HTTP Interface and External Systems for Intelligent Triage

Data Characteristics

Data for intelligent triage registration and declaration document preparation comes primarily from medical device regulatory documents, technical review guidelines, clinical trial data, product manuals, user manuals, and public information on approved products. This data updates infrequently. Regulatory documents typically revise annually. Guidelines update periodically based on industry developments and technological advancements. Document structures are mainly unstructured text, such as PDFs and Word files. Some structured XML or JSON data also exists (e.g., device classification codes). Fields include device name, model specifications, intended use, mechanism of action, key technical indicators, and clinical evaluation results. Units cover both International System of Units (e.g., mm, mg, mL) and medical-specific units (e.g., bpm, mmHg).

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

The unstructured nature of intelligent triage data requires external systems to have robust document parsing capabilities. Effective extraction and structured conversion of PDF and Word formats are essential. Since regulatory documents update infrequently, real-time requirements for HTTP interfaces are relaxed. However, data consistency and accuracy requirements are very high. The complexity of fields and diversity of units mean HTTP interfaces must define clear field mapping rules and unit conversion mechanisms during data transmission to avoid ambiguity. For example, when processing clinical trial data, ensure values and statistical significance for indicators like sensitivity and specificity are accurately parsed and transmitted. Additionally, interface design must consider request body size limits and transmission efficiency for registration documents that may contain extensive text descriptions.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for this Recommendation
`chunkSize`	`800–1200 characters`	Balances semantic completeness and retrieval efficiency. Avoids comprehension issues from overly long texts.
`overlapSize`	`100 characters`	Ensures sufficient contextual overlap between chunks. Improves recall accuracy.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates potentially long parsing times for large PDF or Word documents.
`maxContext`	`4000 tokens`	Balances model comprehension capabilities with cost. Covers most critical information in registration and declaration documents.
`similarityThreshold`	`0.75`	Ensures highly relevant retrieval results to user queries. Reduces interference from irrelevant information.
`topK`	`top 5 entries`	Provides sufficient but not redundant reference information. Facilitates quick user screening.

Common Pitfalls

HTTP interface returns status code 514 with error message unAuthApiKey. This typically indicates an incorrectly configured or expired API Key, resulting in insufficient external system call permissions.
The chatId parameter in conversation logs does not take effect, leading to mixed conversation records from different users. This happens if chatId is not correctly passed in the API request, or the backend system does not correctly parse the parameter to associate user sessions.
After the external system calls the API, the returned document chunk content is incomplete or empty. This could be due to a file parsing timeout, or chunkSize being set too large, causing a single chunk's content to exceed system processing limits.

Verification Steps

Use an API call to simulate submitting a typical registration and declaration document. Check if the chatId is correctly recorded in the conversation logs and associated with the specific user.
Upload a PDF file containing various data types (text, tables, image descriptions). Verify if the system accurately parses it and generates valid chunk indexes, and if the chunk content is semantically consistent with the original text.
Use different query statements to test the topK entries of knowledge base recall results. Ensure the returned content is highly relevant to the query intent. Observe changes in recall quality by adjusting similarityThreshold and chunkSize.

Note: The values provided are common starting points. Measure performance against your own samples to determine optimal configurations.

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.