Data Characteristics in This Category
Academic promotion data in the biomedical field primarily originates from clinical research reports, drug inserts, academic journal articles, conference abstracts, and professional medical guidelines. These sources have varying update frequencies: clinical data and guidelines may update annually, while journal articles are continuously published. Document structures typically follow standard medical paper formats, including title, abstract, introduction, methods, results, discussion, and references. Fields involve drug names, indications, dosage and administration, adverse reactions, clinical trial data (e.g., P-values, confidence intervals), and molecular structures. Units strictly adhere to the International System of Units (e.g., mg/kg, mmol/L) and are often accompanied by specialized medical abbreviations.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The specialized and structured nature of academic promotion data imposes specific constraints on deployment and upgrade. For instance, the presence of numerous specialized terms and abbreviations requires embedded models to have high-precision recognition capabilities to avoid semantic deviations. The sensitivity of numerical values and units in clinical trial data demands knowledge base segmentation strategies that effectively preserve context, preventing incorrect truncation of values. The uncertain frequency of document updates necessitates flexible data synchronization mechanisms to incorporate the latest research findings promptly. Additionally, non-textual information like molecular structures may require preprocessing or specific embedding methods, impacting the selection and configuration of file parsing components. For the deployment environment, continuous operational stability of models and knowledge bases is crucial to ensure uninterrupted consultation services.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large research reports and drug inserts. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Ensures context integrity for clinical data and specialized terminology. |
Recall count (Recall Count) | Top 8 entries (top 8) | Increases recall of highly relevant academic literature segments. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves accuracy of professional query results, reducing irrelevant information. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Optimizes the relevance of academic information presented to the user. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing of complex PDF documents, preventing timeout interruptions. |
Common Mistakes
- Lost configured models and applications after system restart: This usually occurs when Docker container volumes are not correctly persisted, causing data to revert to its initial state after a container restart.
- Workflow debugging error
workflow error {"message":"Dangerous behavio: This may stem from a model's safety policy being triggered, where drug or disease information mentioned in academic promotion content is mistakenly flagged as sensitive. - Inability to integrate a locally deployed embedding model into the knowledge base: This often results from incorrect inter-service network configuration in
docker-compose.ymlor theONAPIaddress not pointing to the correct local model service port.
Verification Steps
- Upload a PDF document containing complex clinical trial data. Check if the knowledge base segmentation fully retains numerical and unit information.
- Test with a query containing various medical abbreviations. Verify if the model accurately understands and provides relevant results.
- Simulate a device restart. Check if configured models and knowledge base applications still exist and are accessible.
- Ask questions about a specific drug's indications, dosage, and administration. Cross-reference the accuracy of the returned information with authoritative medical guidelines.
The values provided are common starting points and should be measured against your 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.