Data Characteristics
Mental health quality documents originate from clinical guidelines, drug inserts, clinical trial reports, ethics review documents, and regulatory guidelines from drug administration agencies. These documents update infrequently, typically annually, with occasional revisions for urgent events or new drug approvals. Document structures are primarily unstructured text, containing extensive medical terminology, diagnostic criteria, treatment plans, drug dosages, and adverse reaction descriptions. Specific fields include disease diagnosis codes (e.g., ICD-10 F00-F99), active pharmaceutical ingredients, dosage units (mg, μg), administration routes (oral, injection), and specific scale scores (e.g., Hamilton Depression Rating Scale HAMD, Positive and Negative Syndrome Scale PANSS) ranges.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The low update frequency of mental health documents implies stable knowledge base content, reducing real-time synchronization requirements. However, accurate initial construction and regular maintenance are critical. Unstructured text and extensive medical terminology demand that the tokenizer and embedding model possess strong medical domain understanding to accurately identify professional vocabulary and concepts. The presence of specific fields like disease diagnosis codes and drug dosages indicates a need for precise matching and numerical range queries during retrieval. Standard semantic retrieval may not effectively distinguish subtle dosage differences or scale thresholds. Diverse scale score ranges and administration route descriptions increase document content complexity, potentially leading to overly broad or irrelevant recall results.
Configuration Strategy
| Parameter | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Mental health documents often have long paragraphs; sufficient context aids semantic understanding. |
Overlap Length | 100–200 characters | Ensures key information across chunks remains connected. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Medical content requires high accuracy; a high threshold reduces irrelevant results. |
Recall count (Recall Count) | Top 8–12 entries | Ensures coverage of diverse relevant information for complex queries. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates parsing large clinical trial reports or guidelines, preventing timeouts. |
UPLOAD_FILE_MAX_SIZE | 200 MB | Accounts for PDF documents that may contain multiple images or detailed tables. |
Common Pitfalls
- Uploading large PDF documents results in a
timeout of 60000ms exceedederror because file parsing exceeds the default waiting time. - Some images in knowledge base Q&A display incorrectly, manifesting as broken image links or rendering errors. This may occur because image resources were not uploaded correctly or their paths were lost during parsing.
- Retrieval results contain a large amount of content unrelated to mental health. This happens when medical terminology is not pre-processed, leading to poor generalization in retrieval.
Verification Steps
- Upload a complex PDF document containing ICD-10 diagnostic codes, drug dosages, and scale scores. Confirm successful parsing and complete content.
- Use query statements with specific medical terminology and numerical ranges. Verify the precision and relevance of recall results against expectations, and check if diagnostic codes and dosage information are correctly identified.
- For an uploaded document, cross-verify with keyword and semantic searches. Ensure critical information is effectively retrieved and compare result coverage across different recall counts.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.