Deployment and Upgrade for Dermatology R&D Document

Data Characteristics

Dermatology R&D documents originate from various sources. These include clinical trial reports, pathological analysis reports, drug mechanism of action studies, patient follow-up records, and domestic and international academic journal papers. Document update frequencies vary. Clinical trial data may update in phases, while academic papers publish continuously. Document structures typically include standard sections like abstract, introduction, methods, results, discussion, and conclusion for reports. However, internal subsections and data presentation methods differ significantly. For example, pathology reports detail histological features and immunohistochemistry results. Clinical trial reports focus on efficacy indicators and adverse event statistics. Common fields include patient ID, diagnosis, treatment plan, drug dosage, follow-up period, efficacy evaluation indicators (e.g., PASI score, IGA score), and adverse event codes. Units cover common measurements (mg, ml, μm) and specialized scale scores.

Constraints on Deployment and Upgrade

The diverse sources and varied structures of dermatology R&D documents require the deployed system to handle heterogeneous data robustly. For example, processing electronic medical record data with different formats from various clinical centers needs flexible document parser configurations. Uncertain update frequencies, especially phased updates of clinical trial data, demand incremental indexing and real-time synchronization capabilities. This avoids resource consumption from full index rebuilds. Complex nested document structures and specialized fields, such as PASI scores or IGA scores, require precise identification and extraction during structural analysis. This directly impacts the accuracy of subsequent knowledge retrieval. Furthermore, due to sensitive patient information and undisclosed R&D data, the deployment environment must meet strict data security and compliance requirements. This includes ensuring data isolation and access control. System upgrades must also be compatible with existing data structures to prevent data loss or parsing errors.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Dermatology clinical trial reports or pathology images are often large. This ensures complete files can be uploaded.
`maxContext`	`1500 characters`	Paragraphs in dermatology R&D documents are information-dense. A longer context window maintains semantic integrity.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDFs or scanned documents can be time-consuming. This provides sufficient time to prevent timeouts.
`Chunk size`	`800 characters`	This balances the completeness of dermatology terminology with retrieval efficiency, preventing semantic loss from over-segmentation.
`Recall count`	`Top 5 entries`	Prioritize retrieving the most relevant key information, reducing irrelevant noise.
`Similarity threshold`	`0.75`	Dermatology professional terminology and disease descriptions require high precision. Increasing the threshold ensures highly relevant retrieval results.

Common Pitfalls

Knowledge base synchronization delays: New files are uploaded but not found in queries. This usually results from incorrect database connection configurations in Docker environments or network isolation issues, preventing FastGPT from accessing updated databases.
Document parsing failures or missing content: Logs show File parse error or critical fields are not extracted. This might be due to complex document formats (e.g., scanned PDFs, multi-nested tables) or parser configurations that do not cover all specialized field identification rules, such as incorrect recognition of Treatment cycles units.
Inaccurate query results: Answers returned have low relevance to dermatology-specific questions. This could be because Chunk size is too short, truncating specialized terms, or Similarity threshold is set too low, introducing excessive generalized information.

Verification Steps

Upload a dermatology clinical trial report containing complex charts and specialized terminology. Verify that the knowledge base displays the text content completely and that key fields like PASI scores and drug dosage are correctly extracted.
Ask multiple questions using keywords and phrases based on the uploaded document. Observe the accuracy of the retrieved passages and generated answers, ensuring high consistency with the original document content.
Simulate concurrent upload and query scenarios. Monitor system resource utilization to confirm that system response speed and stability meet expectations during peak periods, without significant performance bottlenecks or timeout errors.

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.