Data Characteristics
Mental health regulations and SOP documents come from various sources. These include diagnostic and treatment guidelines from national health commissions, management specifications from local health authorities, internal hospital clinical pathways, drug instructions, and ethical review documents. Update frequencies vary: national guidelines might update every few years, while internal hospital SOPs or drug batch instructions could be revised annually or quarterly. Document structures typically include hierarchical headings, paragraph text, tables, images, and cited references. Common fields and units include disease classification (e.g., ICD-10 codes), drug dosages (mg/kg/day), treatment cycles (weeks/months), assessment scale scores (e.g., HAMD scores), and risk levels.
Constraints on Knowledge Base Retrieval and Recall
The hierarchical headings and cited reference structures in mental health regulation documents require the knowledge base to identify and preserve contextual integrity during document chunking. This prevents critical information from being split. Numerical fields with units, such as drug dosages and assessment scale scores, require precise matching or range queries during retrieval; simple keyword matching might not be sufficient to recall relevant content. Varying document update frequencies demand a robust knowledge base update mechanism to ensure recalled information is the latest version. Additionally, diverse sources lead to differences in document style and terminology, increasing the complexity of synonym and term normalization, which affects retrieval accuracy.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances semantic completeness of paragraphs with retrieval granularity. Avoids overly long chunks that dilute key information and overly short chunks that lose context. |
Overlap Length | 100–200 characters | Ensures contextual continuity between chunks, improving the robustness of cross-chunk information retrieval. |
Recall Count | Top 5 | Empirically verified to cover most relevant results and avoids introducing excessive irrelevant information that burdens subsequent processing. |
Similarity Threshold | Calibrate based on actual measurements | Fine-tunes based on query performance with actual datasets to ensure high relevance in recall. |
Rerank Count | Top 3 | Performs fine-grained re-ranking on recalled results to improve the relevance of the final output presented to the user. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the upload requirements for large guidelines or SOP documents, ensuring file integrity. |
Common Pitfalls
- Outdated or superseded regulatory content appears in recall results because the knowledge base update mechanism failed to synchronize with the latest document versions, leading to retrieval of old information.
- Queries like "schizophrenia drug dosage" fail to recall paragraphs containing specific dosage ranges because the knowledge base chunking strategy is too coarse, separating dosage information from its context, or failing to specially process numerical fields.
- Image content in uploaded PDF documents cannot be recognized and retrieved because the file parser lacks OCR capabilities, preventing text information within images from being extracted and indexed.
Validation Steps
- Execute a series of test cases covering different query intents. Check if recall results include all expected relevant paragraphs and verify their content against the latest document versions.
- For queries containing specific numbers, units, or codes, verify the precision of recall results, ensuring correct matching or identification of relevant information.
- Inspect knowledge base indexing logs to confirm that all uploaded documents, especially those with complex structures or image content, have been successfully parsed and indexed.
- Run a set of edge case tests, such as fuzzy queries or typo queries, to assess the robustness of the retrieval system. Adjust the
Similarity Thresholdbased on evaluation results.
Note: The values provided are common starting points. Measure performance against your own samples to find 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.