Dermatology Product Deployment and Upgrade

Data Characteristics for This Category

Dermatology product and reagent consultation data primarily comes from drug inserts, clinical trial reports, academic journal articles, industry standards, and real-world patient data. This data updates frequently, especially with new drug approvals, expanded indications, or updated adverse event reports. Document structures are diverse, including structured table data (e.g., ingredients, dosages, indications, contraindications, adverse event rates), semi-structured text descriptions (e.g., mechanism of action, usage and dosage, precautions), and unstructured images (e.g., skin lesion photos, dosage form diagrams). Fields and units are highly specialized. For example, dosage units are often mg, g, ml; concentration units are g/L, %; treatment course units are days, weeks, months; and adverse event rates are often expressed as percentages or cases/total cases.

Deployment and Upgrade Constraints Imposed by These Characteristics

High update frequency in dermatology data requires FastGPT deployments to have efficient data synchronization and knowledge base reconstruction mechanisms. Diverse document structures necessitate flexible parsers to accurately extract structured information and understand unstructured text content. For example, extracting specific adverse event rates from the side effects section of a drug insert requires customized parsing strategies. Specialized fields and units demand higher precision in knowledge base vector recall, preventing misjudgments due to semantic confusion. Additionally, handling image-based information may require external modules or specific image recognition capabilities. During upgrades, new FastGPT version compatibility, especially changes in parsers, vector models, and retrieval algorithms, requires thorough testing. This ensures existing knowledge base performance remains unaffected while effectively handling new data types and update frequencies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures each segment contains sufficient contextual information for understanding complex descriptions like pharmacological actions and indications.
`Recall count` (Recall Count)	`Top 8–12 entries`	Increases relevance coverage, considering dermatology product consultations may involve multiple aspects.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Guarantees a high match between recalled results and user queries in professional terminology and concepts, reducing irrelevant recalls.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Addresses the parsing needs of large PDF documents like drug inserts and clinical trial reports, preventing parsing timeouts.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Allows uploading medical documents containing numerous charts and detailed descriptions.
`maxContext`	`4096`	Ensures the Q&A model can process longer contexts, understanding complex medical questions and related background information.

Three Common Mistakes

After a knowledge base update, specific drug adverse event information is not retrieved correctly, and logs show context is empty. This happens when the original parsing configuration fails to cover new document formats or field name changes after a data source update.
When users inquire about specific dosage form usage, the system's answer differs from the insert description, or even shows incorrect numerical units. This occurs when data cleaning or vectorization fails to correctly identify and process specialized units, leading to information distortion.
A locally deployed FastGPT experiences 500 errors when uploading and parsing some PDF files after a new version upgrade. This happens because the new version updates the PDF parsing library, potentially causing compatibility issues with older dependencies or specific PDF file formats.

How to Confirm Correct Configuration

Select multiple typical dermatology products. Test whether key information such as ingredients, indications, contraindications, and usage/dosage can be accurately retrieved. Verify the numerical units of the returned information.
For recently updated drug inserts or clinical trial reports, test if the knowledge base update mechanism takes effect promptly. Verify if new information can be effectively recalled.
Simulate common user inquiry scenarios, such as "What is the pediatric dosage for a certain cream?" or "How to handle allergic reactions to a certain ingredient?". Check the accuracy, completeness, and professionalism of the answers. Compare them with official documentation.
Check system logs to confirm no error messages like timeout, file parsing failed, or indexing anomaly appear during data upload, parsing, and knowledge base construction.

Note: The values provided are common starting points. Measure them against your 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.