Health Management Clinical Trial Pre-screening: HTTP

Data Characteristics in This Category

Clinical trial pre-screening data in health management primarily originates from personal health records, medical examination reports, wearable device data, and survey questionnaires. Data update frequency is high. Wearable device data can update minute-by-minute. Medical examination reports typically update annually or semi-annually. Questionnaire surveys update according to project cycles. Document structures vary. Medical examination reports are often PDF or structured XML files, containing blood counts, biochemical indicators, and imaging reports. Wearable device data is often JSON or CSV format, recording heart rate, sleep, and steps. Survey results are usually stored in databases as structured tables. Fields include physiological parameters like heartRate (beats/minute) and bloodPressure (mmHg), as well as textual information such as medical history and family history.

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

The diversity and high update frequency of health management data place specific demands on FastGPT's HTTP interface and external system integration. First, PDF and XML medical examination reports require robust file parsing capabilities to accurately extract structured information and vectorize it. Second, the large volume of real-time or near real-time data streams from wearable devices requires the interface to have efficient data reception and processing capabilities to avoid delays caused by data accumulation. Data field standardization varies; for example, blood pressure readings might have both systolic and diastolic fields. The interface needs to preprocess and normalize this data during ingestion. Additionally, due to the sensitive nature of health data, the interface must support strict authentication and encrypted data transmission to ensure data security.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`3000` characters	Clinical trial pre-screening involves long texts like medical history and examination reports, requiring sufficient context.
`Chunk size` (Segment Length)	`500–800` characters	Balances semantic completeness and model processing efficiency. Avoids losing details with overly long segments or lacking context with overly short segments.
`Similarity threshold` (Similarity Threshold)	`0.75`	Clinical pre-screening demands high recall accuracy. A threshold that is too low might introduce irrelevant information, while one that is too high might miss relevant candidates.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (Top 5)	Pre-screening results need to precisely point to a few key pieces of information. Too many items increase the manual review burden.
`API_KEY`	Use independently generated `key`	Ensures authentication security and permission isolation when external systems call the FastGPT interface.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Processing large PDF or XML medical examination reports requires a longer timeout to prevent interruptions during parsing.

Three Common Mistakes

Phenomenon: The HTTP interface returns 400 Bad Request, with an error message indicating invalid_field_format. Reason: Health indicator fields (e.g., bloodPressure) transmitted by the external system are not serialized according to FastGPT's expected format or units.
Phenomenon: After importing a large amount of wearable device data, knowledge base updates are slow, and query results show data latency. Reason: An appropriate incremental synchronization mechanism was not designed for high-frequency, small-batch data updates, or PARSE_FILE_TIMEOUT_SECONDS was set too short, causing some data parsing failures.
Phenomenon: A 401 Unauthorized error occurs during API calls. Reason: The API_KEY is not configured correctly, or the external system did not include the correct authentication information in the request header when making the call.

How to Confirm Correct Configuration

Trigger a data import containing various health reports (PDF, JSON, XML). Check if the knowledge base correctly parses and builds vector indexes, and if key indicator fields from the reports are retrievable.
Simulate a high-frequency data stream (e.g., sending 100 wearable device data points per minute). Observe FastGPT's knowledge base update speed and query result real-time performance. Ensure data latency is within an acceptable range.
Use the API_KEY configured for the external system to call FastGPT's query interface. Confirm that results are returned normally. Simultaneously, attempt to use an incorrect API_KEY to verify that the 401 Unauthorized error is triggered as expected.

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.