HTTP API and External Systems for Mental Health Products

Data Characteristics

Mental health product data comes from various sources. These include clinical trial reports, drug monographs, academic papers, patient adherence data, and adverse event monitoring reports. Update frequencies vary. Clinical trial data and drug regulatory information might update quarterly, while academic papers publish continuously. Document structures typically include structured clinical study results, dosage and administration, contraindications, and drug interactions. Non-structured elements include case descriptions and patient feedback. Specific fields include DSM-5 or ICD-10 diagnostic codes, HAM-D or PANSS scale scores, Cmax (peak plasma concentration), and Tmax (time to peak concentration). Units for dosage are commonly milligrams (mg) or micrograms (μg). Plasma concentrations are expressed as nanograms/milliliter (ng/mL) or micromoles/liter (µmol/L).

Constraints Imposed by These Characteristics on HTTP API and External Systems

The diverse sources and varying update frequencies of mental health product data require HTTP API designs to be flexible and robust. For example, structured data from regulatory agencies can be reliably obtained via standard RESTful APIs. However, newly published academic papers might require integration with web crawlers or subscription services. Non-structured text, such as patient feedback, requires preprocessing and standardization before HTTP transmission. This includes extracting key information through entity recognition. Specific fields like DSM-5 diagnostic codes require accurate mapping and validation in API parameter design to prevent transmission failures due to data type mismatches. Standardizing units for plasma concentration is crucial for data consistency and must be handled at the API level. Additionally, external systems processing this data must handle high concurrency, especially during initial drug launches or major updates, to ensure timely data synchronization and availability.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Clinical trial reports for mental health products are often long, containing numerous tables and charts, requiring more parsing time.
`maxContext`	`800–1200 characters`	Dosage and interaction information for mental health drugs often require a longer context for accurate understanding, preventing truncation of critical information.
`CONCURRENT_REQUEST_LIMIT`	`5`	External cloud service APIs, such as DeepSeek, have strict concurrency limits. Configuring this setting helps avoid 429 errors.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Raw clinical trial data files or large drug monographs can be substantial, requiring support for large file uploads.
`API_KEY_ROTATION_INTERVAL`	`90 days`	Ensures regular updates of external system API keys, enhancing data access security and complying with industry regulations.
`FIELD_VALIDATION_RULES`	`DSM-5 diagnostic code format validation`	Ensures specific fields, like `DSM-5` diagnostic codes, conform to standard formats and values, preventing data quality issues.

Common Pitfalls

HTTP API returns a 429 Too Many Requests error. This occurs when external API concurrency limits are not correctly configured in FastGPT, leading to requests exceeding service capacity.
A Timeout error occurs when parsing large clinical trial reports. This is due to the PARSE_FILE_TIMEOUT_SECONDS parameter being set too short, insufficient for complex documents.
Dosage units are inconsistent in consultation results, sometimes mg and sometimes μg. This happens when data source units are not uniform and the API layer does not perform unified unit standardization.

Verification Steps

Upload a typical mental health drug monograph containing DSM-5 diagnostic codes via the FastGPT application interface. Verify that its core information is accurately extracted and understood.
Use FastGPT's API interface to simulate multiple concurrent requests to an external knowledge base. Confirm that no 429 errors occur under the configured CONCURRENT_REQUEST_LIMIT.
For a clinical trial report containing Cmax and Tmax data, check that the numerical values and units in FastGPT's consultation results are consistent and correct.

The values given 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.