What this type of data looks like
In financial scenarios, biologics intelligent due diligence data primarily comes from publicly available batch issuance data from the National Medical Products Administration, enterprise clinical trial registration information, and publicly available industry research progress reports. Update cadence: batch issuance data is updated monthly, clinical trial data is released irregularly alongside project progress, and research progress reports have no fixed cycle. Each due diligence document includes fields such as generic name, brand name, manufacturing enterprise, batch issuance batch number, expiration date, indications, clinical trial phase, and adverse reactions. Some nested subfields include administration plans and storage conditions, with units including professional medical units such as ten thousand vials, months, and mg/kg/day.
What constraints do these characteristics impose on HTTP interfaces and external systems?
Biologics due diligence in financial scenarios must meet data traceability and compliance requirements. Multi-source data sources with no fixed update cycle require interfaces to support incremental pulling via timestamps and retain call logs. This avoids redundant overhead and compliance risks caused by full data pulling. Document structures with nested fields and professional medical units require interfaces to support custom field mapping rules. This ensures no unit conversion errors or field loss during data parsing, and meets the standardization requirements of financial due diligence. Each document has a large volume and contains multi-dimensional information. This requires interfaces to support paged pulling and segmented parsing, while extending timeout thresholds to adapt to long-duration data pulling processes.
How to configure the parameters
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
API_REQUEST_TIMEOUT | 600–900 seconds | Biologics due diligence data includes long documents and multi-source stitching workflows. Default timeouts cannot complete full data pulling |
SYNC_INCREMENTAL_ENABLE | Enabled | Biologics batch issuance and clinical trial data have no fixed update cycle. Incremental synchronization reduces resource consumption from repeated pulling and meets financial due diligence traceability requirements |
FIELD_MAPPING_RULES | Map according to official data source fields | Biologics data includes professional medical fields and units. Strict matching avoids parsing errors and adapts to the standardized format required for financial due diligence |
RECALL_TOP_K | 10–15 | Biologics due diligence requires coverage of multi-dimensional information. Too many recall entries increase interface load, while too few fail to meet analysis needs |
OPENAPI_BASE_URL | Official address obtained from the deployment instance | Interface addresses vary across deployment environments. Must match the actual deployment path |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Biologics documents have large volumes. Parsing nested fields and long text requires longer timeout periods |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Returned interface results do not include source file information associated with the knowledge base. Cause: The
INCLUDE_SOURCE_FILESconfiguration item is not enabled. The default interface logic does not return source file associated data. - A
504 Gateway Timeouterror occurs when connecting custom RAG logic. Cause: TheAPI_REQUEST_TIMEOUTparameter was not adjusted. The vector recall or data stitching of the custom process exceeds the default timeout threshold. - Interface calls fail when configuring
OPENAPI_BASE_URLwith a public test address. Cause: The actual deployed FastGPT instance address was not matched. Public addresses are only intended for local debugging scenarios.
How to verify correct configuration
- Call the test interface with a preset biologics data source ID, and check if the returned results include the configured custom fields and their corresponding units.
- Initiate an incremental synchronization request, and verify that only updated batch data is correctly pulled, with no redundant historical data.
- View interface call logs to confirm that the timeout time matches the preset
API_REQUEST_TIMEOUTandPARSE_FILE_TIMEOUT_SECONDSparameter values. - Call the health check interface corresponding to
OPENAPI_BASE_URLto confirm that network connectivity and interface permissions are normal.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.