Data Characteristics
Supplier audit data in the biopharmaceutical sector primarily comes from audit reports, quality system documents, batch records, change control documents, and supplier qualification certificates. These documents are typically in PDF, Word, and Excel formats, containing both structured and unstructured text. Audit reports are usually updated every 1–3 years, while critical supplier qualification certificates may be updated annually.
Audit reports often have fixed sections, such as "Audit Findings," "Deviation Records," and "Improvement Plans." Key fields include supplier name, audit date, auditor, defect number, defect description, risk level, corrective actions, and completion date. Some fields contain specific industry terminology and abbreviations.
Constraints on Reference and Traceability
The specialized nature and periodic updates of supplier audit data impose specific requirements on reference and traceability. Defect descriptions in audit reports often rely heavily on context, necessitating a larger context window during retrieval. The validity period of supplier qualifications limits the timeliness of references; outdated references can lead to incorrect judgments.
Handling mixed document formats requires a system that can uniformly process and accurately parse text from different formats. Fields like risk levels of audit findings and corrective actions must be clearly indicated in references, allowing engineers to quickly locate critical information and avoid misinterpretations or omissions. Accurate identification of specific terminology and abbreviations is essential for precise traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000 token | Audit report defect descriptions often require a longer context for understanding, preventing information fragmentation. |
Chunk size (Segment Length) | 500 characters | Ensures a single segment can contain a relatively complete description of an audit finding or corrective action. |
Recall count (Recall Count) | Top 8 entries | Increases the number of recalled items to cover more potentially relevant audit findings or quality records. |
Similarity threshold (Similarity Threshold) | 0.78 | Clinical trial pre-screening demands high accuracy for audit information, requiring a slightly higher threshold. |
Rerank result count (Rerank Return Count) | Top 5 entries | Filters for the most relevant few items, improving the precision of the final output. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Provides sufficient parsing time for large PDF audit reports and batch record files. |
Common Pitfalls
- Output contains reference markers like
[1]. This occurs when the model retains the knowledge base's reference format during generation. Post-processing or adjusting the model's prompt is needed to remove these. - AI responses lack critical audit findings or risk level information. This may be due to
maxContextbeing set too small, preventing the model from acquiring complete contextual information. - References point to irrelevant, older versions of audit reports. This happens when the knowledge base is not updated promptly or the indexing strategy does not consider document validity dates, leading to the retrieval of expired data.
Verification
- Ask several questions about specific supplier audit findings. Check if the model accurately cites the corresponding audit report sections and verify the cited audit report version.
- For a report containing audit findings with various risk levels, ask questions about "high-risk defects." Check if the model can accurately identify and cite relevant content, and confirm that the risk level field is correctly parsed.
- Randomly select several different audit documents (e.g., PDF audit reports, Excel change records). Upload them to the knowledge base and ask questions. Verify that the system can correctly parse and retrieve information from different documents, and check if the cited file paths and page numbers are accurate.
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.