HTTP Interface and External Systems for Mental Health

Data Characteristics

Mental health regulations and SOP data originate from policy documents, diagnostic guidelines, clinical pathways, and operating procedures. These documents are released by national health commissions, local medical insurance bureaus, and hospital administration departments. Data primarily exists in PDF and DOCX formats, with some web content. Data updates are infrequent, typically occurring annually or when policies change. Document structures are complex, containing specialized terminology, charts, tables, and cross-references. Fields include disease diagnostic criteria (e.g., ICD-10 codes), medication lists (e.g., national essential drug catalog), treatment plans (e.g., indications for electroconvulsive therapy), and patient management processes (e.g., follow-up cycles). Units often involve medical measurements, time units, percentages, or boolean values.

Constraints on HTTP Interfaces and External Systems

The complexity and specialized nature of mental health regulation data demand high robustness from HTTP interfaces and external systems during data extraction and processing. Parsing PDF and DOCX documents requires dedicated libraries or services to accurately extract text, identify chapter structures, and recognize table information. Infrequent updates mean data synchronization does not need to be frequent, but each synchronization must ensure data integrity and version consistency. Specialized terminology and cross-references within documents pose challenges for semantic understanding and knowledge graph construction. This may require calling external medical ontology services for assistance. Additionally, fields containing medical measurement units and special codes require the data returned by the interface to be correctly parsed and utilized by downstream systems, for example, the accurate transmission of ICD-10 codes.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`max_chunk_size`	`800-1200 characters`	Mental health regulation documents often have long paragraphs with strong contextual descriptions. This range helps maintain semantic integrity.
`chunk_overlap`	`100 characters`	Ensures sufficient overlap between adjacent text chunks, preventing loss of critical information due to splitting, especially in policy interpretations spanning paragraphs.
`parse_timeout_seconds`	`600 seconds`	Parsing PDF/DOCX policy documents and guidelines can be time-consuming, especially for documents with many charts and complex layouts. A longer timeout is necessary.
`api_request_timeout`	`120 seconds`	Network latency or large data volumes can slow down responses when calling external medical ontology or drug database interfaces. Allow sufficient time.
`max_retries`	`3 times`	External interface calls may fail due to transient network fluctuations or temporary service unavailability. An appropriate retry mechanism improves success rates.
`rate_limit_interval`	`Calibrate based on actual tests`	Configure according to the target external system's API rate limiting policy, such as the frequency of calls to medical ontology interfaces or drug databases.

Common Pitfalls

Key medical fields like ICD-10 codes or drug CAS numbers are empty in the JSON returned by the HTTP interface. This occurs when interface parameter mapping is not configured correctly, preventing specific fields from being captured or converted from the upstream data source.
External system calls time out, resulting in prolonged unresponsiveness or HTTP 504 errors. This can happen if api_request_timeout is set too short, failing to accommodate complex queries or network latency.
Knowledge base Q&A results misinterpret policy clauses. This is often due to max_chunk_size being too small or insufficient chunk_overlap, which breaks important context and logical connections during document chunking.

Verification Steps

Use FastGPT's debugging tools to inspect the raw data structure and content returned by the HTTP interface. Confirm that key fields like disease names, drug names, and treatment plans are complete and correctly formatted.
Use FastGPT's testing features to simulate common questions about mental health regulations. Observe if the external information cited in the answers is accurate and timely, and review the external interface call logs.
Monitor access logs of external systems (e.g., medical ontology services, drug databases). Verify that requests initiated by FastGPT match expected frequencies, parameters, and response times.
Conduct end-to-end testing after document updates. Verify that new policy content is correctly extracted, indexed, and used for Q&A, ensuring the data synchronization mechanism functions properly.

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.