Data Characteristics in This Category
In clinical trial pre-screening scenarios, pharmaceutical e-commerce platforms primarily process user-submitted medical records, physical examination reports, medication history, and internal user behavior data. This data often exists as unstructured text, such as scanned PDF medical records, image-based lab reports, handwritten doctor's notes, and structured personal health records. Data updates frequently; users can upload new test results or modify personal information at any time. Document structures are complex and diverse, lacking uniform standards, and contain extensive medical terminology, abbreviations, and numerical information. Fields and units involve disease diagnoses (e.g., ICD-10 codes), laboratory test results (e.g., mg/dL, mmol/L), and drug dosages (e.g., mg, ml). Inconsistent units increase the difficulty of data parsing.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The highly unstructured and diverse data from pharmaceutical e-commerce platforms necessitate configuring FastGPT with robust file parsing capabilities during deployment. The abundance of medical terminology and abbreviations makes the selection of tokenizers and embedding models critical for accurate semantic understanding. Frequent data updates require FastGPT's knowledge base synchronization mechanism to support incremental updates without disrupting service continuity. Complex and non-standardized document structures demand more stringent data cleaning and preprocessing, requiring customized parsing strategies for different data sources and formats. Inconsistent fields and units require standardization during knowledge base construction to prevent ambiguity during retrieval.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large image data within medical reports by increasing the file upload limit. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time for parsing large PDF or image files. |
maxContext | 1000 characters | Ensures capture of critical contextual information from medical records, preventing truncation of important diagnoses. |
Chunk size | 300–500 characters | Balances information density per segment with embedding model processing efficiency, suitable for medical texts. |
Recall count | Top 10 entries | Increases initial recall coverage, improving the hit rate of relevant information. |
Similarity threshold | Calibrate based on actual measurements | Adjusts through testing to optimize recall precision, considering the similarity of medical domain terminology. |
Three Common Pitfalls
- After a knowledge base update, user query results do not reflect the latest data in a timely manner. This may be due to the knowledge base index not triggering a rebuild or incorrect incremental synchronization configuration.
- Some medical record files fail to parse after upload, with logs indicating unsupported file types or parsing timeouts. This may be due to parser configurations not covering specific file formats or insufficient timeout settings.
- Clinical trial pre-screening results show low accuracy. This may be due to the tokenizer failing to correctly identify specialized medical terms, leading to semantic understanding deviations.
How to Verify Correct Configuration
- Upload medical record files in various formats (e.g., PDF, PNG, JPG) to confirm successful parsing and knowledge chunk generation for all.
- Simulate a user submitting a medical record with new diagnoses or medications. Verify that query results reflect this new information after the knowledge base update.
- Perform pre-screening queries on a set of medical records known to meet or not meet specific clinical trial criteria. Compare pre-screening results with expectations and adjust the
Similarity thresholdas needed.
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.