Deployment and Upgrade for Infectious Disease Products

Characteristics of Infectious Disease Data

Infectious disease data comes from various sources, including authoritative medical guidelines, global disease surveillance reports, clinical trial data, drug inserts, and pathogen genomic information. This data updates frequently. New or mutated pathogens, in particular, can lead to quarterly or even monthly updates for guidelines and drug indications. Document structures vary, encompassing structured database records, semi-structured clinical research reports, and unstructured academic papers and expert consensus. Fields include pathogen names, hosts, transmission routes, pathogenic mechanisms, diagnostic methods, treatment plans, drug dosages, and drug resistance information. Units involve dosage (mg/kg), time (hours, days), concentration (IU/mL), and measured values (copies/mL), often accompanied by specific medical abbreviations.

Constraints on Deployment and Upgrade from Data Characteristics

The high update frequency of infectious disease data requires deployment systems with efficient data synchronization and index update mechanisms to ensure knowledge base timeliness. Diverse document structures challenge the data preprocessing module, demanding flexible parsing capabilities for different document formats. Specific field types and medical abbreviations necessitate optimization of tokenizers and entity recognition models for the medical domain to accurately interpret query intent. The accuracy of critical information like drug dosages and resistance directly impacts consultation quality. Therefore, strict data consistency checks are essential during deployment, and regression testing is required during upgrades to prevent ambiguity introduced by data changes. High-concurrency consultation scenarios also demand faster response times and lower resource consumption.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Infectious disease guidelines or literature often contain numerous charts and figures, resulting in large file sizes.
`maxContext`	`1500 tokens`	Ensures sufficient contextual information is included when handling complex cases or multiple infection consultations.
`Chunk size`	`800 characters`	Balances semantic completeness with retrieval efficiency, preventing individual segments from becoming too long and diluting the topic.
`Recall count`	`Top 8 entries`	Increases the likelihood of recalling relevant information from the knowledge base, covering more potential answers.
`Similarity threshold`	`0.78`	Ensures recalled knowledge snippets are highly relevant to the query intent, reducing noise.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample parsing time for large PDF or multimedia files, preventing timeout interruptions.

Common Mistakes

Log displays Error: ETIMEDOUT or Connection refused: This usually indicates inter-container network configuration issues. For example, the FastGPT service container cannot connect to the database or vector database container. Check the network names and port mappings between services in docker-compose.yml.
Application interface is inaccessible, but container logs show listening on port 3001: This means the backend service has started, but the frontend or reverse proxy is misconfigured and fails to route external requests to the backend 3001 port. Check the nginx or Caddy reverse proxy configuration to confirm proxy_pass points to http://localhost:3001.
Knowledge base query results are inaccurate or lack critical information: This may occur if the document segmentation strategy used during knowledge base data import is unsuitable for the structural characteristics of infectious disease literature, leading to important information being split or context loss. Adjust the Chunk size and Chunk overlap parameters and re-import the data.

Verification of Configuration

Upload and parse an infectious disease guideline containing complex tables and figures. Check if the knowledge base correctly extracts and indexes all key information, such as drug dosages, pathogen classifications, and treatment processes.
Simulate multiple concurrent users consulting on different infectious diseases. Observe if system response times are stable. Check resource usage displayed by tools like docker stats or htop.
Query for frequently updated content, such as pathogen-specific drug resistance information or vaccination recommendations. Verify that the system's answers are based on the latest imported data version.

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.