HTTP Interface and External Systems for Stability Study

Data Characteristics in this Category

Stability study core data originates from laboratory environment monitoring systems, sample testing equipment, and manual records. This data typically includes batch information, sample IDs, storage conditions (temperature, humidity, light), sampling time points, and various physicochemical indicators (e.g., content, purity, degradation products, pH, moisture). Data update frequency depends on the study design; it can range from multiple times daily (environmental parameters) to once every few months (key testing indicators). Raw data often exists in CSV, Excel, or LIMS system-exported formats, lacking a unified API interface. Document structures are typically tabular, with field names that may contain special characters or abbreviations, and diverse units. For example, temperature might be ℃ or K, humidity %RH, and content % or mg/mL.

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

The discrete nature of stability study data requires highly flexible HTTP interface design. Due to the lack of standard APIs, external system integration often requires developing customized data extraction and transformation logic. Inconsistent data update frequencies necessitate fine-tuned polling interval configurations to avoid burdening low-frequency data sources with high-frequency requests, while ensuring the real-time nature of critical environmental parameters. The diversity of document structures and field names challenges data parsing modules, requiring support for dynamic field mapping or pre-set multiple parsing templates. Heterogeneous units require standardization before data transmission, for example, unifying all temperature units to ℃ to ensure accuracy in subsequent analysis and display.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`60 seconds`	Most LIMS systems are slow to respond. This value prevents timeouts due to short-term network fluctuations or lengthy database queries.
`MAX_RETRIES`	`3`	Provides a moderate retry mechanism for transient network failures or temporary unavailability of external systems.
`RETRY_INTERVAL_SECONDS`	`5 seconds`	Initial retry interval, can be combined with an exponential backoff strategy.
`DATA_PARSE_TEMPLATE_ID`	`Calibrated by actual measurement`	Stability data sources have diverse formats, requiring selection or customization of parsing templates based on actual data structures.
`UNIT_STANDARDIZATION_MAP`	`{"℃":"℃", "K":"℃", "%RH":"%RH", "%":"%"}`	Ensures all measurement units are unified before entering the AI system to avoid ambiguity.
`POLLING_INTERVAL_MINUTES`	`15 minutes`	Balances data real-time requirements with external system load, for high-frequency data sources like environmental parameters.

Common Pitfalls

Calling an external API returns an HTTP 500 error because the batch_id field type passed in the request body does not match the external system's requirements (e.g., an integer is expected but a string is sent).
Received data, after parsing, shows a critical field (e.g., degradation_rate) as empty. This occurs because the external system's returned data structure does not match the pre-set parsing template, or the field is genuinely missing for a specific batch.
A successful request returns significantly less data than expected, indicated by a low response_items_count value. This may be due to the external system's API having a pagination mechanism, but the HTTP request does not correctly handle pagination_token or offset parameters.

Verification Steps

Execute at least three requests for each configured HTTP interface. Verify that the returned JSON or XML structure matches expectations, and confirm that critical fields exist and have correct data types.
Simulate abnormal external system responses (e.g., HTTP 502 or incorrect data structure). Observe if FastGPT's error handling mechanism correctly captures and logs the errors.
Compare the latest updated stability data in the external system's database with the data obtained via the HTTP interface. Ensure data consistency and timeliness, especially for the last_updated_timestamp field.

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.