Data Characteristics
Psychiatric disorder research documents, such as clinical trial protocols, research reports, drug inserts, and patient follow-up records, originate from diverse sources. These documents typically contain extensive unstructured text mixed with semi-structured data like scale scores, genomic data, and proteomic data. Document update frequencies vary; clinical trial protocols remain relatively stable during a trial, while patient follow-up records and research progress reports may update frequently. Document structures are complex, often including multi-level headings, tables, references, and appendices. Beyond standard medical terminology, fields include specific psychiatric diagnostic criteria (e.g., DSM-5 or ICD-10/11 codes), drug dosage units (mg/kg, IU), treatment durations (weeks, months), and complex scale scores (e.g., HAM-D, PANSS).
Constraints on Document Parsing and Chunking
The complex structure of psychiatric disorder research documents requires parsers with robust hierarchical recognition capabilities. This ensures accurate differentiation of chapters, sections, and paragraphs, preventing information confusion. High-frequency updates for patient records and research reports demand that parsing processes quickly identify and handle incremental content, reducing redundant parsing. Precise extraction of specific diagnostic codes and scale scores—semi-structured data within documents—is crucial for subsequent knowledge graph construction and question-answering. This requires specific parsing rules or pattern matching. Diverse units and numerical representations, such as drug dosages and treatment durations, necessitate that parsers correctly associate values with units to avoid decontextualized numbers. Additionally, documents may contain sensitive patient information. Chunking must consider privacy protection, avoiding the concentration of excessive personal information within a single chunk.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters (characters) | Balances semantic completeness and recall efficiency, accommodating the longer descriptive paragraphs common in psychiatric disorder documents. |
Overlap Length | 100–200 characters (characters) | Ensures contextual continuity, especially for diagnostic criteria or treatment plans that span multiple paragraphs. |
Parsing Mode | Enhanced Parsing | Better handles complex layouts, tables, and images in PDFs, improving parsing accuracy for documents with mixed text and graphics. |
Document Type Recognition | Enabled (Enabled) | Automatically identifies document types like clinical trials and research reports, aiding subsequent specific information extraction rules. |
Timeout (PARSE_FILE_TIMEOUT_SECONDS) | 600 seconds (seconds) | Accommodates large research reports or documents with numerous charts, preventing parsing interruptions. |
Max File Size (UPLOAD_FILE_MAX_SIZE) | 500 MB | Allows uploading comprehensive research documents containing large amounts of data or high-resolution images. |
Common Pitfalls
- After uploading, the model fails to read document content or reads it incompletely: This typically results from errors during the document parsing stage. Examples include complex document formats, content too large causing parsing timeouts, or the parser failing to correctly identify text layers within the document.
- Chunking results in semantic fragmentation, leading to poor question-answering performance: This often occurs due to improper
Chunk size(Chunk Size) settings, causing key information to be truncated or important context to be dispersed across different chunks, especially for clinical research reports containing lengthy discussions. - Specific medical terms or scale scores are not correctly extracted: This may be because default parsing rules are insufficient for recognizing specialized terminology, abbreviations, or semi-structured data (like
HAM-Dscores) unique to psychiatric disorders, requiring customized parsing strategies.
Verification Steps
- Randomly select and upload multiple documents of different types (e.g., clinical trial protocols, patient follow-up records). Examine their chunking results to confirm semantic completeness and ensure all critical information is retained.
- Using the knowledge base retrieval function, query with specific diagnostic codes (e.g.,
F32.9), drug names, or scale names from the documents. Verify that relevant and accurate document chunks are recalled. - Simulate user questions, asking for detailed information about specific treatment plans or drug side effects mentioned in the documents. Evaluate whether the model's answers are accurate and based on the parsed document content.
The values provided are common starting points and should be measured against the reader's 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.