HTTP Interface and External Systems for Dermatology

Data Characteristics in this Domain

Core data for dermatology registration documents originates from clinical trial reports, non-clinical study reports, manufacturing process and quality control files, and pharmaceutical research data. This information typically exists as a mix of structured documents (e.g., CRF forms, analysis reports) and unstructured documents (e.g., investigator brochures, expert opinion letters, literature reviews). Update frequency centers around key milestones in the R&D phase, such as clinical trial data lock and pharmaceutical change submissions. Document structures are complex, often containing numerous charts, biomarker data, and specialized terminology. Fields and units are highly specific, for example, lesion area (cm²), inflammation score (e.g., IGA score 0-5), drug concentration (µg/mL), biological activity units (IU/mg), and gene expression levels (e.g., fold change).

Constraints Imposed by these Characteristics on HTTP Interfaces and External Systems

The mixed document structure of dermatology submission materials requires HTTP interfaces to handle various file types, especially PDF, DOCX, and XLSX, and support their content parsing. The high frequency of biomarkers and specialized terminology necessitates robust terminology recognition and entity extraction capabilities in external systems to ensure data accuracy. The update rhythm of clinical trial data dictates that interfaces must support batch data synchronization and incremental updates to avoid duplicate imports. Given the involvement of complex medical units and numerical values, interfaces must ensure unit consistency and numerical precision during data transmission, for example, handling decimal type fields. Additionally, a large volume of unstructured text content, such as expert opinions, places higher demands on text vectorization and semantic understanding modules to support subsequent intelligent Q&A and content generation.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`32000 tokens`	Covers common lengthy discussions in clinical trial reports, maintaining contextual coherence.
`PARSE_FILE_TIMEOUT_SECONDS`	`300` seconds	Handles PDF files containing numerous charts and complex tables, preventing parsing timeouts.
`Chunk size` (Segment Length)	`800–1200` characters	Balances the completeness of dermatology-specific terminology and the processing efficiency of vector models.
`Recall count` (Recall Count)	`8`	Ensures sufficient relevant evidence snippets are recalled from vast submission documents.
`Similarity threshold` (Similarity Threshold)	Calibrated by actual measurement, `0.75-0.85` range suggested	Balances recall precision and breadth, avoiding the omission of critical information or the introduction of excessive noise.
`Rerank result count` (Reranked Return Count)	`3`	Selects the most relevant paragraphs for dermatology-specific questions, improving the quality of the final answer.

Three Common Mistakes

Symptom: API calls return 400 Bad Request errors, indicating missing required fields or field type mismatches. Cause: The HTTP request body does not strictly adhere to the dermatology-specific data model definition. For example, an IGA_score field expecting an integer receives a string, or the lesion_area field lacks unit information.
Symptom: After an external system retrieves data via the HTTP interface, some text content (e.g., symptom descriptions, diagnostic criteria) appears garbled or truncated. Cause: The interface does not correctly set Content-Type: application/json; charset=utf-8, or file parsing fails to handle compatibility with multiple encoding formats.
Symptom: The FastGPT application cannot reference the latest clinical trial data when answering dermatology-related questions. Cause: The external system does not correctly trigger the incremental update mechanism when calling the data synchronization interface, leading to outdated data versions in the knowledge base.

How to Confirm Correct Configuration

Use Postman or curl commands to call FastGPT's knowledge base import interface, upload a PDF document containing dermatology clinical data and pharmaceutical information, and verify that the knowledge base accurately parses and stores the document content.
Within the FastGPT application, pose complex questions related to specific dermatological conditions (e.g., atopic dermatitis, psoriasis) and drugs (e.g., biologics, topical corticosteroids). Observe whether the answers accurately cite professional data provided by the external system.
Simulate scenarios where external systems regularly update clinical trial data. Call FastGPT's incremental update interface, then verify that the corresponding data versions in the knowledge base are updated.
Check the HTTP request node call logs in the FastGPT workflow for external system interfaces. Confirm that the status code is 200 and the returned data structure matches expectations.

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.