Data Characteristics
Medical affairs regulatory submission documents draw from diverse sources. These include clinical trial reports, pharmacology and toxicology studies, manufacturing process files, quality standards, non-clinical study reports, medical literature, and regulatory documents. Data typically exists as structured documents (e.g., PDF, Word, Excel), semi-structured data (e.g., eCTD modules in XML format), and unstructured text (e.g., research report bodies). Data update frequencies vary. Clinical trial data continuously generates during trials. Regulatory documents may update quarterly or annually. Some basic research data remains relatively stable. Document structures are complex, containing extensive specialized terminology, dosage units, statistical data, and charts. Field names and units strictly follow pharmaceutical and medical standards. Examples include dosage units like mg/kg, IU/mL, time units like hours, days, weeks, and statistical indicators like p-value, CI.
Constraints Imposed by These Characteristics on HTTP Interfaces and External Systems
Diverse data sources require HTTP interfaces to handle multiple file formats and data structures, along with flexible parsing capabilities. Varying update frequencies mean interfaces need to support both scheduled synchronization and event-triggered data retrieval modes to ensure data timeliness. Complex document structures and specialized terminology demand high text processing capabilities. This requires external systems to provide professional semantic understanding and entity recognition services. For example, integrating medical terminology ontologies can accurately identify diseases, drugs, and targets. The strictness of fields and units necessitates rigorous data validation and standardization during data transmission and parsing. This prevents information discrepancies caused by inconsistent units or incorrect data formats. Furthermore, the sensitive nature of regulatory submission documents dictates encrypted transmission for interfaces. It also demands high standards for external system authentication and authorization mechanisms to ensure data security and compliance.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
externalApiUrl | External system API address | Determined by the API documentation provided by the specific external data service provider |
requestTimeoutSeconds | 600 seconds | Most regulatory submission documents are large, and transmission and processing are time-consuming |
maxConcurrentRequests | 5-10 | Balances data synchronization efficiency with external system load capacity |
authenticationMethod | Bearer Token | Ensures authentication security for data transmission, aligning with common industry practices |
dataSchemaValidation | Enabled | Strictly validates the type, range, and units of medical data fields to prevent data anomalies |
fileTypeWhitelist | pdf, docx, xml, xlsx | Only allows receiving and processing common regulatory submission document file types |
Three Common Mistakes
- HTTP request returns a 403 error, but parameters appear correct: External systems often have strict IP whitelists or user agent restrictions, and the request is unauthorized.
- Received text content is garbled or partially missing: Character encoding is set incorrectly, or the data volume returned by the external system is too large, leading to truncation.
- Model responses cite incorrect dosages or units: Medical professional fields were not standardized during data parsing, or unit conversion logic was flawed.
How to Confirm Proper Configuration
- Use the test interface provided by the external system. Send a simulated request via FastGPT to confirm receipt of the correct response status code and initial data structure.
- Select a sample regulatory submission document containing various file types and complex medical terminology. Upload it to FastGPT and check if it is correctly parsed and segmented.
- Create a simple Q&A session in FastGPT. Ask questions about key information in the imported documents (e.g., drug dosage, clinical endpoints). Verify the accuracy of the answers and the cited data sources.
- Monitor network traffic and logs between FastGPT and the external system. Confirm request frequency, response time, and any error messages during data transmission.
The values provided are common starting points and 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.