HTTP Interface and External Systems for Rational Drug Use

Data Characteristics

Data related to rational drug use and pharmacovigilance originates from drug inserts, clinical guidelines, adverse event reporting systems (such as national adverse drug reaction monitoring databases), medical literature, and pharmaceutical knowledge bases. Update frequencies vary; drug inserts and clinical guidelines typically update quarterly or annually, while adverse event reports can increase daily or even in real-time. The data document structure is complex, often containing unstructured text (e.g., adverse reaction descriptions, medication orders), semi-structured data (e.g., drug ingredients, indications, contraindications), and structured data (e.g., ICD-10 disease codes, ATC drug classification codes). Fields may include generic drug name, brand name, dosage, administration route, adverse event, occurrence time, severity, patient basic information (after de-identification), and concomitant medications. Units include dosage units like mg, g, ml, IU, and time units like days and hours.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The diversity and complexity of rational drug use and pharmacovigilance data impose specific requirements on HTTP interface design and external system integration. Unstructured text fields require interfaces capable of processing long texts and may necessitate additional text analysis services for preprocessing. Semi-structured and structured data require clear JSON or XML format definitions to ensure accurate data transmission. The real-time nature of data updates requires HTTP interfaces to support high concurrent access and consider incremental update mechanisms to reduce resource consumption from full synchronization. Fields containing numerous medical terms and abbreviations may require custom dictionaries for matching and parsing. Additionally, de-identification of sensitive patient information requires strict control by external systems at the data source or interface layer to ensure data security and compliance. Complex unit conversions may also require logical adaptation at the interface caller or receiver.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`API_REQUEST_TIMEOUT_SECONDS`	`60 seconds`	Addresses delays from slow external system responses or large data volumes, preventing premature timeouts.
`MAX_BODY_SIZE_MB`	`10 MB`	Accommodates potentially long text or multi-field data found in drug inserts or adverse event reports.
`RATE_LIMIT_PER_MINUTE`	`300 times`	Balances data update frequency with external system capacity, preventing service denial due to high concurrency.
`ERROR_RETRY_COUNT`	`3 times`	Handles network fluctuations or occasional external system failures, improving data synchronization robustness.
`CUSTOM_HEADER_AUTH_TOKEN`	Configure as required by external system, e.g., `Bearer xxx`	Ensures secure authentication and authorization with external pharmaceutical knowledge bases or adverse event reporting systems.
`DATA_SCHEMA_VALIDATION_MODE`	`Strict Mode`	Ensures incoming or outgoing data strictly adheres to predefined structures and field types, reducing parsing errors.

Common Pitfalls

HTTP requests return 4xx or 5xx status codes, but business data is not processed correctly. This typically results from interface authentication failure, insufficient permissions, or external system business logic exceptions.
Frequent interface calls lead to excessive load on external systems, or even rate limiting. This occurs due to improper configuration of request frequency or failure to utilize batch query interfaces provided by external systems.
Received data fields are missing or incorrectly formatted, causing subsequent processing logic errors. This often happens when external system data structure changes are not promptly synchronized with interface configurations, or strict data validation is not performed.

How to Verify Configuration

Check FastGPT's log system to confirm all requests under the API_REQUEST_TIMEOUT_SECONDS configuration complete within the specified time and without timeout errors.
Simulate high concurrency scenarios to observe if the external system remains stable under the RATE_LIMIT_PER_MINUTE configuration, without frequent rate limiting error codes.
Perform random sampling checks on critical data fields from external systems, such as drugName and adverseEventDescription, to ensure their type, format, and content match expectations.
For the ERROR_RETRY_COUNT configuration, deliberately trigger occasional external system errors (e.g., temporarily shut down an external service) to verify FastGPT retries as expected and eventually successfully retrieves data.

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.