Deployment and Upgrades for Mental Health Disorder

Data Characteristics

Data for mental health disorder regulatory submissions comes from diverse sources. These include clinical trial reports, pharmacovigilance data, non-clinical study reports, epidemiological surveys, and real-world evidence (RWE). Update frequencies vary; clinical trial data is typically submitted in bulk after trials conclude, while pharmacovigilance data may update continuously. Document structures are complex, adhering to the Common Technical Document (CTD) format under ICH guidelines, covering Modules 1 through 5. Data fields are highly specialized, including psychiatric rating scale scores (e.g., HAM-D, PANSS), symptom descriptions, and assessments of adverse event severity and causality. Data units often consist of scale scores, frequencies, durations, and drug dosage units, demanding high data consistency and standardization.

Constraints on Deployment and Upgrades

The specialized nature of mental health disorder data and complex document structures impose specific requirements on FastGPT knowledge base segmentation strategies and embedding model selection. Traditional segmentation by character count or punctuation may compromise semantic integrity due to extensive psychiatric scales and symptom descriptions, necessitating more intelligent segmentation. Varying data update frequencies, especially continuous pharmacovigilance data, require deployment solutions that support incremental updates and version management to prevent duplicate indexing and data redundancy. The deep hierarchical structure of CTD documents and diverse file types (PDF, Word, Excel) demand robust file parsing and structured information extraction capabilities. Furthermore, sensitive patient data mandates strict data security and compliance for the deployment environment, impacting Docker image configuration and network isolation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports and CTD documents for mental health disorders are often large; ensure full file upload.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances the completeness of psychiatric scales and symptom descriptions, preventing key information from being split.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Ensures coverage of multiple relevant clinical trial results or pharmacovigilance events.
`Similarity threshold` (Similarity Threshold)	`0.75`	Mental health disorder terminology is highly specialized, requiring a higher threshold for precise recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Parsing large PDF or Word documents can be time-consuming; prevents timeout interruptions.
`maxContext`	`16000 tokens`	Ensures sufficient context for accurate understanding of complex medical descriptions.

Common Mistakes

During Docker deployment, Container ob errors or installation failures: This typically results from misconfigurations or network issues with dependent services (e.g., MongoDB or PostgreSQL) in the Docker Compose file, causing container startup failure.
Incomplete knowledge base segmentation or parsing failures after uploading large files: This may relate to UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS being set too low, leading to incomplete file uploads or parsing timeouts.
Insufficient query result relevance, where recalled document segments do not accurately answer questions: This could be due to an inappropriate Chunk size (segment length) splitting key context, or a Similarity threshold (similarity threshold) that is too low, recalling irrelevant segments.

Verification Steps

Upload a typical CTD Module 2.5 (Clinical Overview) PDF file. Check if it parses completely and segments correctly.
For queries related to psychiatric rating scales (e.g., MMSE, ADAS-Cog), verify that recalled document segments accurately include scale names, scoring criteria, and result descriptions.
Simulate an adverse event (AE) query. Check if the system accurately extracts the frequency and severity of relevant adverse events from pharmacovigilance data.
Review FastGPT backend logs for timeout or out of memory errors during file parsing.

The values provided are common starting points. Measure them against your 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.