Data Characteristics for This Category
Dermatology product data originates primarily from public databases of drug regulatory agencies, clinical trial reports, academic journals, product inserts, and internal pharmaceutical company product archives. This data updates at a relatively stable frequency. Concentrated updates occur when new drugs launch or inserts revise, but daily information changes are infrequent. Document structures, such as product inserts and clinical reports, are typically in PDF format. These contain extensive unstructured text, including indications, dosage and administration, adverse reactions, and contraindications. Common fields and units include drug components (milligrams mg, grams g), concentration (percentage %), dosage form (cream, gel, solution), batch number, expiration date (year/month/day), and dermatology-specific efficacy indicators (e.g., lesion area cm², pruritus score NRS). Some data may exist in multiple language versions, requiring unified processing.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The characteristics of dermatology product data sources impose specific requirements on HTTP interfaces and external system interactions. First, the prevalence of PDF-formatted product inserts and clinical reports necessitates robust file parsing capabilities, particularly for extracting and structuring unstructured text. Second, the cyclical nature of data updates dictates the interface synchronization strategy. Frequent polling is unnecessary, but new or revised data releases should trigger timely updates to prevent information lag. Multilingual data requires interfaces to support language identification or specified language parameters. The standardization of fields and units, especially for drug dosages and efficacy indicators, requires strict validation during data transmission and reception. This ensures numerical accuracy and consistency, for example, converting mg to g and correctly parsing percentages. Additionally, integration with regulatory agency databases may involve specific authentication mechanisms and API rate limits.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
API_ENDPOINT | Actual URL provided by the data source | Ensures connection to the correct external data service |
AUTH_TOKEN_TYPE | Bearer or Basic | Common authentication methods for most APIs |
REQUEST_TIMEOUT_SECONDS | 600 seconds | Accommodates response times for large PDF parsing or complex queries |
MAX_RETRIES | 3 times | Handles transient network fluctuations or temporary unavailability of external services |
FILE_UPLOAD_MAX_SIZE_MB | 50 MB | Covers the file size of most product insert PDFs |
PARSE_TEXT_CHUNK_SIZE | 800–1200 characters | Balances text parsing efficiency with semantic completeness |
Three Common Mistakes
- Symptom: API calls return
401 Unauthorizedor403 Forbidden. Reason:AUTH_TOKENis misconfigured or expired, failing external system authentication. - Symptom: Chat responses cannot reference the latest product insert information, or reference outdated data. Reason: FastGPT did not synchronize in time via the HTTP interface after external system data updates, leading to an unrefreshed knowledge base.
- Symptom: File processing times out or some content is missing when uploading PDF files for knowledge base construction. Reason:
REQUEST_TIMEOUT_SECONDSis set too short, or the file parsing service has insufficient capability for large files or complex layouts.
How to Verify Correct Configuration
- Use FastGPT's external interface test function. Send a simple query request with a valid
API_ENDPOINTandAUTH_TOKEN. Check for a200 OKstatus code and the expected data structure. - Upload a PDF file containing typical dermatology product information. After the knowledge base processes it, use FastGPT's chat function to ask questions. Verify accurate answers regarding indications, dosage, and administration from the document.
- Simulate an external system data update, for example, changing a product's expiration date. Then trigger FastGPT's data synchronization mechanism. Check if the product's expiration date information in the knowledge base has updated.
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.