Data Characteristics
Registration data for respiratory system diseases originate from various sources. These include clinical trial reports, non-clinical study reports, pharmaceutical research data, post-market surveillance data, and guidelines from regulatory bodies. Data updates are influenced by research progress, clinical trial phases, regulatory revisions, and post-market feedback. Updates often occur in intensive phases, interspersed with routine, scattered additions. Documents are structurally complex, containing specialized terminology, dosage units, biomarker data, and imaging reports. Field specificity is high, exemplified by lung function indicators (FEV1, FVC), blood gas analysis (PaO2, PaCO2), and various imaging (CT, MRI) interpretation results. Units range from milliliters and mmHg to international units and percentages. Data are distributed across multiple file formats, such as PDFs, Word documents, Excel spreadsheets, and DICOM image reports.
Constraints on Deployment and Upgrade
The complexity and diversity of respiratory system data impose specific requirements on FastGPT's deployment and upgrade processes. First, the large volume of documents and multiple file formats demand sufficient storage and processing capacity from the deployment environment. File parsing services, in particular, require high stability. Second, the specialized terminology and measurement units necessitate optimizing the vectorization model for the medical domain to improve semantic understanding accuracy. The phased update nature of registration data means that system upgrades must ensure data migration integrity and backward compatibility, preventing knowledge base interruptions or loss. Furthermore, sensitive clinical data require higher standards for deployment security and permission management, ensuring data isolation and compliance. The need for multi-user collaborative data preparation requires upgraded systems to smoothly support multi-tenancy and resource allocation strategies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Respiratory clinical trial reports and imaging analysis reports can be large. Large file uploads must be supported. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDFs and Word documents can take a long time to parse. |
embeddingModel | text-embedding-ada-002 (or equivalent domain-optimized model) | Medical domain terminology is specialized. Generic models may not provide sufficient understanding. |
maxContext | 8000 | Ensures that longer passages, covering medical background and experimental data, can be processed in a single operation. |
Chunk size | 800–1200 characters | Balances context completeness with vectorization efficiency. Reduces the risk of critical information being truncated. |
Recall count | Top 10 entries | Increases the probability of selecting highly relevant document fragments. Addresses the precision requirements of specialized queries. |
Common Pitfalls
- An
ERROR: failed to solve: failed to checksum fierror during Docker image build often indicates a Docker daemon cache issue or improper network proxy settings, leading to dependency package download failures. - After an upgrade, users may observe a significant decrease in knowledge base query relevance. This typically occurs when vector models are not retrained or updated, causing incompatibility between new and old data embeddings.
- In multi-user environments, some users may be unable to access or modify their knowledge bases. This often results from incorrect permission configurations or improperly deployed multi-tenancy strategies.
Verification Steps
- Upload a typical respiratory system clinical trial PDF document containing lung function indicators, blood gas analysis results, and imaging reports. Verify that it parses correctly and segments into the knowledge base.
- Use query statements with respiratory system-specific terminology. Verify that the knowledge base retrieves accurate and relevant document fragments. Check that measurement units in the search results are correct.
- In a multi-user scenario, log in as different users. Attempt to add, delete, modify, and query their respective knowledge bases. Confirm that data isolation and permission control function as expected.
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.