Data Characteristics
Pharmacovigilance data for small molecule drugs primarily originates from regulatory bodies like the National Medical Products Administration (NMPA), FDA, and EMA. Sources include adverse drug reaction (ADR) reporting systems, clinical trial databases, post-market surveillance data, and academic journals. This data updates frequently, especially ADR reports, which may update daily or weekly. Document structures typically include fields for basic drug information, active ingredients, indications, dosage and administration, ADR event descriptions, event timestamps, severity, outcomes, and relevant laboratory test results. Field units vary; for example, dosage units may be milligrams (mg) or micrograms (µg), time units days or hours, and laboratory indicator units mmol/L or U/L. ADR descriptions often exist as unstructured text, requiring natural language processing.
Constraints on Tool Calling and Plugins
The unstructured text nature of small molecule drug ADR reports necessitates robust text processing capabilities from tool calling and plugins. This includes using NLP tools to extract structured information from reports. High-frequency data updates demand efficient data synchronization and incremental processing, requiring tool plugins to regularly pull the latest data and integrate it effectively. Diverse field units mean that data comparison or analysis requires unit consistency or conversion, achievable through pre-processing plugins or by specifying conversion functions during tool calls. Data source compliance and security are core considerations; tool calls must adhere to strict data access permissions and security protocols, especially when handling patient privacy information. For structured data, tool calls require precise field mapping to ensure accurate data transfer and parsing between different systems.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
API_TIMEOUT_SECONDS | 300 seconds | Addresses potential network latency or complex computation time for external API calls, especially in scenarios involving large-scale data processing. |
MAX_RETRIES | 3 | Enhances the stability of external tool calls, reducing failures due to transient network fluctuations or temporary service unavailability. |
CHUNK_SIZE_KB | 1024 KB | Optimizes file upload and processing efficiency, balancing memory usage and transfer speed. |
FILE_PARSER_CONCURRENCY | 5 | Considers that small molecule drug documents (e.g., clinical reports) may contain large amounts of text; multi-concurrent processing accelerates parsing. |
EXTERNAL_TOOL_SCHEMA | Defined according to actual tool API documentation | Ensures FastGPT correctly understands and constructs tool call request bodies and parses responses. |
DATA_SYNC_INTERVAL_HOURS | 24 hours | Balances ADR data update frequency (typically daily or weekly) with system resource consumption. Adjust as needed. |
Common Pitfalls
- Missing or type-mismatched JSON fields returned by external tool calls, leading to subsequent processing failures. This often occurs due to not strictly adhering to the tool API's response contract, or changes in the tool's data structure not being updated in FastGPT's configuration.
- Inaccurate or omitted entity extraction (e.g., drug names, symptoms) when processing drug ADR report text. This stems from insufficient recognition capabilities of the NLP plugin's pre-trained models for specific medical terminology, or a lack of fine-tuning for the linguistic characteristics of small molecule drugs.
- Data synchronization tasks remaining incomplete or failing long after a tool call, with logs indicating connection timeouts or authentication failures. This is often caused by expired external data source API keys, IP whitelist restrictions, or network policy changes, without corresponding updates to FastGPT's credentials or network configuration.
Validation Steps
- Manually trigger a tool call via the FastGPT interface and verify that the returned results conform to the expected data structure and content.
- Configure a test set containing small molecule drug ADR reports, run a batch processing task, and validate the concordance between extracted structured information and human-annotated results.
- Simulate incremental updates from an external data source and verify that FastGPT's data synchronization plugin accurately identifies and imports new ADR events, while confirming historical data remains unaffected.
- Check FastGPT's log system to confirm the absence of critical error messages such as
connection timeout,authentication failed, orschema validation errorduring tool calls.
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.