Data Characteristics
Supplier audit data for clinical trial pre-screening primarily comes from supplier qualification documents, quality management system documentation, production process records, equipment calibration reports, personnel training records, and previous audit reports. These documents are typically in PDF, Word, or Excel formats. Their structure varies, containing extensive unstructured text descriptions and structured data in tables. Examples of structured data include equipment lists, personnel qualification lists, and batch records. Update frequency depends on audit cycles and supplier qualification changes, occurring quarterly, semi-annually, or annually. Fields and units in these documents are highly specialized. Examples include equipment model, serial number, calibration date, expiration date; personnel education, major, training courses, certificate numbers; and quality indicator ranges and test methods.
Constraints on Document Parsing and Chunking
The heterogeneous nature of supplier audit documents challenges parsing. Unstructured text requires precise semantic understanding to identify critical risk points and compliance information. Table data requires the parser to accurately identify table boundaries, rows, and columns, and extract cell content. This prevents data misalignment or omission. Specialized fields and units necessitate specific entity recognition capabilities to correctly associate values and units. For example, "temperature: 25℃" and "25 degrees" are semantically equivalent but require unified processing during parsing. The document update frequency dictates the timeliness of knowledge base content. This requires support for incremental updates and version management to avoid using outdated information. Additionally, many scanned documents and image formats increase the need for OCR, whose accuracy directly impacts subsequent parsing quality.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances semantic completeness and recall efficiency. Avoids overly long segments that cause information redundancy or overly short segments that lack context. |
Chunk overlap | 100 characters | Ensures critical information spanning segments is captured, especially when describing supplier qualifications or quality systems. |
Parsing Strategy | Smart Chunking，Priority Table Parsing | Audit documents have a high proportion of table data. Prioritizing table parsing improves the accuracy of structured information extraction. |
OCR Recognition Threshold | 0.85 | Balances recognition accuracy and recall rate. Reduces interference from low-quality OCR text in subsequent matching. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates large audit reports or documents with complex tables, providing sufficient file processing time. |
maxContext | Calibrate by actual measurement，No Less Than 4000 token | Ensures complete context for key information like supplier qualifications and quality systems when answering questions. |
Common Pitfalls
- Uploading large audit reports results in a "file processing timeout" error. This occurs when the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is too low to handle the computational load required for file parsing. - Supplier equipment lists or personnel qualification tables in the knowledge base appear corrupted, with mismatched fields and values. This is due to inaccurate table structure recognition during document parsing, leading to incorrect cell content extraction.
- Answers to questions about certain suppliers lack critical information, such as "Does the supplier have GMP certification?" This can happen if document chunking is too granular, splitting relevant certification information across different knowledge chunks, preventing aggregation during recall.
Verification Steps
- Upload typical supplier audit documents. Inspect the generated segments in the knowledge base to ensure semantic completeness of key paragraphs (e.g., quality system descriptions, qualification certificate numbers).
- Use the knowledge base retrieval function. Query with table content from the document to verify that table data is correctly parsed and effectively recalled.
- Ask questions involving specialized terminology and numerical units from the document. Evaluate the accuracy of the answers to confirm that entity recognition and unit handling meet expectations.
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.