Deployment and Upgrade for Infectious Disease Registration

Data Characteristics in this Category

Infectious disease registration dossiers draw data from diverse sources. These typically include clinical trial reports, non-clinical study reports, pharmacovigilance data, epidemiological data, microbiology test reports, and guidelines/regulations from global regulatory bodies. Data updates frequently, especially during new infectious disease outbreaks or when drug resistance variations emerge, leading to rapid iteration of relevant guidelines and clinical data. Document structures are complex and varied, encompassing ICH E3 clinical study report formats, CTD (Common Technical Document) modular structures, WHO guidelines, and CDC reports. Fields and units are highly specialized, for example, minimum inhibitory concentration (MIC, unit µg/mL), viral load (copies/mL), antibody titer (IU/mL), infection rate (%), and incidence rate (per hundred thousand people). Data often contains numerous tables, charts, and medical images. Text content involves specialized terminology, abbreviations, and descriptions of complex causal chains.

Constraints Imposed by these Characteristics on "Deployment and Upgrade"

The data characteristics of infectious disease registration dossiers impose specific requirements on FastGPT's deployment and upgrade. Frequent updates and heterogeneous data sources necessitate efficient and stable mechanisms for data synchronization and index rebuilding to ensure the timeliness and accuracy of the knowledge base. Complex document structures and specialized field units require more refined text segmentation strategies and entity recognition capabilities to prevent loss or confusion of critical information. For example, MIC values in microbiology test reports must be accurately identified and associated with drug dosages. Simultaneously, the presence of numerous medical images and charts challenges file parsing capabilities, potentially requiring enhanced image recognition and table structural extraction functions. During upgrades, compatibility between new and old data formats must be considered, as well as the model's ability to quickly learn and adapt to new knowledge, ensuring upgrades do not lead to degradation or bias of existing knowledge.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports and CTD modules are often large, requiring support for large file uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing PDF files with numerous charts and complex tables can take a long time.
`Chunk size`	`800-1200 characters`	Ensures completeness of medical terminology and context, preventing semantic truncation.
`Overlap Length`	`150 characters`	Guarantees semantic continuity between adjacent paragraphs, especially when describing complex pathological processes.
`Similarity threshold`	`0.75-0.85`	Improves retrieval accuracy, filtering out document segments with low relevance to infectious diseases.
`Rerank result count`	`Top 10 entries`	Ensures the most precise key data can be filtered from a large amount of relevant information under complex queries.

Three Common Mistakes

A Rerank model returns a 401 Unauthorized error after deployment. This is typically due to incorrect API Key or access credential configuration, or network policy restrictions on inter-service communication in private deployments.
After upgrading FastGPT, some Deepseek models cannot be called correctly, and logs show invalid model API keys. This often occurs because the new version has updated the model authentication mechanism, requiring reconfiguration or refreshing of the API Key.
After uploading knowledge base documents, critical values (such as MIC values) from microbiology test reports are missing or incorrect in retrieval results. This happens because the default file parser fails to correctly identify and extract complex tables or specific medical fields.

How to Confirm Correct Configuration

Upload a typical clinical trial report (PDF format). Observe its segmentation count and content completeness, ensuring critical data and specialized terminology are not fragmented.
Retrieve information from a document containing microbiology susceptibility results. Query the MIC value for a specific pathogen and verify that the returned value matches the original document.
After updating epidemiological data, perform relevant epidemic trend queries. Verify that the system can recall the latest published data and analysis reports.
Test the document upload function for different file sizes through the FastGPT interface. Confirm that large file uploads do not result in timeouts or failures.

Note: The values provided are common starting points and should be measured against specific 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.