Data Characteristics for this Category
Autoimmune disease regulations and SOP documents typically originate from national drug administration agencies, internal control documents of medical institutions, clinical practice guidelines, and technical specifications published by industry associations. Data update frequency is relatively low, primarily occurring with new drug approvals, adjustments to treatment protocols, or revisions to policies and regulations. Document types are mainly PDF, Word, and some HTML pages. Structural consistency varies, with extensive medical terminology, abbreviations, charts, and flowcharts. Fields often include disease names, diagnostic criteria, treatment plans, drug dosages, adverse reactions, follow-up requirements, and ethical approvals. Units cover milligrams (mg), milliliters (ml), days, weeks, months for time and dosage, with some data presented as percentages.
Constraints Imposed by these Characteristics on "Deployment and Upgrade"
The low update frequency of autoimmune disease regulatory data means that initial deployment can involve large-scale batch imports, reducing the need for real-time synchronization mechanisms. Documents contain extensive unstructured content and complex charts, requiring robust parsers. Specifically, when extracting key information, parsers must identify and skip or correctly process chart content. The prevalence of medical terminology and abbreviations demands strong semantic understanding from the RAG system during vectorization and retrieval. This enables matching colloquial user queries with professional terms in documents. Additionally, SOPs from different medical institutions may have subtle differences. Deployment must consider conflict resolution mechanisms for multi-source documents to ensure accuracy and consistency of answers.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Autoimmune SOP documents are often large, including charts and attachments |
maxContext | 4000 characters | Needs sufficient context to understand complex medical concepts and processes |
Chunk size | 800–1200 characters | Balances semantic completeness with retrieval accuracy |
Similarity threshold | 0.75 | Ensures retrieved regulatory clauses are highly relevant to the query |
Rerank result count | Top 5 entries | Prioritizes the most relevant and re-ranked regulatory clauses |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDF or Word documents |
Three Common Mistakes
- The deployed FastGPT service is inaccessible within the local area network. This manifests as browser connection timeouts or inability to access pages. Reasons typically include firewall ports not being open, or incorrect
HOSTorPORTconfiguration indocker-compose.yml, causing the service to listen only on the local loopback address. - After document upload, some critical fields (e.g., drug dosages, diagnostic criteria) are missing or inaccurate in the question-answering results. This occurs because the document parser fails to correctly identify text content within complex tables or images, preventing effective extraction and vectorization of key information.
- Irrelevant regulatory clauses frequently appear in question-answering results, or there are significant semantic misunderstandings of user queries. This is often due to insufficient generalization capability of the vector model, which fails to fully understand medical terminology and abbreviations, leading to reduced retrieval relevance.
How to Verify Correct Configuration
- Upload an autoimmune SOP document containing complex tables and medical terminology via the FastGPT administration interface. Check if parsing is successful and verify that document content is fully imported.
- Simulate user questions, such as "What are the diagnostic criteria for Systemic Lupus Erythematosus?". Verify that the answers accurately cite specific clauses from the document and assess their semantic relevance.
- Attempt to access the FastGPT service from other devices within the local area network. Ensure normal login and usage to verify network configuration and port open status.
- Check backend logs for an absence of numerous errors or timeout warnings during document parsing. Pay particular attention to
PARSE_FILE_TIMEOUT_SECONDS-related logs to ensure large documents are processed effectively.
The values provided 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.