Deployment and Upgrade for Autoimmune Regulatory Submission

Data Characteristics in Autoimmune Regulatory Submissions

Autoimmune disease regulatory submission data encompasses extensive biological, pharmacological, clinical trial, and manufacturing quality control information. Data sources are diverse, including clinical trial databases (e.g., ClinicalTrials.gov, ICH GCP E6(R2) trial reports), pharmaceutical research reports, toxicology research reports, regulatory guidelines, and public information on similar approved drugs. Data update frequencies vary. Clinical trial data updates periodically during and after trials, while pharmaceutical and toxicology data remain relatively stable. Regulatory guidelines revise regularly. Document structures typically follow the ICH Common Technical Document (CTD) format, divided into Modules 1 to 5, covering administrative information, summaries, quality modules, non-clinical study reports, and clinical study reports. Fields and units are highly specialized. For example, pharmacokinetic data includes Cmax (maximum plasma concentration, unit ng/mL), Tmax (time to reach Cmax, unit h), AUC (area under the curve, unit ng·h/mL). Immunological indicators include CRP (C-reactive protein, unit mg/L), ESR (erythrocyte sedimentation rate, unit mm/h), and SNP site information in gene sequencing data.

Constraints from Data Characteristics on Deployment and Upgrade

The complex data characteristics of autoimmune regulatory submissions impose specific constraints on deployment and upgrade. Diverse, heterogeneous data requires FastGPT to robustly parse various formats like PDF, Word, and Excel during data import, and handle encoding and structural differences across data sources. Frequently updated clinical trial data means the knowledge base update mechanism needs to support incremental updates. The CRON_SCHEDULE parameter directly impacts data freshness. The hierarchical structure of CTD documents requires FastGPT to identify and maintain logical relationships between sections during document chunking, preventing semantic breaks. The presence of specialized fields and units demands stronger domain knowledge adaptability from the model for understanding and content generation. This may require integrating or fine-tuning large models with biomedical backgrounds during model deployment, pointing to these specific models via AI_PROXY_URL. Additionally, sensitive data handling requires attention to data security and compliance. Strict control over data storage and access permissions during deployment ensures adherence to regulations like GDPR or HIPAA.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1024 MB`	Regulatory submission documents often include large PDF files. This ensures complete uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files takes time. This prevents timeout failures.
`Chunk size` (Chunk Length)	`800–1200 characters`	CTD document semantic units are long. This maintains contextual integrity.
`Recall count` (Recall Count)	`Top 8 entries`	Complex queries need more context support. This improves recall accuracy.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Domain terminology has high similarity. This requires stricter filtering to avoid false recalls.
`CRON_SCHEDULE`	`0 0 * * *`	Automatically checks and syncs the latest clinical data daily at midnight. This maintains knowledge base freshness.

Three Common Mistakes

curl: (7) Failed to connect to host port or Connection refused in logs: The AI_PROXY_URL configuration points to an incorrect AI service address or port, or the AI service container is not running.
After uploading large PDF files, the knowledge base has significantly fewer chunks than expected, or critical information is missing: The PARSE_FILE_TIMEOUT_SECONDS parameter may not have been adjusted, causing file parsing to time out before completion.
When calling environment variables in a workflow, the values set during deployment are not retrieved: Environment variables in the docker-compose file are not correctly mounted to the FastGPT container, or the variable names called in the workflow do not match the actual settings.

How to Confirm Correct Configuration

Upload a typical CTD Module 3 (Quality Module) PDF file. Check if the generated chunks in the knowledge base are complete and logically coherent, and if chunk length matches the expected configuration.
Use the docker logs <fastgpt-aiproxy-container-id> command. Confirm the AI Proxy container starts without errors and successfully connects to the large model service specified by AI_PROXY_URL.
Create a simple Agent. Ask it questions about recent clinical trial data. Observe if the model's response includes the latest information. This verifies the data synchronization effect of the CRON_SCHEDULE configuration.

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.