HTTP Interface and External Systems for Respiratory Quality

Data Characteristics for this Category

Quality documents for respiratory diseases originate from clinical trial reports, Good Manufacturing Practice (GMP) documents, medical device registration data, drug inserts, and patient education materials. Update frequencies vary. Clinical trial reports typically update after phased research results are published. Drug inserts may revise based on post-market surveillance data, with frequencies ranging from quarterly to annually. Document structures often include substantial structured and semi-structured data, such as trial protocols, statistical analysis reports, adverse event lists, production batch information, and quality control standards. Specific fields include lung function indicators (e.g., FEV1, FVC), drug dosage units (e.g., mg/kg, mcg/puff), clinical symptom scores (e.g., mMRC dyspnea score), and specific diagnostic codes (e.g., ICD-10 J44.9 for Chronic Obstructive Pulmonary Disease).

Constraints Imposed by these Characteristics on HTTP Interfaces and External Systems

The data characteristics of respiratory quality documents impose specific constraints on HTTP interfaces and external system integration. First, extensive semi-structured data requires interfaces to flexibly handle embedded tables and charts within XML, JSON, or PDF formats. Traditional text parsing may not capture complete semantics. Second, uncertain update frequencies mean the system must support incremental updates and version management, for example, using ETag or Last-Modified headers for conditional requests to avoid redundant transfers. Third, precise recognition of specific measurement units and medical terminology requires standardized processing during data ingestion, potentially necessitating integration with medical terminology ontologies. Finally, since documents often involve sensitive patient data or proprietary intellectual property, HTTP interfaces must enforce strict authentication (e.g., OAuth 2.0) and authorization mechanisms to ensure data security and compliance.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for this Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Respiratory clinical trial reports and GMP files often contain numerous charts and extensive data, resulting in large individual file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF structures and extracting table data is time-consuming, requiring ample parsing time.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures the integrity of medical concepts, preventing truncation of critical information, while balancing vector retrieval efficiency.
`Recall count` (Recall Count)	`Top 10–15 entries`	Ensures coverage of multiple relevant paragraphs, aiding in the understanding of complex pathological mechanisms or treatment plans.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Accurately matches medical terminology and disease characteristics, reducing interference from irrelevant or generalized information.
`Rerank result count` (Reranked Return Count)	`Top 5 entries`	Prioritizes the most relevant core information, improving the efficiency for engineers to obtain effective information.

Three Common Mistakes

Symptom: Frequent interface calls lead to slow server responses or even crashes. Reason: Lack of rate limiting or concurrency control for external system integration, causing instantaneous request volume to exceed server processing capacity.
Symptom: API-returned application initialization information (e.g., opening remarks, form structure) is inconsistent or missing. Reason: Different applications calling the same API model do not explicitly specify the application context via request headers or query parameters, leading the model to return default or mismatched configurations.
Symptom: Uploaded document content fails to parse correctly, resulting in inaccurate retrieval results or empty fields. Reason: PDF or Word documents contain numerous scanned images, complex tables, or non-standard fonts, and the parser fails to effectively extract text information.

How to Verify Configuration

Upload a respiratory clinical trial report containing complex tables and medical terminology via the API interface. Check that the returned status code is 200 OK and verify that the parsed text content is complete and free of garbled characters.
Using FastGPT's knowledge base management interface, randomly select an uploaded respiratory document. Check if its segment content falls within the preset Chunk size (Segment Length) range and if medical concepts are not noticeably truncated.
Simulate a high-concurrency scenario by having multiple clients simultaneously call the knowledge base retrieval interface. Observe server response times and resource utilization to confirm system stability under heavy load.
For a document containing a specific disease code (e.g., ICD-10 J44.9), perform a retrieval via the API interface. Review the returned Similarity score and Recall count (Recall Count). Evaluate if the precision meets expectations and adjust the Similarity threshold (Similarity Threshold) based on actual retrieval performance.

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.