Data Characteristics
Attenuated inactivated vaccine pharmacovigilance data originates from clinical trial reports, real-world studies, post-market active surveillance systems, and spontaneous reporting systems. Data updates are frequent, especially during initial market release or large-scale vaccination campaigns, with monitoring data potentially updating daily or weekly. Document structures typically include basic patient information, vaccine batch numbers, vaccination dates, adverse reaction dates, specific symptom descriptions (e.g., fever, swelling, allergic reactions), severity, outcomes, relevant laboratory test results, and causality assessments. Field content is highly standardized, often following medical terminology coding (e.g., MedDRA). Units involve dosage (μg), temperature (℃), and time (days, hours).
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The real-time update requirements for attenuated inactivated vaccine data necessitate that external systems pulling data via HTTP interfaces support high-frequency requests and incremental update capabilities, avoiding full synchronization each time. Detailed symptom descriptions and causality assessment fields mean the interface's returned data structure should support nesting or complex objects to transmit clinical details completely. The presence of standardized fields like MedDRA codes requires FastGPT to correctly parse and map these codes during data processing for subsequent knowledge extraction and reasoning. Additionally, data from different sources may have format variations. The HTTP interface needs to provide flexible parameter configurations to accommodate various data sources while demanding higher requirements for request timeouts and error handling mechanisms to ensure stable data transmission.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
FETCH_INTERVAL_SECONDS | 600 seconds | Addresses the high-frequency update requirements for attenuated inactivated vaccine adverse reaction data. |
MAX_RETRIES | 3 | Reduces data synchronization failures caused by transient network fluctuations. |
TIMEOUT_SECONDS | 180 seconds | Accounts for potentially large text descriptions and attachments in vaccine adverse reaction reports, ensuring complete data transmission. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Handles the parsing of large files that may contain detailed clinical reports. |
CHUNK_SIZE | 800–1200 characters | Balances semantic completeness of text chunks with retrieval efficiency. |
MAX_OUTPUT_TOKENS | 2048 | Ensures sufficiently detailed analysis results are returned for complex queries. |
Common Pitfalls
- Symptom: Output from FastGPT API calls stops mid-process. Reason: The
TIMEOUT_SECONDSparameter is set too short, truncating complex queries or large data processing before completion. - Symptom: After uploading a vaccine adverse reaction report file, the agent cannot accurately answer questions about its content. Reason: The
PARSE_FILE_TIMEOUT_SECONDSparameter is insufficient to process multi-page or complex PDF/Word documents, leading to incomplete file parsing. - Symptom: External systems occasionally encounter an
Error: write EPROTwhen calling the FastGPT interface. Reason: Server resources (e.g., memory, number of connections) reach their limits when the HTTP interface handles large file transfers or high concurrent requests, causing write errors.
Verification
- Execute a series of queries for attenuated inactivated vaccine adverse reactions with varying complexity. Check the completeness and accuracy of the returned results, ensuring all key information is included.
- Simulate high-frequency data updates. Observe FastGPT's knowledge base synchronization to confirm that new data is indexed and available for querying promptly, with acceptable synchronization latency.
- Upload vaccine adverse reaction report files in various formats (PDF, Word, TXT) and sizes via the API. Verify that file upload, parsing, and knowledge extraction processes are correct and that professional fields like MedDRA codes are accurately identified.
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.