What the data for this category looks like
The data sources for biologics smart due diligence primarily include documents approved by the National Medical Products Administration, public Phase III clinical trial reports, enterprise research and development pipeline disclosure documents, and original trial records issued by CRO institutions. Update cycles vary by source. Approved files are updated via fixed archiving. R&D pipelines are updated quarterly. Original trial records have a long retention period. Most documents contain fields such as trial number, number of enrolled subjects, adverse reaction rate, drug activity unit, and approval number. Units involve biologics-specific measurement standards such as IU, mg/mL, and human dose.
What constraints do these characteristics impose on the "citation sources and traceability" link
The multi-source, heterogeneous data characteristics of biologics create multiple constraints for citation traceability. Approved files are fixed documents with official seals. File hashes or official archive numbers must be verified to ensure traceability authenticity. Quarterly updated R&D pipeline data must be bound with update timestamps to avoid referencing expired versions. Original trial records include specialized units such as IU and mg/mL. Original field mappings must be retained, and these units cannot be converted to generic numerical values alone. Fragmented recall from long documents requires precise association with original page numbers or paragraph identifiers. Without this association, original data for specific trial steps cannot be mapped back to.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_SEGMENT_LENGTH | 800–1200 characters | Biologics trial reports often contain long paragraphs of trial method descriptions. This segment length can fully retain the contextual association of a single trial group, avoiding splitting that disrupts trial logic |
RECALL_TOP_K | Top 6–8 results | Biologics due diligence requires coverage of multiple content types including approval information, trial data, and pipeline information. 6-8 results can cover core citation sources while avoiding redundancy |
VECTOR_SIMILARITY_THRESHOLD | 0.72–0.80 | Most biologics fields use specialized terminology. A threshold that is too low will introduce irrelevant trial data. A threshold that is too high will fail to recall precise trial fragments |
FILE_HASH_VERIFY_ENABLE | Enabled | Biologics approval documents require official traceability. When enabled, this setting generates file hashes bound to citation records to prevent document tampering |
MYSQL_QUERY_CONTEXT_ENABLE | Enabled | When calling original trial data from a MySQL database, this setting retains fragment references to the original SQL query results, aligning with the traceability logic of the RAG knowledge base |
RECALL_REORDER_COUNT | Top 3 results | Biologics specialized content requires reordering to prioritize the most relevant trial data, preventing the large language model from referencing non-core fragments |
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 individual analysis. Testing on one's own samples is recommended before finalizing settings.
Three common mistakes
- Issue: After calling trial data from a MySQL database, the large language model output does not include original database fragments, only summary conclusions. Cause: The
MYSQL_QUERY_CONTEXT_ENABLEconfiguration is not enabled, and contextual fragments of the original query results are not retained. - Issue: After switching the vector model, recall results differ significantly from previous results and fail to match biologics specialized terminology. Cause: The field mapping configuration for the vector model is not updated synchronously, resulting in mismatched vector encoding of specialized terminology.
- Issue: The units of cited trial data do not match the original text, leading to numerical deviations. Cause: Original document field mappings are not retained, and specialized units such as IU, mg/mL are converted to generic numerical units, losing traceability basis.
How to confirm configurations are set correctly
- Upload a single biologics approval document, check whether citation records are bound with file identification information, and confirm that the hash verification configuration is enabled.
- Initiate a query containing biologics specialized terminology, and verify that the number of recall results matches the preset recall count rule.
- Call original trial data from a MySQL database, and confirm that the large language model output includes fragment content returned by the original query.
- After switching the vector model, initiate a query on the same topic, and verify that the specialized terminology matching logic of recall results meets expectations.
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.