Data Characteristics
Batch record review in clinical trial pre-screening primarily involves verifying original records generated during manufacturing, inspection, and release. Data sources are diverse, including scanned paper batch records, electronic records exported from Manufacturing Execution Systems (MES), deviation reports and change control documents from Quality Management Systems (QMS), and inspection reports from Laboratory Information Management Systems (LIMS). These documents typically exist in PDF, Word, Excel, or structured XML formats. Data update frequency is low, usually archived after each batch production. Document structures are complex, containing numerous tables, charts, handwritten annotations, and specialized terminology. Key fields include batch number, production date, expiration date, critical process parameters, inspection results, operator signatures, and deviation descriptions. Units involve mass (kg, mg), volume (L, mL), time (h, min), temperature (℃), and mixed unit systems may be present.
Constraints on "Reference and Traceability" Imposed by These Characteristics
The complexity of batch record data places specific demands on reference and traceability. First, diverse and heterogeneous data formats require the knowledge base to have robust document parsing capabilities, especially OCR for scanned documents and field extraction for structured data. Second, table and chart content within documents must be accurately indexed to provide precise references in responses. The low data update frequency means knowledge base construction needs to focus on the completeness of historical batch data and ensure version control. The mix of specialized terminology and measurement units requires the model to associate with the correct context when understanding semantics, avoiding incorrect references due to unit conversion or terminology ambiguity. Additionally, due to the legal validity of batch records, traceability must be precise to the specific page number or section of the original document to meet compliance requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | A single operation step or inspection result in a batch record typically falls within this length, which helps maintain semantic integrity. |
Recall count (Recall Count) | Top 8–12 chunks | Complex batch record review involves multiple related checks, requiring more context to support judgment and traceability. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures the precision of recalled content, avoiding irrelevant or semantically similar but factually incorrect references. |
Rerank result count (Rerank Return Count) | Top 5 chunks | Reranking further focuses on the most relevant paragraphs, improving answer quality and reference accuracy. |
ENABLE_OCR | True | Handles a large volume of scanned and image-format batch records, ensuring text content can be recognized and indexed. |
MAX_FILE_SIZE_MB | 200 MB | Batch record files, especially PDFs containing high-resolution scans, are often large. |
Three Common Pitfalls
- Reference links returned by the model display "404 Not Found": This usually occurs due to an expired document storage path or link configured in the knowledge base.
- When asked about a specific key parameter in a batch record, the model fails to cite relevant data: This may be because the document parsing did not correctly extract specific fields from tables or charts.
- When asked a question in Chinese containing specific technical terms, the model does not cite the corresponding content in the English original: This could be because the knowledge base indexing did not perform cross-language semantic association or vocabulary mapping.
How to Verify Configuration
- Upload multiple batch record PDF files containing tables and charts. Verify if the model can cite specific data within tables and descriptions of charts in its responses.
- For a scanned document containing handwritten annotations, ask a question about the annotation content. Confirm that the model can recognize and cite it via OCR.
- Ask questions using batch record files of different batches and formats (PDF, Word, XML). Check if the references are precise to the page number or section of the original document.
- Construct questions involving unit conversions and mixed technical terminology. Check if the model can accurately understand the semantics and cite the correct original text.
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.