HTTP Interface and External Systems for siRNA Nucleic Acid

Data Characteristics

siRNA nucleic acid drug clinical trial data is highly heterogeneous and time-sensitive. Data sources include internal databases from clinical trial institutions, public databases from regulatory bodies like the National Medical Products Administration (NMPA) and the U.S. Food and Drug Administration (FDA), and academic and clinical trial registration platforms such as PubMed and ClinicalTrials.gov. Data update frequencies vary. Regulatory databases might update weekly or monthly, while academic platforms could update in real-time. Document structures typically include structured clinical trial protocols (e.g., study design, inclusion criteria, exclusion criteria, dosing regimens), unstructured safety reports (adverse event descriptions), and efficacy evaluation reports (biomarkers, clinical endpoint data). Specific fields for siRNA nucleic acid drugs include nucleic acid sequences, target genes, delivery system types, and dose units (e.g., nmol/kg). These fields are uncommon in conventional small or large molecule drugs.

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

The high heterogeneity of siRNA nucleic acid drug clinical trial data requires the HTTP interface to have robust data parsing and standardization capabilities. Due to diverse data sources and varying update frequencies, external systems must aggregate data from multiple sources and handle data conflicts and redundancy between them. For example, trial status updates from ClinicalTrials.gov might be faster than those from the NMPA database. The presence of unstructured text, such as adverse event reports, challenges the interface's data preprocessing stage, requiring more sophisticated text extraction and entity recognition techniques. siRNA-specific fields like target genes and delivery systems mean that data model design must define additional data types and validation rules for these specific fields to ensure data integrity. Dose units such as nmol/kg require the interface to correctly identify and convert units during data ingestion to avoid potential calculation errors. High time-sensitivity demands that the interface has near real-time data synchronization mechanisms to ensure clinical pre-screening results are based on the latest information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000 Tokens`	siRNA trial protocol texts are lengthy, requiring sufficient context to understand complex inclusion/exclusion criteria.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Text extraction and structuring from large clinical trial reports (PDF/DOCX) takes considerable time.
`Chunk size` (Chunk Length)	`800-1200 characters`	Adapts to the paragraph logic of clinical trial texts, preventing critical information from being truncated.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recall results are highly relevant to pre-screening conditions, reducing false positives.
`HTTP_REQUEST_TIMEOUT_MS`	`30000 ms`	Addresses slow response times or large data volumes from external regulatory database interfaces.
`API_RATE_LIMIT_PER_MINUTE`	`Calibrate by actual measurement`	Dynamically adjusts based on external data source API call frequency limits and system load capacity.

Common Pitfalls

External systems calling the FastGPT interface might encounter a 400 Bad Request error. This often happens when the JSON structure in the request body does not meet expectations, especially when passing siRNA-specific parameters like nucleic acid sequences where field names or data types do not match.
Key inclusion or exclusion criteria information might be missing from clinical trial pre-screening results. This typically results from an improper knowledge base chunking strategy, such as Chunk size (Chunk Length) being too small, leading to a complete sentence being truncated, or text extraction failing to effectively identify critical information in unstructured reports.
Frequent 504 Gateway Timeout errors might occur when calling external regulatory databases for the latest trial status. This usually indicates that HTTP_REQUEST_TIMEOUT_MS is configured too short, not allowing enough time for the remote server to process the request and return a large amount of data.

Verification Steps

Use FastGPT's workflow testing feature. Input queries containing siRNA-specific information (e.g., target genes, delivery systems). Check if the output accurately identifies and references relevant knowledge base content, and compare it with expected results.
Monitor logs of external systems calling the FastGPT API. Observe for HTTP status codes other than 200 OK. Analyze error details, especially regarding the correct passing of the dataId parameter.
In the FastGPT knowledge base management interface, randomly select several siRNA clinical trial documents. Preview their chunking effects to ensure critical information (e.g., dose unit nmol/kg, inclusion criteria) is complete within a single chunk, and that chunking depth meets expectations.
Perform a simulated clinical trial pre-screening. Send multiple concurrent queries to the FastGPT interface. Observe the external system's data processing pipeline to ensure stable system response times and good data consistency under the preset concurrency load.

The values provided are common starting points. They should be measured 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.