Data Characteristics in Medical Affairs
Medical affairs data primarily originates from internal research reports, clinical trial data, drug labels, medical journal literature, regulatory documents, and expert consensus. These documents are typically in PDF, Word, or structured database formats. Update frequencies vary from weekly to quarterly, driven by new drug development, clinical trial results, and regulatory policy changes. Document content is highly specialized, containing extensive medical terminology, drug dosages, mechanisms of action, indications, contraindications, and other critical information. Fields and units involve various measurement units (e.g., mg, ml, μg), time units (e.g., hours, days, weeks), and specific medical coding systems (e.g., ICD-10, SNOMED CT). Data structures often include nested tables, charts, and complex medical logic descriptions.
Deployment and Upgrade Constraints from Data Characteristics
The specialized and complex nature of medical affairs data imposes specific requirements on FastGPT deployment and upgrades. First, extensive professional terminology and medical coding systems necessitate optimizing knowledge base segmentation and embedding model selection to ensure accurate semantic understanding. Second, frequent update requirements, especially for new drug launches or clinical guideline revisions, mean the incremental update mechanism for the knowledge base must be efficient and stable, supporting automatic parsing of multi-source data formats. Identifying nested tables and charts in documents requires advanced layout parsing capabilities from the text extraction component. Furthermore, the strictness of regulatory documents makes traceability and version management of knowledge sources a core requirement. Deployments must reserve sufficient storage space and computing resources to handle the storage of massive professional documents and high-concurrency queries.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Medical reports and clinical trial documents can be large, requiring support for large file uploads. |
maxContext | 3000 Tokens | Medical texts have strong contextual relevance, requiring a longer context window to understand complex logic. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex PDF or Word documents can be time-consuming; avoid parse timeouts. |
Chunk size | 800–1200 characters | Balances medical information integrity and retrieval efficiency, ensuring each segment contains sufficient context. |
Recall count | Top 10 entries | Improves recall of relevant medical knowledge for complex queries, covering more potential answers. |
Similarity threshold | Calibrate by measurement | Medical terminology requires high precision; evaluate against specific corpora and models to ensure high relevance recall. |
Common Pitfalls
- After a knowledge base update, the latest indications for a specific drug are not retrievable. This occurs due to incorrect incremental synchronization configuration, where the system fails to correctly identify and merge new and old document versions.
- When calling an external medical database API in a workflow, a
ReferenceError: 'fetch' is not definedoccurs in the JS code. This is because the Node.js environment does not provide the browser'sfetchAPI by default; libraries likenode-fetchare needed as alternatives. - When a user queries a medical concept, the number of returned results is too low or irrelevant. This might be due to a
Similarity threshold(similarity threshold) set too high, leading to overly strict filtering and excluding some valid information.
Verification of Configuration
- Upload a standard medical report containing nested tables and charts. Verify that its content is correctly parsed and indexed, and check the integrity of segments in the knowledge base.
- Perform keyword queries for recently updated drug labels. Confirm the system accurately returns the latest version of relevant information and verifies the document source and version number.
- Design questions with multiple medical terms and complex logic. Test the AI Agent's responses for accuracy, coherence, and ability to cite correct knowledge base segments. Evaluate the combined effect of
Recall countandmaxContext. - Simulate multiple concurrent user queries during peak hours. Monitor system response times to ensure the deployment environment can stably handle the load without timeouts or performance bottlenecks.
The values given 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.