HTTP Interface and External Systems for Solid Tumor

Data Characteristics

Solid tumor pharmacovigilance data originates from clinical trial reports, real-world studies, spontaneous adverse event reporting systems, and medical literature. This data is typically structured or semi-structured. Examples include ICH E2B R3 safety reports in XML format, clinical event records from Electronic Health Records (EHRs), or literature abstracts. Data updates are frequent. Clinical trial data accumulates continuously during trials, and spontaneous reporting systems receive real-time updates. Document structures are complex, encompassing patient demographics, diagnoses, comorbidities, medication history, adverse event descriptions, severity assessments, and causality judgments. Field names may involve specialized acronyms like AE (Adverse Event) or SAE (Serious Adverse Event). Units typically follow international standards, such as mg or g for dosage, and days, weeks, or months for time.

Constraints on HTTP Interfaces and External Systems

The high update frequency and complex structure of solid tumor pharmacovigilance data impose constraints on HTTP interface design. Real-time or near real-time data synchronization requires external systems to support high-concurrency requests and incremental update mechanisms. For example, ICH E2B R3 reports are often transmitted in XML format, requiring interfaces to correctly parse and process nested structures. Medical terminology is highly standardized, but actual reports may contain non-standard expressions, necessitating robust error handling in the interface. Data sensitivity (involving patient privacy) mandates encrypted HTTP communication and strict authentication and authorization mechanisms. Importing large volumes of historical data and performing regular synchronization requires efficient and stable interfaces to prevent timeouts or connection interruptions due to excessive data volume.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`requestTimeoutSeconds`	`300 seconds`	Accommodates large XML file transfers and parsing, preventing interface timeouts due to extended processing times.
`maxConnections`	`Calibrate based on actual measurements`	Balances external system concurrent request volume with FastGPT server resources, ensuring stable interface response under high load.
`httpMethod`	`POST`	Suitable for transmitting request bodies containing large amounts of structured data, such as ICH E2B R3 XML.
`contentType`	`application/xml; charset=UTF-8`	Ensures correct identification and parsing of ICH E2B R3 report content in XML format.
`maxPayloadSizeMB`	`50 MB`	Allows transmission of large safety reports containing detailed clinical information and attachments.
`retryAttempts`	`3 times`	Addresses transient network fluctuations or occasional external system service unavailability, improving data synchronization success rates.

Common Pitfalls

An HTTP request returning a 400 Bad Request error typically indicates that the XML structure in the request body does not conform to the ICH E2B R3 standard or the interface's expected schema, leading to parsing failure.
An interface call succeeds, but some fields in the FastGPT knowledge base are empty. This may occur if the JSON/XML field names returned by the external system do not match FastGPT's predefined mapping, or if data types are inconsistent.
The debug page correctly retrieves knowledge base content, but some queries fail to hit the knowledge base when called via API. This might be due to overly strict topK or similarityThreshold parameters in the API call, filtering out recall results.

Verification Steps

Use FastGPT's interface debugging tool to send a POST request with a real ICH E2B R3 XML sample. Check if the HTTP status code is 200 OK and verify that the returned knowledge base content is complete and as expected.
Regularly review FastGPT knowledge base import logs. Confirm that data synchronization tasks do not show timeout or parsing error messages, and that the data volume matches the external system's source data.
Execute a series of solid tumor pharmacovigilance-related queries via the FastGPT API. Verify that the similarityScore of the results is within a reasonable range and that relevant knowledge snippets are retrieved.
Trigger a small number of adverse event report updates in the external system. Observe the update speed and accuracy of the corresponding solid tumor information in the FastGPT knowledge base to validate the incremental synchronization mechanism.

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.