HTTP Interface and External Systems for Rare Disease

Data Characteristics

Rare disease product data comes from diverse sources. These include global rare disease databases (e.g., Orphanet, OMIM), clinical trial registries, public product monographs from pharmaceutical companies, academic papers, and patient registries. Data update frequencies vary. New drug development, clinical trial results, and guideline revisions trigger updates, typically aggregated quarterly or semi-annually. However, urgent safety information can be updated at any time. Document structures are primarily semi-structured and unstructured. For example, product monographs are often PDF or Word documents containing extensive free-text descriptions. Structured data resides in databases. Fields cover disease names, drug names, indications, dosage and administration, adverse reactions, pharmacokinetic parameters, clinical study data (e.g., NCT numbers), target information, and regulatory approval status. Units typically involve medical professional units like dosage (mg, μg), frequency (times/day, times/week), and concentration (ng/mL).

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The discrete nature of data sources and inconsistent update frequencies require HTTP interface design to accommodate aggregation and synchronization strategies for multiple data sources. For example, product information from various pharmaceutical companies and regulatory bodies might be exposed through different API interfaces, necessitating a unified data model for integration. The presence of semi-structured and unstructured documents demands robust document parsing capabilities from external systems. These systems must extract and structure key information from PDFs or Word documents. This includes using OCR technology to recognize tabular data in images or natural language processing to extract drug dosages and adverse reactions from text. The specialized nature of fields and units requires interfaces to strictly adhere to medical standard terminology and unit specifications during data transmission to avoid ambiguity. An example is distinguishing between dosage units mg/kg and mg. The existence of highly sensitive data (e.g., patient privacy information) imposes strict requirements on interface authentication, authorization mechanisms, and encrypted data transmission. This includes using OAuth 2.0 for API access authorization and ensuring TLS encryption for the data transmission path.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient response time for large document parsing and complex database queries, preventing data retrieval failures due to timeouts.
`MAX_FILE_SIZE_MB`	`100 MB`	Rare disease product monographs or clinical reports can contain extensive charts and detailed descriptions, resulting in large file sizes.
`API_KEY_ROTATION_INTERVAL_DAYS`	`90 days`	Enhances security for sensitive data interfaces. Regular API key rotation reduces the risk of compromise.
`PARSE_DOCUMENT_CONCURRENCY`	`5`	Balances system resource utilization with document processing efficiency. Parallel parsing of multiple documents accelerates knowledge base construction.
`EMBEDDING_BATCH_SIZE`	`32`	Balances the processing capacity of the text embedding model with API call frequency limits, optimizing vectorization efficiency.
`RETRY_ATTEMPTS`	`3 times`	Addresses occasional network fluctuations or temporary unavailability of external systems. A retry mechanism increases data retrieval success rates.

Common Mistakes

Symptom: External API calls return 401 Unauthorized or 403 Forbidden. Reason: Incorrect API key configuration or insufficient permissions, failing to pass the external system's authentication and authorization mechanisms.
Symptom: The drug dosage field extracted from a PDF document is empty or incorrectly formatted. Reason: The document parser failed to correctly identify specific table layouts in the PDF or dosage expression patterns in free text.
Symptom: After importing a workflow template, the corresponding HTTP request node is not correctly activated or parameters are missing. Reason: The workflow template is incompatible with the FastGPT version, or not all node configurations were correctly parsed during import.

Verification Steps

Simulate external API calls. Check if the returned status code is 200 OK and verify if the response body content matches the expected data model.
Upload a rare disease product document containing various formats (tables, text). Check if key fields extracted into the knowledge base (e.g., indications, dosage and administration) are complete and accurate.
Configure a workflow that includes an HTTP request. After execution, review logs to confirm that request parameters were sent correctly and the expected external system response was received.
Examine the embedding quality of rare disease-related text in the vector database. Use similarity search to verify the relevance of recall results, assessing if the embedding model and data processing pipeline are functioning correctly.

The values provided are common starting points and should be measured against specific 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.