Data Characteristics in This Category
Batch record review data primarily originates from batch production records and batch inspection records in pharmaceutical manufacturing. These documents are typically structured. They contain production process parameters, material batch numbers, inspection results, operator signatures, and deviation records. Data updates align with the drug batch production cycle, usually daily or weekly. Document formats are mainly scanned PDFs or electronic spreadsheets (e.g., .xlsx). Some are structured XML or JSON. Field types are diverse, including numerical values (e.g., temperature, pressure, content), text (e.g., operating procedures, deviation descriptions), date/time (e.g., production date, inspection date), and boolean values (e.g., pass/fail). Units strictly follow pharmacopoeia or internal standards, such as ℃, kPa, mg/L, and pH values.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The structured nature of batch record documents requires parsers to accurately identify data types and semantics in different regions. The OCR quality of scanned documents directly impacts text extraction accuracy, which in turn affects subsequent chunking accuracy. Spreadsheets and structured documents must retain their inherent row and column relationships; avoid flattening, which leads to information loss. High update frequency demands automated and efficient document parsing processes to accommodate continuous new data. The strictness of numerical fields and units means that chunking cannot arbitrarily truncate data. A complete numerical value and its unit must be preserved as a semantic unit. Unstructured text content, such as deviation records, requires more flexible chunking strategies to capture key facts and causal relationships.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Balances information density of a single chunk with retrieval efficiency. Accommodates both long descriptions and short records in batch records. |
Chunk Overlap Length | 80 characters | Ensures contextual continuity and prevents critical information from being split by chunk boundaries, especially in process descriptions and deviation analyses. |
OCR Preprocessing | Enable | Batch records often include scanned documents. High-quality OCR is fundamental for text extraction and ensures accurate recognition of numbers and text. |
Table Parsing Mode | Smart Recognition | Batch records contain a large amount of tabular data. Smart mode better preserves table structures, facilitating subsequent semantic understanding. |
Parsing Timeout | 600 seconds | Handles parsing of large or complex batch record documents. Prevents parsing failures due to excessively large files or time-consuming OCR. |
Max File Size | 200 MB | Batch records may contain numerous images or scanned pages. Setting a reasonable upper limit processes most files while preventing system overload. |
Three Common Mistakes
- Table data is flattened into plain text after document parsing. This leads to the loss of relational information between rows and columns. This occurs because parsing configurations do not adequately consider table structures.
- Uploaded Excel file content cannot be read correctly, or chunking is chaotic. This manifests as a lack of understanding of tabular data in question-answering results. This occurs because the table parsing mode is not specified or is improperly configured.
- Numerical fields containing special symbols or units are truncated after chunking. This prevents obtaining complete numerical values during queries. This occurs because the chunking strategy does not adequately consider the atomicity of fields, for example, incorrectly splitting
10.5 mg/Linto10.5andmg/L.
How to Verify Correct Configuration
- Upload different types of batch record documents (PDF, Excel, scanned images). Check if the parsed text preview is complete and clearly structured, especially if table content retains its original layout.
- For critical fields in batch records, such as production date, batch number, and inspection results, use question-answering tests to verify if the model can accurately extract and understand this information. Compare the results with the original documents.
- Upload batch records containing complex deviation descriptions and operating procedures. Check if question-answering results can coherently explain the sequence of events and causes. This assesses the chunking strategy's ability to retain the semantics of long texts.
Note: The values provided are common starting points. Measure them 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.