Data Characteristics
Rational drug use data originates from drug inserts, clinical guidelines, pharmacological and toxicological research reports, adverse event monitoring data, and internal experimental records from drug development. These documents have varying update frequencies. Drug inserts and clinical guidelines may update regularly with drug approvals or regulatory changes, while research reports are phase-specific. Document structures are complex, often containing numerous tables, charts, and unstructured text. Sections include indications, dosage and administration, contraindications, drug interactions, pharmacokinetics, and pharmacodynamics. Fields often involve drug names, dosage units (mg, ml, U), time units (h, min, day), effect indicators (AUC, Cmax), and various medical terms.
Constraints on Document Parsing and Chunking
The complexity of rational drug use R&D documentation imposes specific requirements on document parsing and chunking. First, documents contain extensive specialized terminology and abbreviations, requiring parsers to have robust domain vocabulary recognition to prevent tokenization errors. Second, information presented in tables and lists is a critical source of structured data, such as drug interaction tables. Traditional text chunking methods can compromise their integrity, leading to information loss or context disruption. Third, strong logical connections exist between different sections; for example, "Dosage and Administration" and "Use in Specific Populations" are often complementary. Simple paragraph-based splitting can fragment crucial information. Finally, the periodic nature of document updates requires the system to efficiently handle version iterations and identify content changes to ensure knowledge base timeliness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances context completeness and retrieval efficiency, preventing overly long chunks from diluting key information or overly short chunks from losing context. |
Overlap Length | 100–150 characters | Ensures sufficient overlap between adjacent chunks to maintain semantic coherence, especially when processing critical information that spans multiple chunks. |
Separator | \n\n, ., !, ?, ;, ， | Identifies natural paragraphs, sentence boundaries, and list items, preserving the document's original logical structure. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accounts for the parsing time of large PDFs or complex documents, providing ample time to prevent timeouts. |
maxContext | 4000 token | Ensures enough relevant chunks can be included when generating responses to provide comprehensive and accurate rational drug use advice. |
UPLOAD_FILE_MAX_SIZE | 50 MB | Accommodates R&D documents (e.g., clinical trial reports) that may contain numerous charts, high-resolution images, and thus have large file sizes. |
Common Pitfalls
- Table data in parsing results is garbled or missing because the parser failed to correctly identify the table structure in the document, treating table content as plain text.
- Key information is split across different chunks, making it impossible to retrieve complete context during retrieval. This occurs when the chunking strategy does not adequately consider the strong logical connections between sections in rational drug use documents.
- Timeout errors occur when uploading large PDF documents because the system's default file parsing timeout is insufficient for complex or large R&D reports.
Verification Steps
- Randomly select multiple types of rational drug use R&D documents. After uploading, check if the parsed chunks completely retain key section information such as indications and dosage from drug inserts.
- For table data within documents, verify that the table content in the parsing results maintains the original row and column correspondence, without misalignment or omissions.
- Use specific medical terms or drug names from the document for retrieval. Verify that the retrieved chunks contain complete relevant descriptions and contextual information, and check if the overlap is reasonable.
The values provided are common starting points. Measure performance 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.