Data Characteristics
Patient Assistance Program (PAP) R&D documents include clinical trial protocols, investigator brochures, informed consent forms, ethics approval documents, drug inserts, patient education materials, and safety reports. These documents originate from pharmaceutical companies' clinical research departments, medical affairs departments, or CROs. Document updates are frequent, especially during clinical trials, with protocol amendments and safety data additions occurring often. Documents have complex structures, often containing multi-level headings, tables, appendices, and references. They may also mix formats such as scanned PDFs, Word documents, Excel spreadsheets, and some images. Key fields include drug name, indications, dosage and administration, adverse reactions, inclusion/exclusion criteria, follow-up plans, and patient informed consent clauses. Units involve medical professional expressions like dosage (mg, g), time (days, weeks, months), and frequency (times/day).
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complex structure and high update frequency of PAP R&D documents impose specific requirements on document parsing. Multi-level headings and mixed formats make traditional plain-text chunking methods ineffective at capturing semantic hierarchies, potentially leading to loss of critical context. For example, inclusion criteria in a clinical trial protocol might be spread across different sections; simple paragraph-based chunking would fragment their completeness. High update frequency requires the parsing system to support efficient incremental updates, avoiding full re-parsing for minor revisions, which wastes resources. Embedded charts, tables, and scanned content require OCR technology for text extraction and layout analysis for semantic identification. Accurate recognition of medical terminology and units is fundamental for subsequent retrieval and inference; incorrect parsing directly impacts the accuracy of patient assistance solutions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances semantic completeness and retrieval efficiency, covering the length of most clinical research clauses. |
Overlap Length | 100–200 characters | Ensures contextual continuity at chunk boundaries, reducing semantic fragmentation risk, especially for long sentences and lists. |
OCR_ENABLED | True | Ensures text content in scanned documents and images is recognized and indexed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDF files or documents with many images, preventing timeout interruptions. |
MAX_FILE_SIZE_MB | 200 MB | Covers most R&D documents containing rich charts and high-resolution images. |
Chunking Strategy | Smart Chunking (based on headings, paragraphs) | Better preserves the logical structure of documents, especially clinical protocols with multi-level headings. |
Three Common Mistakes
- Some content is missing after document parsing, particularly sub-content under multi-level directories or embedded image text not indexed. This happens because the default parser lacks sufficient compatibility with complex structures or non-standard formats, or the OCR service is not correctly enabled.
- After uploading a document via API, the interface returns success, but new content is not retrievable in the knowledge base. This occurs when the backend parsing queue fails, or the file is skipped during parsing due to format issues, but the API layer does not expose the internal error.
split-related error messages appear in logs, preventing normal document chunking. This can be due to special characters or encoding issues in the document content, which do not match the chunking algorithm's expected input, leading to processing interruption.
How to Verify Correct Configuration
- Upload a typical patient assistance R&D document (e.g., a clinical trial protocol PDF). Check if all key sections and appendix content, especially text within charts and tables, are successfully indexed in the knowledge base.
- For documents containing scanned content or image text, perform keyword searches in the knowledge base to confirm that text content from images is retrievable.
- Upload large, multi-page Word or PDF documents via API. Observe if the document parsing status is normal after the API call returns, and verify content completeness and retrievability in the knowledge base.
- Search for specific medical terms or dosage units within documents to verify their accurate recognition and chunking, and check for good semantic coherence.
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.