Data Characteristics
Medical Information (MI) data in a standard answer library typically originates from pharmaceutical companies' medical affairs departments. This data undergoes rigorous medical and compliance review. Update frequency is stable, usually quarterly, or when significant updates occur in drug inserts or clinical guidelines. The document structure is highly standardized, often organized as Question & Answer (Q&A) pairs. Each answer includes fields such as question description, standard answer, references, and approval date. Field content is precise; for example, drug dosages include specific values and units (e.g., mg, ml), and disease diagnosis information references International Classification of Diseases (ICD) codes. Data formats are often structured JSON or XML files, and commonly appear as CSV or Excel tables.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The characteristics of standard answer library data impose specific requirements on deployment and upgrade processes. First, the compliance of data sources dictates that data import must occur via secure channels with strict access control to prevent unauthorized access or tampering. Second, the stable yet regular update frequency requires the system to have automated or semi-automated data synchronization and version management mechanisms. This ensures that the latest approved answer content is always used. The highly structured nature of documents allows for efficient and accurate parsing and indexing. For instance, fields containing dosage units require precise entity recognition rules to avoid misinterpretation. Furthermore, the presence of reference fields necessitates that the system can trace sources when providing answers, which demands robust metadata management and retrieval capabilities from the knowledge base.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Medical documents are often large, containing detailed information and charts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures complex structured files have sufficient time to complete parsing. |
maxContext | 1024 characters | Guarantees MI answers cover complete medical concepts, preventing truncation. |
Similarity threshold | 0.8 | Ensures recalled answers are highly relevant to user queries. |
Rerank result count | Top 3 entries | Prioritizes displaying the most relevant and authoritative few answers. |
Data Sync Cycle | Calibrate by actual measurement | Dynamically adjusts based on actual data update frequency and compliance requirements. |
Common Pitfalls
- Symptom: After data import, some medical terms or dosage units appear garbled or missing in responses. Cause: Character encoding is not correctly configured, or the parser is not optimized for specific medical fields.
- Symptom: After a system upgrade, the old version of the knowledge base fails to load, reporting
Database Schema Mismatch. Cause: Database migration scripts were not executed during the upgrade, or the migration script is incompatible with the current data version. - Symptom: Reference links cited in response results are broken or point to incorrect content. Cause: Reference fields were not validated during data import, or external links changed due to version updates.
Verification
- Select questions of varying complexity and test if FastGPT accurately recalls standard answers. Verify consistency between the response content and the original data.
- Check system logs to confirm no significant errors or warning messages during data import, index building, and model inference.
- Compare response performance on the same questions before and after an upgrade. Ensure response quality has not degraded. Verify the accuracy of key fields (e.g., dosage, ICD codes) meets expectations.
- Simulate the data update process. Verify that the new version of the standard answer library can be smoothly imported and replace the old version. Check that historical answer records maintain traceability.
Note: 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.