Data Characteristics
Dermatology quality document data originates from drug manufacturers' package inserts, clinical trial reports, real-world study (RWS) data, post-market adverse event reports, and regulatory guidelines. Document update frequency is relatively stable. Package inserts typically update after regulatory approval. Clinical trial reports release as trials progress. Document structures include strict section divisions such as [Indications], [Dosage and Administration], [Adverse Reactions], [Contraindications], and [Precautions]. Fields involve drug names, active ingredients, excipients, dosage units (e.g., mg, g, ml), patient characteristics, pathological descriptions, and treatment efficacy evaluation metrics. Documents also contain numerous medical abbreviations and charts.
Constraints on Document Parsing and Chunking
Strict section divisions and high professional terminology density in dermatology quality documents require accurate identification and preservation of the original logical structure by the document parser. For example, parsing the [Adverse Reactions] section needs to ensure that related symptoms and drug associations remain intact. Numerous medical abbreviations and charts challenge chunking granularity. Overly large chunks can dilute critical information. Overly small chunks can lose context. The precision of dosage units and treatment efficacy evaluation metrics requires avoiding truncation of complete descriptions involving values and units during chunking. This directly impacts subsequent retrieval accuracy. Document update frequency is not high, but each update may involve critical information revisions. This demands version management and incremental update capabilities from the parsing system.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances medical terminology context integrity and retrieval granularity; avoids truncating critical descriptions. |
Overlap Length | 100–150 characters (characters) | Ensures smooth transitions at chunk boundaries; preserves semantic connections between adjacent chunks. |
Parsing Mode | By Title | Dermatology documents often use standard chapter structures; chunking by title effectively maintains logical integrity. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing time for large clinical trial reports or PDFs with embedded multimedia. |
maxContext | 3000 Tokens | Accommodates the specialized nature and information density of medical documents; ensures the large language model receives sufficient context. |
ENABLE_OCR | True | Identifies image-based tables that may be embedded in documents; extracts text information from them. |
Common Pitfalls
- Symptom: Uploaded PDF documents show parsing failure or timeout due to excessive parsing time. Reason: Documents contain numerous scanned copies or image-based tables, and OCR is not enabled, or
PARSE_FILE_TIMEOUT_SECONDSis set too short. - Symptom: Retrieval results show incomplete drug dosage or efficacy evaluation metric descriptions. Reason:
Chunk size(Chunk Length) is set too small, causing sentences containing values and units to be truncated. - Symptom: After a knowledge base update, critical information from the new document version cannot be accurately retrieved. Reason: No version management strategy is configured, or the incremental update mechanism fails to correctly identify and process document content revisions.
Verification Steps
- Upload representative dermatology quality documents (e.g., package inserts, clinical trial reports). Check parsing logs for errors or warnings.
- Perform retrievals using professional terminology, dosage information, or adverse event descriptions from the documents. Verify the completeness and accuracy of returned results, ensuring critical information is not truncated.
- Upload revised versions of documents. Compare retrieval results between old and new versions to confirm the system identifies and prioritizes information from the latest version.
The values provided are common starting points. Measure them against specific 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.