Data Characteristics
Mental health treatment protocols and SOP documents typically originate from national health commissions, mental health centers, or professional associations. These include guidelines, diagnostic and treatment norms, and internal hospital management protocols. Documents are primarily in PDF, Word, or scanned image formats. They feature complex structures, extensive specialized terminology, flowcharts, tables, and legal citations.
Update frequency is relatively low, usually tied to policy adjustments, new drug approvals, or clinical guideline revisions. Major updates occur annually or every few years, with minor revisions happening irregularly. Key data fields include disease diagnostic criteria (e.g., ICD-10 or DSM-5 classifications), treatment plans (drug dosages, treatment durations), nursing procedures, and risk assessment scales (e.g., Hamilton Depression Rating Scale HAMA, Positive and Negative Syndrome Scale PANSS) with their scoring standards. Units primarily involve drug dosages (milligrams, grams), time (days, weeks, months), and frequency (times/day).
Constraints on Deployment and Upgrade
The complex structure and specialized nature of mental health protocol documents demand high accuracy in model comprehension and content extraction. Flowcharts and tables within documents are challenging for standard text segmentation. This requires enhanced multimodal or layout-sensitive parsing capabilities.
Low update frequency allows for more resources to be invested in fine-grained processing during initial deployment. However, subsequent upgrades must focus on incremental update strategies to avoid reprocessing large amounts of unchanged content. The precision of scales and scoring standards requires the model to accurately identify and cite relevant numerical values and units during retrieval and generation. Any deviation could lead to significant medical risks.
Due to sensitive medical information, localized deployment and strict data isolation are mandatory. This limits reliance on cloud-based APIs and necessitates considering offline model integration and the stability and compatibility of private deployment environments.
Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 | Mental health protocol documents are often lengthy. Sufficient context is maintained to understand complex logic and cross-references. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing takes time. Ample time is reserved to prevent timeout interruptions, especially for scanned PDFs. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic integrity with retrieval efficiency. Avoids splitting core diagnostic processes or scale scoring standards. |
Recall count (Number of Retrieved Items) | Top 10 entries (top 10) | Ensures coverage of potentially relevant protocol or guideline segments, improving answer comprehensiveness. |
Similarity threshold (Similarity Threshold) | 0.75 | Mental health terminology is highly specialized. A higher threshold ensures precision of retrieved content and reduces misinformation. |
Rerank result count (Number of Reranked Items) | Top 5 entries (top 5) | Further filters the most relevant and important protocol clauses or treatment pathways based on high-similarity retrieval. |
Common Mistakes
- Frontend interface displays blank or continuous loading: This usually indicates incorrect
VITE_APP_API_URLorVITE_APP_BASE_URLconfiguration, preventing the frontend from connecting to the backend service. Check the.envfile to ensure addresses point to the correct FastGPT backend interface. - Model response content deviates significantly from expectations, or "hallucinates": This may stem from a
Similarity threshold(Similarity Threshold) set too low, leading to the retrieval of a large amount of irrelevant or peripheral information, diluting core knowledge. - System errors or unresponsiveness when uploading large files: Verify if
UPLOAD_FILE_MAX_SIZEis set too low, failing to support the upload of large protocol documents (e.g., PDFs exceeding 50MB).
Verification Steps
- Upload a protocol document containing complex flowcharts and multi-level headings. Check if file parsing completes normally and if the full content is viewable via the preview function.
- Query for diagnostic standards or drug dosages for a specific mental illness (e.g., major depressive disorder). Verify if the model's answer accurately cites specific numerical values and units from the document, with no factual errors.
- Simulate questions involving the interpretation of risk assessment scale scores or processing procedures. Verify if the model correctly understands and summarizes relevant steps, and if the returned information includes the scale name and key assessment indicators.
The values provided are common starting points and should be measured against specific 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.