Data Characteristics for this Category
Mental health registration and declaration documents typically include clinical trial reports, pharmacology and toxicology studies, manufacturing processes, quality standards, stability studies, drug instructions, ethical approvals, and informed consent forms. These documents are often in PDF or DOCX format, containing specialized and complex content. Data update frequency is relatively low, primarily occurring during the research and development phase and the declaration period. Documents contain numerous medical terms, scale scores, statistical data (e.g., p-value, confidence interval), dosage units (e.g., mg/kg, ml), and patient visit timelines. Some materials may include tables, figures, and scanned images.
Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall
The dense presence of specialized terminology and scale scores requires the knowledge base to accurately identify and retrieve passages containing specific medical concepts. This avoids interference from irrelevant information due to generalized retrieval. Complex tables and statistical data in clinical trial reports necessitate optimized chunking and indexing strategies for unstructured text to ensure data integrity. Low document update frequency means the knowledge base requires comprehensive and meticulous cleaning and annotation during initial construction, but subsequent maintenance pressure is relatively low. Additionally, the large number of scanned images demands high accuracy in OCR recognition and subsequent text extraction. Inaccurate text extraction can lead to missing key information (e.g., trial protocol number, primary endpoint), affecting recall quality.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Balances context completeness and retrieval efficiency. Avoids overly large chunks that include irrelevant information or overly small chunks that lose semantic meaning. |
Overlap Length | 100–150 characters | Ensures contextual continuity, especially when medical terms and data descriptions span across chunks, improving recall accuracy. |
Recall Count | Top 5 | Considering the specialized and detailed nature of mental health documents, increasing the recall count improves critical information coverage. |
Similarity Threshold | 0.75–0.85 | For specialized terminology and highly similar texts, a higher threshold filters out irrelevant generalized results. |
Rerank Return Count | 3 | Performs refined sorting based on initial recall, prioritizing the most relevant core information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large clinical reports and complex PDF files, preventing timeout failures. |
Common Mistakes
- Empty knowledge base information extraction: This typically occurs when uploaded document formats are unsupported or OCR recognition fails, preventing text content from being correctly indexed.
- Retrieval results contain significant irrelevant information: This may be due to a
Similarity Thresholdset too low or overly coarse document chunking, leading to excessive generalized retrieval results. - Inability to output image or table content from documents: Current knowledge base indexing primarily targets text content. Its ability to extract structured information from images and tables is limited, requiring additional processing via multimodal approaches or specific parsers.
How to Confirm Correct Configuration
- Upload a typical document (e.g., a clinical trial report) and check if the knowledge base preview displays the text content completely and accurately.
- Perform multiple retrieval tests for specific medical terms or data within the document. Check the relevance and completeness of the recall results.
- Simulate questions from actual declaration processes. Observe if the knowledge base results effectively support answering these questions and adjust the
Similarity Thresholdbased on feedback from business personnel.
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.