Data Characteristics
Dermatology pharmacovigilance data originates from clinical trial reports, real-world studies, medical literature, patient case records, and adverse event reporting systems from drug regulatory agencies. This data updates frequently, especially after new drug launches or new adverse reaction discoveries. Data documents typically contain both structured and unstructured sections. Structured data includes patient demographics, medication history, adverse event occurrence time, description, severity, outcome, and relevant laboratory test results. Unstructured data describes the detailed adverse reaction process, physician diagnostic opinions, and patient complaints in free text. Field units vary, including days to adverse reaction onset, percentage of skin lesion area, dosage units (mg/kg, g/m²), and specific biomarker concentration units (ng/mL, U/L).
Deployment and Upgrade Constraints
High update frequency of dermatology pharmacovigilance data requires FastGPT deployments to have efficient data ingestion and index update mechanisms. This is critical for quickly integrating the latest information into the knowledge base, especially for newly released drugs or new adverse reaction signals. The coexistence of structured and unstructured data necessitates different processing strategies during deployment. Structured data supports precise retrieval, while unstructured text relies on advanced semantic understanding and vectorization. Free text contains extensive medical terminology, disease descriptions, and symptom details, demanding high domain adaptability from tokenizers and embedding models. The diversity of specific field units requires meticulous handling during data cleaning and standardization to prevent query deviations caused by inconsistent units. The upgrade capability of the PgVector plugin directly impacts vector retrieval efficiency and accuracy, particularly when processing large volumes of new data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Dermatology clinical trial reports and literature often include charts and detailed case studies, resulting in large file sizes. |
Chunk size (Chunk Length) | 800–1000 characters (characters) | Dermatology adverse reaction descriptions are often rich in detail, requiring longer contexts to preserve semantic integrity. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures coverage of various possible similar adverse reaction descriptions during the initial recall phase. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall and precision; dermatology symptom descriptions can have subtle differences. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | After reranking, select the most relevant few results for further analysis by engineers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large PDFs or complex structured reports can be time-consuming. |
Common Misconfigurations
- When uploading large PDF documents, logs show
File Parsing Timeout(file parsing timeout). This occurs whenPARSE_FILE_TIMEOUT_SECONDSis not increased to account for the complexity of dermatology reports. - Queries for specific adverse reaction symptoms return too few or irrelevant results. This can happen if the
Similarity threshold(similarity threshold) is set too high, filtering out semantically similar but differently worded documents. - After a knowledge base update, drug names in new data are not recognized or retrieved. This indicates a failure to promptly update or fine-tune models to adapt to new dermatology drug nomenclature or terminology.
Verification Steps
- Upload and parse a typical report containing various dermatology adverse reaction symptoms. Confirm that all key information is correctly extracted and indexed.
- Conduct multiple rounds of queries for a known specific dermatology adverse reaction. Verify that retrieval results include relevant literature and cases, and confirm result completeness.
- Regularly check the knowledge base's
index status. Confirm that new and updated data are reflected in search results in a timely manner. - Simulate user questions. Verify the system's understanding of dermatology-specific terminology and abbreviations to ensure semantic matching accuracy meets the expected threshold.
The values provided are common starting points. Measure them 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.