Data Characteristics in This Category
Data in the mental health domain comes from various sources, including clinical trial reports, drug inserts, academic papers, clinical guidelines, and patient education materials. Document update frequencies vary; new drug development and clinical research advancements can lead to several updates per year for some data, while clinical guidelines might update every few years. Document structures are complex, often containing extensive medical terminology, abbreviations, clinical data tables, charts, and references. Fields include drug dosage, mechanism of action, adverse reactions, indications, contraindications, and clinical rating scales (e.g., HAM-D, PANSS). Units are diverse, such as milligrams (mg), micrograms (µg), milliliters (mL), and international units (IU), often accompanied by complex dosage adjustment instructions.
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
The complex structure and high density of specialized terminology in mental health documents require robust document parsing tools to accurately identify and extract key information. Varying update frequencies necessitate flexible data synchronization mechanisms for the knowledge base to ensure information timeliness. Structured or semi-structured data, such as clinical rating scales and dosage adjustments, challenge chunking strategies; critical associated information must not be fragmented. Parsing charts and tables presents another difficulty, as plain text parsing can lose important data. Accurate identification of units and fields directly impacts the accuracy of subsequent retrieval and question answering. Any parsing error could lead to serious medication or diagnostic risks.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
Chunk size | 800–1200 characters | Balances contextual completeness with retrieval efficiency. Avoids overly long chunks that dilute key information and overly short chunks that lose context. |
Chunk Overlap Length | 100–200 characters | Ensures key information is not truncated at chunk boundaries, improving recall rate. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time to process large clinical trial reports or PDF documents, preventing parsing timeouts. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the upload of large PDF documents, such as clinical research reports containing high-resolution charts. |
CHUNK_STRATEGY | Chunk by Title + By Paragraph chunks | Prioritizes maintaining the document's logical structure, then refines to paragraph level. Suitable for medical guidelines and papers. |
TEXT_EXTRACT_METHOD | OCR + Text Extraction | Addresses scanned or image-based documents, ensuring all text content can be parsed. |
Three Common Pitfalls
- PDF file upload fails with a
PDF parsing error: Corrupted filein the logs. This usually indicates a corrupted or encrypted PDF file that the parsing library cannot read. - After document parsing, key data (e.g., drug dosage, adverse reactions) is missing or inaccurate in the Q&A results. This occurs due to an improper chunking strategy, such as separating critical numerical values from descriptive information in tables or lists, leading to incomplete context during retrieval.
- After system deployment, multiple users cannot upload files simultaneously, or uploaded files remain in a "processing" state for an extended period. This might be due to insufficient permissions for file storage configuration (e.g.,
STORAGE_PATH) or unoptimized concurrent processing capabilities, hindering file read/write operations or queue processing.
How to Confirm Correct Configuration
- Select representative documents of different types (e.g., clinical trial reports, drug inserts) and sizes from this category. Upload them and observe if parsing completes successfully without errors.
- Review the chunk preview of the parsed documents. Confirm that key information (e.g., drug names, dosages, indications, adverse reactions, clinical rating items) remains intact within single or few related chunks and is not unreasonably fragmented.
- Conduct multi-round Q&A tests on the parsed documents. Ask questions involving specific data from the documents (e.g., "What is the recommended starting dose of drug X for indication Y?", "Which adverse reaction had the highest incidence in clinical trial Z?"). Verify the accuracy and completeness of the answers.
The values provided are common starting points and should be measured 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.