records. These data sources update relatively infrequently, typically quarterly or annually, but update immediately for significant changes. Document structures are semi-structured, containing extensive non-standardized text descriptions, tabular data, and embedded charts. Key fields include supplier name, audit date, non-conformance description, corrective actions, completion date, batch number, product name, and relevant quality standards (e.g., GMP, ISO 13485). Units involved include temperature (℃), pressure (kPa), and concentration (mg/mL).
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The semi-structured nature of supplier audit documents requires the knowledge base to recognize and preserve contextual relationships between tables and key descriptions during chunking. This prevents information fragmentation. A lower update frequency allows for deeper preprocessing and vectorization during data import, but requires attention to incremental update strategies. Specialized terminology and abbreviations within documents, such as "CAPA" (Corrective and Preventive Actions) and "OOS" (Out-of-Specification), demand strong tokenization and semantic understanding from the retrieval model. The mix of various units requires the model to identify and normalize units to support queries based on numerical ranges. The presence of embedded charts means pure text retrieval might not cover all information, necessitating consideration of multimodal information integration.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Balances the completeness of paragraphs in audit reports with relevance during recall, avoiding noise from overly long contexts. |
Chunk overlap (Chunk Overlap) | 50–100 characters | Ensures critical information spanning across chunks does not lose context due to splitting, especially where audit conclusions link to appendices. |
Recall count (Recall Count) | Top 8–12 items | Given the complexity of audit reports, increasing recall quantity improves the probability of discovering potentially related non-conformances. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | For audit records with highly similar professional terminology, a higher threshold ensures precision of recall results. |
Rerank result count (Reranked Return Count) | Top 5 items | After initial recall, a reranking model further filters for audit records or corrective action suggestions most relevant to the query intent. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Audit reports can contain extensive content; extending parsing time ensures complete processing of large documents. |
Common Mistakes
- Empty knowledge base information extraction: This typically occurs when the document parser fails to correctly identify key fields or table structures within audit reports, preventing content from being effectively chunked and indexed.
- Confused recall of similar issues: For example, "Supplier A's CAPA process" is confused with "Supplier B's CAPA process." This happens when the vector model fails to effectively distinguish entity information, or when sufficient entity context is not preserved during chunking.
- Inability to output existing images from documents: This indicates that the knowledge base processing pipeline fails to extract embedded images (e.g., non-conformance site photos) from audit reports, perform OCR, or associate them with text content.
How to Verify Configuration
- Upload a typical audit report. Check if the chunked content in the knowledge base preview is complete, especially if tabular data and key conclusions are correctly identified.
- Query for specific non-conformances or corrective actions. Observe if the recall results include relevant suppliers, audit dates, and specific batch information. Check if the
Recall count(Recall Count) meets expectations. - Use queries containing specialized terminology and abbreviations (e.g., "OOS handling process"). Verify the precision of recall results and evaluate if the
Similarity threshold(Similarity Threshold) effectively filters irrelevant information. - Test queries involving numerical ranges for audit results (e.g., "temperature excursion range"). Confirm if the system correctly extracts and matches corresponding numerical values from the document.
The values provided are common starting points and should be measured against the reader's 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.