HTTP Interface and External Systems for Home Medical Device

Data Characteristics for This Category

Registration data for home medical devices primarily originates from internal quality management system documents, product design documents, clinical evaluation reports, testing reports, and regulatory compliance declarations. This data typically exists as structured documents (e.g., Word, PDF, Excel) and unstructured data (e.g., images, CAD drawings). Data update frequency is low, concentrating on key milestones such as product development, registration applications, and change applications. Document structure adheres to the National Medical Products Administration (NMPA) registration requirements. Fields cover product name, model specifications, intended use, technical indicators, main components, manufacturing processes, and testing methods. Units strictly follow the International System of Units or industry standards; for example, power in watts (W), dimensions in millimeters (mm), and weight in grams (g). The accuracy and completeness of data content are critical for successful registration.

Constraints Imposed by "HTTP Interface and External Systems"

The low update frequency of home medical device registration data means external systems do not require frequent polling when retrieving data via HTTP interfaces. Interface design should focus on batch document transfer and version management, for example, by providing APIs like getDocumentVersion and downloadDocument. The highly standardized document structure requires interfaces to accurately identify and extract key fields during data parsing, such as using a documentType parameter to specify the document type for targeted backend parsing. The strictness of fields and units demands high validation requirements for interface return data, ensuring correct numerical types and units to prevent registration failures due to data format errors, for example, explicitly including a unit field during data transfer. The presence of unstructured data requires interface support for large file transfers and file preview functions, such as providing an uploadAttachment interface and setting an appropriate timeout parameter to handle the time consumed by uploading large files.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	8000 tokens	Ensures accommodation of a single registration document's complete context, preventing information truncation.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Addresses the time required to parse large PDF or image files, preventing timeout interruptions.
`maxConcurrentRequests`	5	Balances external system concurrent access pressure with FastGPT processing capability, preventing overload.
`similarityThreshold`	0.75	Improves the accuracy of knowledge recall, filtering irrelevant information, as registration data requires high precision.
`retryAttempts`	3	Handles network fluctuations or temporary external system failures, increasing interface call robustness.
`documentChunkSize`	1000 characters	Optimizes text segmentation, ensuring each chunk contains complete semantic meaning for subsequent retrieval.

Common Mistakes

An external system calls an interface, receives a 400 status code, and an error message INVALID_UNIT_FORMAT. This occurs because a critical field's unit in the transmitted data does not match the expectation, for example, "milliliter" was written as "ml".
After uploading a large PDF test report, the system remains unresponsive for an extended period, eventually displaying GATEWAY_TIMEOUT. This happens because the HTTP interface's timeout parameter is set too short, failing to cover the time required for large file transfer and parsing.
After creating an application via API, attempting to call the knowledge base interface fails to retrieve the expected registration document content, returning an empty result. This is due to a similarityThreshold set too high in the knowledge base configuration, leading to overly strict recall that cannot match relevant information.

Verification of Configuration

Test a typical registration document using the FastGPT platform's file upload function. Verify that the file content is fully parsed and key fields are correctly extracted.
Use an external system to simulate calling the uploadDocument interface, uploading a test document containing all common data types (text, images, tables), and check that the interface returns a 200 status code.
Use FastGPT's retrieval testing feature. Input key terms or regulatory clauses from the registration documents. Verify that relevant document segments are accurately recalled and adjust the similarityThreshold to an appropriate value.

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.