HTTP Interface and External Systems for CMC Research

Data Characteristics in this Category

Chemical, Manufacturing, and Control (CMC) research data in pharmacovigilance focuses on drug production quality attributes, batch information, raw material sources, manufacturing process changes, and stability data. Data sources include laboratory test reports, production batch records, quality control documents, supplier qualification certificates, and change control documents. This data updates infrequently, typically with batch production or process changes. Document structures primarily consist of structured tables, PDF reports, and specific XML files. Fields include batch_id, manufacture_date, expiry_date, key quality attributes (e.g., purity, assay) with their units (percentage, mg/g), and detection limits for specific impurities.

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

The low update frequency of CMC data means interface design does not require frequent polling. Event-driven or scheduled batch synchronization is more suitable. Diverse document structures require interfaces to handle various data formats, such as parsing tabular data from PDF reports or extracting specific fields from XML. Standardization of key quality attributes and units is fundamental for data accuracy. The HTTP request body's data structure must strictly follow predefined schemas to prevent unit confusion. Unique identifiers like batch numbers are central to data correlation; external system calls must ensure their correct transmission. When processing large volumes of historical batch data, the interface should support pagination and incremental synchronization to optimize performance.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`request_timeout`	`60 seconds`	Most CMC report files are large, and processing can take time. Allow sufficient time for transmission and parsing.
`max_retries`	`3`	Appropriate retries improve data synchronization success rates during occasional network fluctuations or temporary service unavailability in external systems.
`content_type`	`application/json` or `application/xml`	Accommodates mainstream data exchange formats used by external systems, facilitating structured data transfer.
`chunk_size_mb`	`50 MB`	For large PDF or XML reports, limiting single upload file size prevents network congestion or memory overflow.
`auth_method`	`OAuth2` or `API Key`	Ensures data transfer security and compliance, controlling external system access permissions.
`error_log_level`	`WARN`	Records non-fatal errors, aiding in troubleshooting issues like data format mismatches or missing fields.

Three Common Mistakes

An HTTP status code of 200 is returned, but the response body is empty or incomplete. This occurs when an external system encounters an internal logical error during data processing but fails to map it correctly to an HTTP status code, leading the caller to mistakenly assume success.
When uploading large batch report files, the interface frequently returns 413 Payload Too Large errors. This happens when the maximum request body size limit for the web server or application server is not configured correctly, causing files to be rejected at the transport layer.
Critical fields like batch_id or manufacture_date exhibit type mismatches or missing values after data parsing. This indicates a discrepancy between the data format returned by the external system and the interface's expected structure, or insufficient validation of field completeness and correctness during data preprocessing.

How to Verify Configuration

Simulate sending HTTP requests containing typical CMC report data. Check if the response status code is 200. Verify that key quality attribute fields like purity and assay in the response body contain expected values and correct units.
Perform upload tests using files of different sizes (e.g., 5 MB and 40 MB PDF reports). Ensure the interface handles them correctly within the chunk_size_mb limit and without 413 errors.
Review system logs for WARN or ERROR level entries, specifically focusing on exceptions related to data parsing and field mapping. Confirm that the log detail aligns with the error_log_level setting.
Execute a complete batch data synchronization process. Verify that the data synchronized to the FastGPT platform matches the external source system in terms of quantity, uniqueness of key fields like batch_id, and data freshness.

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.