HTTP Interface and External Systems for Bioequivalence

Data Characteristics

Bioequivalence (BE) study quality documents primarily include protocols, reports, raw data, and statistical analysis files. Data typically originates from clinical research organizations, CROs, and pharmaceutical company R&D departments. Document update frequency is relatively low, concentrating on project initiation, mid-term revisions, and final report submissions. Document structures are complex and often contain numerous tables, charts, and statistical data. Common fields include subject ID, dosing time, blood sampling time, drug concentration (Cmax, AUC, etc.), Tmax, and adverse event records. Units are highly standardized, such as ng/mL or μg/mL for concentration, hours or minutes for time, and mg for quantity.

Constraints Imposed by HTTP Interface and External Systems

Bioequivalence documents usually involve large data volumes; a single report file can span hundreds of pages. Their complex internal structure impacts file parsing efficiency and accuracy. Low document update frequency means real-time synchronization is not a high priority, but data integrity and version control are critical. High field standardization facilitates data correlation and validation through structured extraction. However, the interface must accurately identify these specific fields and their units during data parsing. Given raw data and subject information, data transmission and storage security are paramount. The HTTP interface must support encrypted transmission and implement strict authentication and access control for external systems. For charts within reports, the interface requires some image parsing capability or relies on OCR services for text extraction.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Bioequivalence report files can be large, containing many images and data.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF documents is time-consuming and requires sufficient processing time.
`Chunk size` (Segment Length)	`800 characters`	Ensures individual segments contain complete table rows or key conclusions, preventing semantic fragmentation.
`Similarity threshold` (Similarity Threshold)	`0.75`	Guarantees recall results are highly relevant to bioequivalence terminology and data.
`maxContext`	`32000 tokens`	Provides a sufficient context window for comprehensive understanding of complex research protocols and reports.
`HTTP_REQUEST_TIMEOUT`	`120 seconds`	External system data synchronization may involve large data transfers, requiring a longer timeout period.

Common Pitfalls

An external system receives a 400 Bad Request error when calling the interface. This typically occurs when the JSON format in the request body does not comply with API specifications, for example, missing required fields like file_type or app_id.
After uploading a large bioequivalence report file, the system remains unresponsive for an extended period or eventually displays a 504 Gateway Timeout. This likely indicates that the PARSE_FILE_TIMEOUT_SECONDS configuration is too low, not allowing enough time for file parsing.
After submitting a document via the API, critical data (e.g., Cmax values) is missing or incorrect in retrieval results. This can happen if the document parser fails to correctly identify table structures or specific units like ng/mL within the report.

Verification Steps

Upload a bioequivalence report PDF file containing complex tables and charts. Observe if the file processing completes normally and if corresponding segments are generated in the knowledge base.
Upload a test document via the API interface. Check if the API returns a 200 OK status code and verify that the document content is successfully ingested.
Perform retrieval tests for specific drug concentration values or statistical results within the report. Confirm that the original text segments containing this information are accurately recalled.
Simulate an external system pulling data via the HTTP interface. Verify that the data format, field completeness, and data consistency meet expectations, for example, if the patient_id field is correctly mapped.

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.