Data Characteristics
Dermatology protocol data primarily originates from diagnostic and treatment guidelines, clinical pathways, drug inserts published by national health authorities, and hospital-internal Standard Operating Procedures (SOPs). These documents are typically in PDF, Word, or Markdown formats. Content covers disease diagnostic criteria, treatment plans, medication specifications, and operational procedures. Update frequency is relatively stable; national guidelines are revised annually or bi-annually, while hospital SOPs are adjusted quarterly or semi-annually based on practical situations and policy changes. Document structures are rigorous, containing extensive medical terminology, drug names, dosage units (e.g., mg, ml, IU), and time units (e.g., days, weeks). Some documents may include complex tables and images describing disease classifications or procedural steps.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The highly specialized and standardized nature of dermatology protocol data places specific demands on deployment environment performance and configuration. The abundance of medical and specialized terms requires models to possess strong semantic understanding capabilities to prevent recall deviations caused by lexical ambiguity. Dosage and time units, along with complex structures like tables and images, increase text parsing difficulty, necessitating longer parsing timeouts and more refined segmentation strategies. Frequent update cycles mean the knowledge base must support efficient incremental update mechanisms to ensure the timeliness and accuracy of Q&A content. Furthermore, given the involvement in medical decision-making, the accuracy and completeness of recall results are critical. The system must handle ambiguous user queries and provide answers with clear sources to meet the rigor required in the medical field.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Dermatology guidelines or SOP documents are often large; this ensures full file uploads. |
maxContext | 1500 characters | Medical documents have strong contextual relevance; increasing the context window captures more details. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex tables and extensive medical terminology in PDF/Word documents requires longer parsing times. |
Chunk size | 600–800 characters | Balances contextual integrity of long paragraphs with retrieval efficiency of short paragraphs, avoiding critical information segmentation. |
Recall count | Top 8 entries | Increases recall coverage to address multiple potentially relevant knowledge points in medical Q&A. |
Similarity threshold | 0.78 | Ensures highly relevant knowledge points are recalled to the user's question, reducing inaccurate answers. |
Common Pitfalls
- Knowledge base query results inconsistent with model output: Unreasonable knowledge base segmentation strategies lead to key information being split or missing, preventing the model from effectively structuring answers.
- Port 3000 inaccessible after deployment: Firewall not configured to open the port, or container network configuration errors prevent external requests from reaching the service.
- Knowledge base query performance degradation after upgrade: Issues during the new version's index reconstruction process, or incompatibility between old and new index structures, lead to inefficient queries.
Verification Steps
- Upload multiple typical dermatology diagnostic guidelines or SOP files. Check if file parsing status shows "success" and verify that segmented text is complete and logically coherent.
- For uploaded documents, ask questions containing specialized terms and dosage units. Verify the system accurately recalls relevant passages and that recalled passages contain correct data and units.
- Simulate daily update scenarios by performing incremental updates on the existing knowledge base. Query relevant content before and after the update to confirm changes have taken effect and have not impacted the accuracy of existing knowledge.
- Access the deployed service using different browsers (e.g., Chrome, Edge). Confirm all functional interfaces load and interact normally without front-end errors.
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.