HTTP Interface and External Systems for Blood Glucose Data

Blood Glucose Data Characteristics

Blood glucose data originates from glucometers, Continuous Glucose Monitoring (CGM) devices, or lab reports. This data updates frequently, from several times daily (finger-prick blood glucose) to every 5 minutes (CGM), exhibiting strong time-series properties. Data document structures vary; they can be CSV or JSON export files, or structured data streams obtained via API. Key fields include measurement_time (to the minute), glucose_value (typically in mmol/L or mg/dL), measurement_type (e.g., pre-meal, post-meal, bedtime), insulin_dose (in IU), and carbohydrate_intake (in grams). The glucose_value range and anomaly flags (e.g., hypoglycemia, hyperglycemia) are central to interpretation.

Constraints from HTTP Interface and External Systems

The real-time nature of blood glucose data requires the intelligent customer service system to frequently pull the latest data via HTTP interfaces, ensuring interpretation accuracy. Diverse data sources (different devices, manufacturers) mean the interface may need to adapt to multiple data formats and authentication mechanisms. Specifically, high-frequency CGM data updates demand specific request frequency and concurrent processing capabilities from the interface. This prevents interpretation delays caused by data retrieval latency. Unit conversion for blood glucose values (mmol/L and mg/dL) requires standardization after the interface returns data to unify the internal data model. Requirements for historical data traceability mean the interface must support time-range based data queries and handle pagination for large volumes of historical data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`API_ENDPOINT`	Actual data provider's API address	Ensures connection to the correct data source
`REQUEST_INTERVAL`	`300 seconds`	Balances data real-time needs with interface load; can be shortened for CGM data
`AUTH_TOKEN_TYPE`	`Bearer Token` or `OAuth2`	Adapts to mainstream API authentication mechanisms
`DATA_FORMAT`	`JSON`	Ensures data parsing generality and efficiency
`DATE_FIELD_NAME`	`measurement_time`	Unifies time field for easy data sorting and filtering
`UNIT_CONVERSION`	Conversion factor `18.018` for `mmol/L` to `mg/dL`	Unifies blood glucose units to avoid confusion

Common Pitfalls

Symptom: The intelligent customer service system cannot retrieve the latest blood glucose data, returning "Data acquisition failed" or an empty result. Reason: The HTTP interface authentication credential AUTH_TOKEN is expired or has insufficient permissions, causing the external system to reject the request.
Symptom: Blood glucose interpretation results show unit errors or abnormal values. Reason: UNIT_CONVERSION is not configured correctly, or the glucose_value field returned by the external system has a unit inconsistent with expectations and was not standardized.
Symptom: When a user switches, the system displays another user's historical blood glucose records. Reason: The user_id or patient_id identifier was not correctly passed in the HTTP request, causing the external system to return incorrect contextual data.

Verification Steps

Use FastGPT's debugging tools to send a simulated request to the configured HTTP interface. Check if the returned HTTP status code is 200 and if the response body contains the expected blood glucose data fields.
Create a test application in FastGPT, link it to this external system, and simulate several blood glucose data queries for different users. Observe if the returned blood glucose values and timestamps are correct, and compare them with the original data.
Check the log system to confirm no Timeout or Connection Refused errors occurred during data retrieval. Observe if the data retrieval frequency matches the REQUEST_INTERVAL setting.
Perform a query for blood glucose data with different units. Verify the system correctly identifies and uniformly processes both mmol/L and mg/dL units.

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.