HTTP Interface and External Systems for Bioequivalence

Data Characteristics

Bioequivalence (BE) clinical trial pre-screening data primarily comes from early drug development. Sources include in vitro dissolution experiment reports, animal pharmacokinetic (PK) study results, and public information on similar marketed drugs. This data updates infrequently, typically with new experimental batches or research progress. Document structures are mostly structured tables, such as pharmacokinetic parameter tables (Cmax, AUC0-t, Tmax, etc.), and unstructured experimental records and analysis reports. Fields include drug name, batch number, test product/reference preparation, dose, administration route, sampling time points, plasma concentration (ng/mL), and pharmacokinetic parameters (e.g., AUC0-inf in ng·h/mL, Cmax in ng/mL). Some data may be in chart format, requiring image recognition or manual entry.

Constraints from "HTTP Interface and External Systems"

Diverse data sources require the HTTP interface to have flexible file parsing capabilities. It must handle structured data files (e.g., CSV, Excel) and unstructured text reports (e.g., PDF). Infrequent updates mean real-time interface calls are not necessary, but data completeness and historical version traceability are crucial. The specialized nature of pharmacokinetic parameters and the strictness of units demand accurate identification and preservation of values and unit information during data extraction and transformation. This prevents pre-screening result deviations due to unit confusion. Chart data may require additional pre-processing services to convert image information into parseable text or numerical data, increasing interface call complexity and duration. For sensitive trial data, the interface must support secure authentication and authorization mechanisms.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Bioequivalence report files are often large, containing multiple pages of data and charts, making parsing time-consuming.
`maxContext`	`8000 characters`	Must accommodate complete experimental background, method descriptions, and key pharmacokinetic parameters to avoid information truncation.
`Chunk size`	`800 characters`	Ensures each segment contains complete logical information, such as a full pharmacokinetic parameter table row or a section of method description.
`Similarity threshold`	`0.75`	Ensures accurate recall of historical data highly relevant to the current queried drug or parameter during pre-screening.
`Rerank result count`	`Top 10 entries`	Pre-screening requires evaluating multiple relevant historical cases; providing more re-ranked results aids comprehensive analysis.
`API_CONCURRENCY_LIMIT`	`Calibrate by actual measurement`	Depends on the backend service (e.g., DeepSeek) concurrency limit to avoid 429 errors; requires adjustment based on actual conditions.

Common Mistakes

Interface calls for file parsing take too long or time out directly. This occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low for large PDF reports or Excel files with complex tables.
Some key pharmacokinetic parameters (e.g., AUC or Cmax) in pre-screening results are incorrect or missing. This happens when the file parser fails to correctly identify values with units or when unit information is lost during data cleaning.
Frequent 429 error responses occur during external system integration. This is due to not configuring an appropriate API_CONCURRENCY_LIMIT in the FastGPT application, causing requests to upstream AI services to exceed their concurrency limits.

Verification Steps

Upload a typical bioequivalence study report (PDF or Excel). Check if its parsing time is within the PARSE_FILE_TIMEOUT_SECONDS limit. Verify that extracted key fields (e.g., Cmax, AUC0-t) are accurate and have correct units.
Use a query containing a specific drug name and pharmacokinetic parameters. Verify that the recalled results include the expected relevant historical trial data. Check if the Similarity threshold effectively filters for high-quality results.
Simulate multiple concurrent requests. Observe FastGPT application calls to external AI services. Ensure that 429 status codes do not frequently appear under concurrent pressure. Verify the effectiveness of API_CONCURRENCY_LIMIT.

The values provided are common starting points and should be measured against your 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.