Data Characteristics for This Category
Rare disease registration data comes from diverse sources. These include medical literature, clinical trial reports, pharmaceutical regulatory documents, patient registry data, and gene sequencing results. Update frequencies vary. Medical literature and clinical trial reports may update quarterly or semi-annually. Regulatory documents may release immediately with policy changes. Document structures are complex. They contain unstructured text descriptions, structured tabular data (e.g., dosage regimens, adverse event lists), and semi-structured medical imaging reports. Fields and units are highly specialized. Examples include gene loci (rsID), phenotypes (HP:0000001), drug dosages (mg/kg), and biomarker concentrations (ng/mL). The data volume is large, and specialized terminology is dense. This demands high processing capability and accuracy.
Constraints on Deployment and Upgrade from These Characteristics
The diversity of rare disease data sources requires FastGPT to support multiple data ingestion methods during deployment. These include file uploads, API integration, and database synchronization. The complexity of document structures necessitates enhanced document parsing capabilities. This is especially true for recognizing tables and specialized terminology within PDF and image formats. Inconsistent update frequencies demand robust data synchronization and incremental update mechanisms. This ensures the knowledge base reflects the latest research and regulatory changes promptly. The specialized nature of professional fields and units requires customized entity recognition and information extraction models for knowledge base construction. This guarantees question-answering accuracy. Large data volumes and specialized content challenge computational resources and storage capacity. They also require high stability and response speed for model inference.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Rare disease literature often contains high-resolution images and large datasets, resulting in large file sizes. |
maxContext | 8000 | Ensures the capacity to include complex pathological descriptions, clinical trial details, and regulatory clauses, improving answer completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF documents or reports with complex tables can be time-consuming. |
Chunk size | 1000 characters | Rare disease literature has strong contextual relevance; longer segments help preserve semantic integrity. |
Recall count | Top 10 entries | Increases the number of recalled items to cover more potentially relevant information, addressing the diversity of specialized terminology. |
Similarity threshold | Calibrate by actual measurement | Rare disease terminology requires high precision; testing ensures accurate matching of recalled content. |
Three Common Mistakes
- Knowledge base content updates but question-answering results do not reflect the latest information. This occurs when the data synchronization mechanism is incorrectly configured or scheduled tasks do not execute as expected.
- Uploading large PDF files fails or times out. This manifests as HTTP status codes
413or504. This is often due toUPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSparameters being set too low. - After deployment, some rare disease-specific terminology or drug names are not correctly recognized. Fields in question-answering results are empty or inaccurate. This happens when no customized entity recognition model or vocabulary training has been performed for the rare disease domain.
How to Confirm Correct Configuration
- Upload a rare disease-related PDF document containing the latest clinical trial data. Verify that the knowledge base content has synchronized.
- Ask questions about key information such as disease names, gene loci, and drug dosages. Check the accuracy of relevant fields in the question-answering results.
- Simulate uploading multiple large declaration documents concurrently. Observe system resource usage and file processing time to ensure compliance with expected performance metrics.
- Regularly check system logs for records of file parsing failures, data synchronization anomalies, or model inference errors.
The values provided are common starting points and should be measured against the reader's own 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.