Deployment and Upgrades for Neurodegenerative Disease

Data Characteristics

Protocol and Standard Operating Procedure (SOP) documents in the neurodegenerative disease domain originate from pharmaceutical companies, Contract Research Organizations (CROs), hospital ethics committees, and regulatory bodies. These documents have a relatively low update frequency, typically when new drug development milestones are announced, clinical trial protocols are revised, or national/international guidelines are updated. Document structures are complex, often in PDF or Word formats, containing numerous figures, tables, appendices, and cross-references. The text is highly specialized, covering drug names, dosage units (e.g., mg/kg), timeframes (e.g., once weekly), patient inclusion/exclusion criteria, and biomarker detection methods and interpretation (e.g., amyloid, Tau protein). Field expressions are precise and highly context-dependent; for example, "treatment cycle" could mean 12 weeks or 24 months, requiring interpretation within specific clauses.

Constraints on Deployment and Upgrades

The complexity of neurodegenerative disease protocol data imposes specific requirements on deployment and upgrades. First, PDF and Word formats demand robust file parsing capabilities to accurately extract text and structural information, especially for tables and figures. The low update frequency means significant effort is required for high-quality data cleaning and annotation during initial knowledge base construction. Subsequent maintenance focuses on incremental update compatibility and efficiency. The presence of numerous specialized terms and abbreviations requires the RAG (Retrieval-Augmented Generation) model's embedding layer to accurately understand their semantics, preventing inaccurate recall due to lexical ambiguity. The strictness of fields and units dictates that the model must precisely identify and output correct values and units during Q&A, distinguishing between mg and μg. Furthermore, extensive cross-references necessitate knowledge graphs or multi-document chain retrieval to ensure answer completeness and logical consistency.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF or Word documents, especially those with complex figures and tables, takes longer.
`Chunk Size`	`800–1200 characters`	Ensures each knowledge chunk contains sufficient context for complete explanations of specialized terms and concepts.
`Recall Count`	`Top 5`	Neurodegenerative disease protocol Q&A requires high accuracy, needing multiple relevant documents to support answers and reduce hallucinations.
`Similarity Threshold`	`0.75–0.80`	Precisely matches professional concepts and clauses, avoids interference from low-relevance documents, and ensures answer professionalism.
`Rerank Return Count`	`3`	Further filters recalled documents to prioritize the most relevant and information-dense segments for generation.
`MAX_MEMORY_SIZE`	`4 GB`	Processing complex document structures and embedding/retrieving large volumes of specialized vocabulary requires significant memory.

Common Mistakes

After local startup, API port 3000 is inaccessible, but container logs show port 3001 is normal. This usually indicates incorrect reverse proxy configuration or firewall blocking of port 3000.
After configuring "Conversation Guide" in the knowledge base, multi-turn conversations fail. This might be due to incorrect context passing mechanisms in the workflow or a maxContext parameter set too low.
After uploading large PDF documents, file parsing remains stalled or reports errors for an extended period. This often occurs because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, not allowing enough time for the parser to handle complex document structures.

Verification Steps

Upload a PDF of a neurodegenerative disease clinical trial protocol containing complex tables and figures. Observe the file parsing progress to ensure it completes successfully and generates knowledge chunks.
Ask questions about several protocol clauses containing specialized terms and cross-references. Check if FastGPT's answers accurately cite document content, use correct professional terminology, and avoid hallucinations.
Conduct multi-turn conversation tests within the application. Ask sequential questions about drug dosages and patient inclusion criteria to verify the model maintains contextual coherence and provides logically consistent answers.
Check docker logs output to confirm no memory overflow or 500 error codes occur during high-concurrency requests, ensuring system stability.

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.