HTTP API and External Systems for CMC Research Registration

Data Characteristics for This Category

CMC research data originates primarily from laboratory records, production batch reports, quality control reports, and stability study data. This data updates frequently, especially during late-stage R&D and clinical trials, with updates potentially occurring weekly or even daily. Document structures are typically a mix of structured tables and unstructured text, including image data such as chromatograms, mass spectra, and NMR spectra, along with detailed experimental methods and results descriptions. Fields include batch number, production date, expiration date, test item, test method, result, and unit (e.g., %, ppm, μg/mL). Strict numerical precision is required for critical quality attributes like impurity content and content uniformity.

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

The high update frequency of CMC research data requires external systems to support real-time or near real-time incremental data synchronization, avoiding delays caused by manual intervention. The coexistence of structured and unstructured data means HTTP API design must accommodate structured data transfer (e.g., JSON or XML) while also supporting binary file uploads (e.g., PDFs, images). The strictness of fields and standardization of units necessitate rigorous data type validation and unit parsing during data transfer to prevent data misalignment or misinterpretation. Furthermore, the generation of large volumes of experimental data can lead to large data payloads for individual requests, requiring HTTP APIs to handle significant request body sizes and processing times.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096`	Balances context length and processing efficiency, accommodating most CMC report lengths.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Supports uploading PDF documents containing extensive charts and data, such as batch production records.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient time to parse complex unstructured PDF documents, preventing timeout failures.
`Chunk size`	`800–1200 characters`	Balances text semantic integrity and RAG recall accuracy, suitable for experimental descriptions and results analysis.
`Recall count`	`Top 5 entries`	Covers key information, reduces irrelevant noise, and focuses on specific experimental details and data.
`Similarity threshold`	`0.75`	Ensures highly relevant retrieval results, lowering the false positive rate, especially when querying specific test methods or impurity information.

Common Pitfalls

HTTP API returns a 413 Request Entity Too Large error because the uploaded batch production record or stability report file exceeds the server's configured request body size limit.
After knowledge base updates, answers to related questions are still based on old data. This occurs because the external system is not configured for incremental synchronization or the synchronization frequency is too low, leading to delayed data import.
Key numerical values or units are missing from RAG results. This happens when fields are not strictly validated during API transmission, or the parser fails to correctly extract table data from PDFs.

Verification Steps

Upload a multi-page PDF document containing charts and detailed experimental data. Verify that the file is successfully parsed and segmented, and that the content of the segmented text blocks is complete.
Simulate a data update scenario by submitting a new batch report via the HTTP API. Verify that the data for the corresponding batch number in the knowledge base has been updated.
Query the knowledge base for specific test items and results. Check that the returned content includes correct numerical values with units and compare them against the original document.

Note: The values provided are common starting points. It is important to measure them against specific 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.