Database and Operations for Structured Analysis of Mental

Data Characteristics

Mental health R&D document data originates from diverse sources. These include clinical trial reports, pathological analyses, genomic sequencing data, drug mechanism of action studies, patient behavioral observation records, and multimodal imaging data. Document update frequency depends on the R&D phase. Early basic research may have slower updates, while clinical trial phases can generate new data weekly or even daily. Documents have complex structures, often containing large volumes of unstructured text, semi-structured tabular data, and graphical information. Fields and units are highly specialized. Examples include patient mood scale scores (e.g., HAM-D, MADRS), gene locus expression levels (e.g., FPKM, TPM), neurotransmitter concentrations (e.g., ng/mL), and imaging metrics (e.g., fMRI signal intensity, DTI fractional anisotropy). Data also frequently includes citations and interpretations of disease diagnostic criteria (e.g., DSM-5, ICD-11).

Constraints on Database and Operations

The complexity and diversity of mental health R&D documents impose specific constraints on database and operations. Large volumes of unstructured text and semi-structured tables require the database to have robust document storage and indexing capabilities. This includes support for JSON or BSON formats and efficient processing of text vectors. High-frequency data streams necessitate database support for real-time or near real-time write operations. The database must also effectively manage version control to track R&D progress and data provenance. Specialized fields and units demand strict type validation and unit standardization during data ingestion to avoid ambiguity. Furthermore, due to sensitive patient data and intellectual property, the database must meet stringent security and compliance requirements, including data encryption, access control, and audit logs. Storing and retrieving multimodal data (e.g., images) also places higher demands on database storage capacity and retrieval efficiency.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`MONGODB_URI`	`mongodb://user:password@host:port/fastgpt`	FastGPT supports MongoDB by default. Its document-oriented nature suits storing complex R&D documents.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Mental health R&D documents may include large images or multimodal data reports.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large, complex PDF or Word documents can be time-consuming.
`Chunk size`	`800–1200 characters`	Balances context completeness and vector retrieval efficiency. Adapts to text dense with specialized terminology and strong contextual links.
`Similarity threshold`	`0.75`	Ensures retrieved R&D document segments are highly relevant to the query. Reduces false positives for domain-specific terms.
`maxContext`	`32000`	Provides the model with a longer context window to understand complex clinical descriptions and experimental results.

Common Pitfalls

An Authentication failed error occurs when connecting FastGPT to MongoDB. This indicates incorrect username or password in MONGODB_URI, or insufficient database user permissions.
After uploading a large clinical trial report, the "file processing" status persists for a long time, eventually resulting in a "file parsing failed" error. This typically happens when PARSE_FILE_TIMEOUT_SECONDS is set too short, not allowing enough time for complete document parsing.
When querying mental health symptoms or treatment plans, retrieved document segments have low relevance. This may be due to a Similarity threshold set too low, leading to the retrieval of semantically less relevant documents.

Verification Steps

Upload a typical mental health R&D document containing various data types (e.g., text, tables, charts). Confirm successful parsing and vector generation.
Ask multiple questions based on the uploaded document content. Observe if retrieved knowledge base segments are accurate and semantically coherent. Check the practical effect of Similarity threshold.
Monitor MongoDB's connection status, storage usage, and write latency in the database monitoring interface. Ensure stable performance during high-concurrency write operations.
Regularly check FastGPT system logs for any errors or warnings related to database connections or file parsing.

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.