What the data for this category looks like
Biologics research report data mainly comes from securities firm pharmaceutical industry research reports, official announcements from the National Medical Products Administration, clinical trial databases, and public annual reports of biologic enterprises. Update cadence varies by content type: regulatory announcements update in real time, securities firm research reports are released on workdays, and clinical trial data is disclosed in phases. Most documents are long texts, with structures including indication descriptions, clinical trial data, registration and approval progress, dosage forms and specifications, and other content. Fields include drug generic names, brand names, acceptance numbers, dosage units (such as mg/vial), clinical trial sample sizes, and more.
What constraints do these characteristics impose on the "knowledge base retrieval and recall" link
The multi-source heterogeneous data feature of biologics research reports requires the retrieval system to support cross-data source field association recall, to avoid siloed information for the same drug from different sources. The long text structure leads to redundant content in single documents, so targeted recall rules must be set for professional fields such as indications and registration progress. Differences in update cadence across content types require incremental sync trigger logic to be configured per data source, to ensure timely updates for regulatory data and sync of research report data according to their release cycles. Additionally, the presence of professional terminology and specific units requires recall matching to retain field-level precision, to avoid information deviation caused by fuzzy matching.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Biologic research reports contain long sections of clinical trial data and professional descriptions. This length preserves complete pharmacological explanations and trial details, avoiding semantic breaks |
similarityThreshold | 0.75–0.85 | Biologic research reports have high professional terminology density. A higher threshold filters non-precise matching results while covering relevant content for narrow indications |
recallTopK | Top 8–12 results | Research report content often has cross-references. Too many recalled results introduce redundant information, while too few fail to cover all relevant studies |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large individual research report documents have substantial content, leading to long parsing times. This duration avoids timeout failures |
enableFieldRetrieval | Enabled | Biologic research reports have dedicated fields such as drug generic names and acceptance numbers. Enabling field-level recall improves matching precision |
incrementalSyncInterval | Configured per data source: 1 hour for regulatory data, 24 hours for securities firm research reports | The update frequencies of the two data types differ significantly. Separate sync cycles ensure timeliness |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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 mistakes
- Phenomenon: Generic content not from the knowledge base is returned after retrieval. Cause: The
strictModeparameter is not enabled, allowing the large language model to call external training knowledge. - Phenomenon: The exported knowledge base file details lack dedicated fields such as drug acceptance numbers and dosage forms and specifications. Cause: The
metadataFieldsparameter is not specified in the export configuration, so dedicated metadata for biologic research reports is not included. - Phenomenon: Recalled results only cover research reports for a small number of popular drugs. Cause: No
recallFilterrule is set to filter by drug category, leading to interference from research reports of unrelated categories in recall results.
How to confirm the configuration is correct
- Upload a biologic research report document, check if the parsed chunked content retains complete clinical trial data and professional fields, to confirm the chunking configuration is effective.
- Initiate a retrieval targeting a specific drug acceptance number, check if the recalled results prioritize documents containing that acceptance number, to confirm the field-level recall configuration is effective.
- Perform a knowledge base file detail export operation, check if the exported results include the preset dedicated metadata fields, to confirm the export configuration is effective.
- Initiate a retrieval request with no matching results, check if the returned content is the preset custom response, to confirm the no-result fallback configuration is effective.
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.