HTTP Interface and External Systems for Small Molecule Drug

Data Characteristics

Small molecule drug quality documents typically include raw material inspection reports, intermediate analysis data, finished product release test records, stability study reports, and batch production records. Data sources are diverse, covering Laboratory Information Management Systems (LIMS), Electronic Batch Record (EBR) systems, and scanned paper documents. These documents are updated frequently, especially with frequent production batches and numerous changes during the research and development phase. Document structures are highly standardized; for example, batch production records follow GMP guidelines, containing specific sections and fields. Field units are strict, such as percentage content, impurity ppm, and heavy metal ppb, and often include metadata like testing methods and instrument models.

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

The high standardization and strict unit requirements of small molecule drug quality documents mean HTTP interfaces must precisely match fields and units during data parsing. This prevents data discrepancies caused by data type mismatches or incorrect unit conversions. High update frequency requires interfaces to have efficient data synchronization capabilities, ensuring external systems receive the latest document versions. Diverse document sources mean interfaces need to support parsing various data formats, such as PDF, XML, JSON, and even text from Optical Character Recognition (OCR). Specific metadata (e.g., testing methods, instrument models) must be fully transmitted through the interface to maintain data traceability and compliance. Data volumes are typically large, demanding higher response speeds and concurrent processing capabilities from the interface.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`1500 tokens`	Balances information density and model processing efficiency, preventing context overflow.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the parsing time for large PDF documents or complex XML files.
`Chunk size`	`800 characters`	Preserves semantic integrity of context, suitable for chemical document paragraph structures.
`Recall count`	`Top 10 entries`	Ensures relevant quality control indicators and batch information are sufficiently retrieved.
`Similarity threshold`	`0.75`	Improves retrieval precision, matching key quality parameters and avoiding irrelevant content interference.
`http_timeout`	`120 seconds`	Addresses potential response delays from external LIMS or EBR systems, preventing premature request termination.

Common Pitfalls

HTTP requests return a 504 Gateway Timeout error. This occurs when the external system processes an excessively large data volume or experiences network latency, causing the FastGPT interface to time out.
Document field data received by the external system is empty. This happens if the FastGPT interface encounters OCR errors when parsing scanned PDFs or fails to correctly map to the target field batch_number.
API output response speed is slow. This is due to the workflow including multiple complex document parsing and data validation steps without asynchronous optimization.

Verification Steps

Through FastGPT's debugging interface, check if the HTTP Request module's Response status code is 200 and confirm that the Response Body contains the expected field assay_result.
In the external system, randomly select 5-10 batches of chemical drug quality documents. Compare their key fields purity_percentage and impurity_A_ppm to ensure consistency with data provided by the FastGPT interface.
Perform stress testing using FastGPT's API interface, simulating 50 concurrent user requests. Observe if the average response time is within 5 seconds and check logs for HTTP 5xx errors.
Regularly check FastGPT logs to confirm that no large number of parsing failures occur within the PARSE_FILE_TIMEOUT_SECONDS configured timeout.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.