Data Characteristics in This Domain
Pharmacovigilance data in health management comes primarily from smart wearables, Electronic Health Records (EHR), patient self-reporting systems, and third-party testing institutions. This data typically exists as a mix of structured (e.g., JSON, XML) and semi-structured formats (e.g., clinical notes, PDF reports). Update frequency is high; some physiological indicators update minute-by-minute, while drug use and adverse event reports are usually event-driven. Document structures are complex, containing patient basic information, medication records, physiological parameters, diagnostic results, and adverse reaction descriptions. Fields are diverse, including drug name, dosage, frequency, adverse reaction type, occurrence time, severity, and often involve medical terminology and abbreviations. Units like milligrams (mg), milliliters (ml), times/day, mmHg, etc., require precise parsing.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
High-frequency physiological data updates require the HTTP interface to handle high concurrency and low-latency responses, ensuring data real-time accuracy. Complex, multi-source data necessitates support for parsing and standardizing various data formats. For example, medication records from different sources need unification into entities recognizable by FastGPT. Semi-structured documents (e.g., patient narratives) require robust text extraction capabilities to identify key drug and adverse reaction information. The specialized nature of medical terminology and units demands effective semantic understanding and unit conversion during data ingestion to prevent data errors due to inconsistent units. Furthermore, to protect patient privacy and data security, interfaces must support strict authentication and encrypted data transmission, such as HTTPS, and anonymize data before storage.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Accommodates large health reports and image files. |
maxContext | 4096 tokens | Balances long text processing with response speed, prevents context overflow. |
PARSE_FILE_TIMEOUT_SECONDS | 180 seconds | Handles time-consuming OCR parsing for complex PDFs or image documents. |
Chunk size | 800–1200 characters | Ensures each segment contains sufficient information without being overly long. |
Similarity threshold | 0.75 | Balances recall accuracy and relevance, reduces interference from irrelevant information. |
HTTP_REQUEST_TIMEOUT | 60000 ms | Prevents premature timeouts when external systems are slow or data volumes are large. |
Three Common Pitfalls
- Document upload fails with "file too large" or "parsing timeout" messages. This indicates
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSare set too low for large or complex documents. - FastGPT fails to extract key information correctly after receiving data from an external system, leading to inaccurate answers. This occurs when data format parsing rules are not optimized for medical terminology and units specific to health management, or field mapping is incorrect.
- Errors occur when executing multi-line query statements with a database plugin. The database connection plugin typically supports only single-line SQL statements by default; multi-line statements require multiple calls or adjustments to the plugin logic.
How to Verify Correct Configuration
- Upload multiple health reports of different sizes and formats (e.g., PDF, JSON) via the HTTP interface. Check that all upload and parse successfully, with no "file too large" or "parsing timeout" errors.
- Simulate requests from an external system pushing various pharmacovigilance data. Verify that FastGPT accurately identifies and extracts key fields such as drug names, adverse reaction types, and dosages. Confirm extraction accuracy through question-answering.
- Configure a database query plugin. Attempt to query data containing medical terminology and units. Verify that query results match expectations and that data fields are correctly mapped.
Note: The values provided are common starting points. Measure them against your 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.