Data Characteristics for This Category
SMO (Site Management Organization) registration and declaration document preparation involves a wide range of data types. These primarily include ethics committee approvals, investigator brochures, clinical trial protocols, informed consent forms, case report forms (CRFs), subject recruitment records, adverse event reports, data management plans, and statistical analysis reports. This data often exists in various formats such as PDF, Word documents, Excel spreadsheets, and images. Data sources are diverse, including Hospital Information Systems (HIS), Clinical Trial Management Systems (CTMS), and Electronic Data Capture (EDC) systems. Data update frequency varies across project stages; for example, ethics committee approvals are typically obtained once at project initiation, while adverse event reports may be generated in real-time. Document structures generally follow ICH-GCP guidelines and specific NMPA (National Medical Products Administration) requirements, exhibiting high standardization and normalization. Fields and units are highly specialized, including dosage units like mg/kg, time units like days, and various medical terms and codes.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The data characteristics of SMO registration and declaration documents impose specific requirements on HTTP interfaces and external system integration. First, diverse and heterogeneous data formats require interfaces with robust file parsing and content extraction capabilities. Examples include recognizing table data within PDF documents or extracting key fields from Word documents. Second, inconsistent data update frequencies necessitate interface support for various triggering mechanisms. For static documents, scheduled synchronization or manual uploads may be used, while real-time data (e.g., adverse events) requires support for webhooks or message queue pushes. Third, highly standardized document structures mean that during data validation, interfaces must strictly adhere to predefined templates and rules, ensuring field completeness, accuracy, and compliance. Finally, specialized fields and units require interfaces to accurately identify and retain their semantics during data transmission and storage, preventing data errors due to unit conversion or misinterpretation of terminology. For example, drug dosage fields must ensure the correct association of values with units and allow comparison with external toxicology databases.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Registration and declaration documents may contain large high-resolution images or detailed reports, leading to large individual file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF or Word documents, especially those with complex tables or images, requires extended parsing time. |
maxContext | 32000 | Ensures that the main content of a standard clinical study protocol or statistical analysis report can be fully loaded and processed. |
Similarity threshold (Similarity Threshold) | 0.85 | Guarantees precise matching of highly relevant text segments when retrieving regulatory provisions or historical approvals. |
Rerank result count (Reranked Results Count) | 10 | Considering that relevant information in regulatory provisions or approvals may be dispersed, increasing the reranked count helps improve recall. |
CHUNK_SIZE | 1000 characters | Registration and declaration documents typically contain long paragraphs and specialized terminology; longer chunks help maintain semantic integrity. |
Common Pitfalls
- When integrating with external CTMS or EDC systems, key fields (e.g., subject ID, adverse event description) in the received data are empty. This occurs because external system field mapping rules are not configured correctly, leading to information loss during data transfer.
- The system fails to parse or incorrectly identifies content in ethics committee approval PDFs. This happens when PDF documents contain scanned images or non-standard fonts, preventing the OCR engine from accurately recognizing text content.
- Occasional timeouts or connection interruptions occur during concurrent API calls. This is due to not properly setting
maxConnectionsortimeoutparameters, leading to resource bottlenecks in high-concurrency scenarios.
How to Verify Configuration
- Upload a standard clinical trial protocol PDF document containing complex tables and embedded images. Check if the system can fully and accurately parse all text content and table data.
- Configure the HTTP interface with an external CTMS and trigger an adverse event data synchronization. Verify that the data fields transferred to FastGPT are identical to the source data in the external system.
- Use a multi-threading tool to simulate high-concurrency requests, continuously calling the knowledge base query interface. Monitor the interface response times to ensure all requests return results within the set timeout threshold.
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.