Knowledge Base Retrieval and Recall for Supplier Audit

Data Characteristics for This Category

Data for supplier audits during clinical trial pre-screening primarily comes from audit reports, supplier qualification documents, Standard Operating Procedures (SOPs), contracts, quality management system documents, and historical audit records. These documents are typically in PDF, Word, or scanned image formats. Update frequencies vary; qualification documents and contracts might update annually or biennially, while audit reports are generated with each audit activity. Document structures often include fixed fields in audit reports such as executive summaries, audit scopes, findings, recommendations, and supplier responses. SOPs detail specific processes chapter by chapter. Common fields and units include "non-conformance level" (e.g., Major, Minor), "completion date" (YYYY-MM-DD), and "responsible person." Some metrics may involve "equipment calibration cycle" (e.g., 12 months) or "deviation handling timeliness" (e.g., 7 days).

Constraints on Knowledge Base Retrieval and Recall

Supplier audit documents combine structured and unstructured data, posing challenges for knowledge base chunking strategies. Audit report findings and recommendations are often short sentences or lists, while SOPs can contain lengthy process descriptions. This requires flexible chunking to ensure critical information integrity and prevent individual chunks from becoming too long and diluting core semantics. Historical audit records update infrequently but show significant version iteration, requiring effective document version management in the knowledge base to ensure retrieval results are current. The large volume of documents, due to multiple suppliers and audit batches, demands high retrieval performance and precise recall. Semantic recognition of specific fields like "non-conformance level" and "deviation handling timeliness" requires a higher degree of domain adaptation from the embedding model.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`300–500 characters`	Balances short findings in audit reports with process descriptions in SOPs, maintaining semantic completeness.
`Overlap Length`	`50 characters`	Ensures contextual continuity across chunks, especially when extracting sequential process steps or audit findings.
`Recall Count`	`8–12 items`	Expands the recall scope to cover multi-faceted audit findings and SOP clauses, while avoiding excessive irrelevant information.
`Similarity Threshold`	`0.75–0.85`	Adapts to scenarios where documents have similar terminology but different semantics, balancing recall rate and accuracy, reducing false positives.
`Rerank Return Count`	`3 items`	Improves the ranking of the most relevant results after initial recall using a reranking model, focusing on core information.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Accommodates large audit reports or SOP files containing many scanned images, ensuring successful uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accounts for the time required for OCR of scanned documents and parsing complex PDFs, providing sufficient processing time to prevent timeouts.

Common Pitfalls

When uploading large audit reports or SOPs with complex tables, the system displays "File processing failed" or "Parsing timeout." This happens when UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS parameters are not adjusted, causing the file to exceed limits or parsing time to be insufficient.
Retrieval results contain many generic terms or background information irrelevant to the query, failing to precisely locate specific audit findings or non-conformances. This occurs when the Similarity Threshold is set too low, leading to the recall of broadly related content.
After a user uploads audit-related documents from Feishu, the knowledge base does not automatically synchronize content updates, requiring manual operation. This happens when automatic synchronization for the Feishu knowledge base is not configured or enabled, leading to outdated content.

Verification Steps

Select multiple representative audit reports and SOP files, including different formats and sizes. Upload them to the knowledge base and verify that all files parse successfully and display correct text content.
Formulate multiple natural language queries for specific audit findings, SOP process steps, or supplier qualification requirements. Verify that the retrieval results accurately recall document fragments containing this key information and rank them highly.
Test queries of varying complexity. Observe the knowledge base's response time performance to ensure acceptable speed even with large document volumes and high concurrency.
Check that document version management for the same supplier or audit batch is correct within the knowledge base. Ensure that the latest version is prioritized during retrieval and that historical versions can be traced.

The values provided are common starting points and should be measured 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.