Deployment and Upgrade for Dermatology Registration and

Data Characteristics for This Category

Dermatology registration and declaration documents include clinical trial reports, non-clinical study reports, manufacturing processes, quality standards, instructions, and labels. These documents are typically in PDF, Word, or scanned image formats. They contain extensive medical terminology, charts, experimental data, and statistical analysis results. Data sources are diverse, including internal pharmaceutical company R&D data, CRO clinical trial data, and regulatory guidelines and review reports. Update frequency is relatively low, primarily during new drug development, post-market changes, or annual reports. Document structure is highly standardized, adhering to international norms like ICH GCP and GLP. Fields such as "primary efficacy endpoints," "adverse event incidence," and "pharmacokinetic parameters" have clear definitions. Units like mg/kg, IU/mL, and % are industry standard.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The complex structure and extensive specialized terminology of dermatology declaration documents require FastGPT to have robust file parsing and semantic understanding capabilities during data ingestion. This ensures accurate extraction of chart and table content. The low update frequency means initial deployment involves processing a large volume of historical data at once, demanding system stability and concurrent processing capacity. Standardized document structure facilitates efficient knowledge indexing but requires fine-tuned segmentation strategies to avoid fragmenting critical information. The specialized nature of fields and units requires FastGPT's retrieval and generation modules to accurately identify and maintain consistency, preventing result deviations due to unit confusion. Additionally, the sensitive nature of declaration documents necessitates a deployment environment with high security and data isolation capabilities.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Dermatology declaration files are generally large, containing multiple pages of charts and detailed text. This value accommodates most single documents.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF or Word documents can be time-consuming. Increasing the timeout prevents parsing interruptions and ensures complete ingestion.
`Chunk size`	`800–1200 characters`	Dermatology documents have high information density per paragraph. Longer segment lengths help maintain contextual completeness and prevent critical medical concepts from being truncated.
`Recall count`	`Top 10 entries`	Ensures that enough relevant segments are retrieved, especially when a query might involve multiple clinical trial details.
`Similarity threshold`	Calibrated by actual measurement	Requires adjustment based on actual retrieval performance and data characteristics. The goal is to balance recall and accuracy, avoiding interference from irrelevant segments.
`maxContext`	`3000 Tokens`	Ensures the AI model can handle longer contexts to understand complex clinical data descriptions, trial designs, and results analysis, guaranteeing accuracy and comprehensiveness of responses.

Three Common Pitfalls

The system displays mongo connection error on startup, with containers repeatedly restarting. This typically indicates incorrect MongoDB database configuration or network connectivity issues, preventing FastGPT from connecting to its data store.
After a new version upgrade, retrieval results for some historical documents are inaccurate, or field information is missing. This might be because the new version's file parser has changed its logic for handling old document structures, leading to incomplete index rebuilding or parsing errors.
Uploading large PDF files via the frontend interface results in a long period of unresponsiveness and eventually a "request timeout" error. This suggests that UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS parameters are set too low, failing to accommodate the actual file size and parsing time of dermatology documents.

How to Confirm Correct Configuration

Upload a dermatology clinical trial report PDF containing complex tables and charts. Verify that the file is successfully parsed and that the complete text content is visible in the knowledge base.
Perform a retrieval query in the knowledge base using a specific disease name, drug dosage, and adverse reaction terminology. Verify that the returned results include key segments from the relevant documents.
Execute a complete incremental update operation for the knowledge base. Monitor system logs to confirm no file parsing failures or data synchronization errors occur.
Use FastGPT's Q&A function to ask questions about dermatology drug mechanisms, indications, or side effects. Verify the accuracy of specialized terminology and consistency of units in the AI's responses.

Note: The values provided are common starting points. They 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.