Infectious Disease Regulations: HTTP Interface and External

Data Characteristics

Data for infectious disease regulations primarily originates from national health commissions, CDCs (Centers for Disease Control and Prevention), internal medical institution policies, and academic journals. This data updates frequently, for example, epidemic notifications and revised diagnostic and treatment guidelines. Document structures typically include policy texts, technical operating specifications, clinical pathways, and emergency plans. Formats are often PDF or DOCX. Fields and units are specialized, such as pathogen names, transmission routes, incubation periods (days), incidence rates (%), antibiotic usage indications, and isolation protection levels. These documents often contain extensive medical terminology and abbreviations.

Constraints on HTTP Interface and External Systems

The high update frequency of infectious disease regulations demands efficient data synchronization mechanisms from external systems to ensure the timeliness of Q&A results. Diverse document formats mean the interface must support uploading and parsing various file types, for example, via file_upload or base64_encode. Specialized fields and abbreviations require medical terminology recognition and standardization during data preprocessing to avoid inaccurate recall due to semantic misunderstandings. Furthermore, because regulatory content involves clinical decisions, data accuracy and completeness are critical. The HTTP interface must include traceability information like source_document_id and page_number in returned data, enabling engineers to verify results.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`chunk_size`	`500-800 characters`	Infectious disease regulation documents often have long paragraphs with multiple related provisions. This range ensures contextual completeness.
`overlap_size`	`50 characters`	Ensures continuous context at segment boundaries, reducing the risk of critical information being cut off.
`max_tokens`	`2048`	Addresses complex, multifaceted questions that may arise in regulatory Q&A, ensuring the model has sufficient space for detailed responses.
`similarity_threshold`	`0.75-0.85`	The medical field demands high recall accuracy. A high threshold effectively filters out irrelevant regulatory provisions.
`embedding_model`	`text-embedding-ada-002`	Balances semantic understanding capabilities with cost-effectiveness, suitable for specialized domain texts.
`api_key_ttl`	`Determined by actual measurement`	Ensures the security of API calls. This can be dynamically adjusted based on actual usage frequency and risk assessment.

Common Pitfalls

API calls return too few data entries, failing to cover all details of a question. This occurs when parameters like max_tokens or max_retrieval_chunks are set too low, limiting the model's output length or the number of retrieved document chunks.
Regulatory documents uploaded via the HTTP interface fail to parse or have missing content. This happens when document formats are complex, such as PDFs with embedded image text or scanned documents, preventing the document_parser from correctly extracting text content.
External systems calling the FastGPT interface receive {"code": 401, "message": "Invalid API Key"}. This indicates that the API key following Bearer in the Authorization request header has expired or is misconfigured, failing interface authentication.

Verification Steps

Upload a typical infectious disease regulation document via FastGPT's Web UI. Observe if its Status is "Completed" and check if the number of Text Segments is reasonable.
Use FastGPT's /api/v1/chat/completions interface, combining dataId and a question, to initiate a query. Verify that the returned answer is accurate and includes a source field pointing to the correct regulatory document.
Simulate multiple concurrent calls to the FastGPT interface from an external system. Monitor interface response times and HTTP Status Codes to ensure interface stability and availability meet expectations under high load.

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.