What the Data for This Category Looks Like
Biologics smart due diligence report data originates from National Medical Products Administration public approval documents, corporate clinical trial announcements, batch release data, and pharmacopoeia quality standard files. Update rhythm fluctuates with project progress and batch release cycles. There is no fixed uniform cycle, but there are high-frequency update nodes. Document structures include structured tables (such as batch information, active ingredient content tables), long-text clinical trial reports, and standardized field descriptions. Fields include drug generic names, active ingredient content, units IU or mg, clinical trial enrollment data, approval status, batch numbers, and more. Some fields use mixed units.
What Constraints These Characteristics Impose on the Vector Models and Indexing Link
Biologics data includes large volumes of structured tables and mixed unit fields. This requires vector models to have structured data encoding capabilities. Without this, core fields such as active ingredients and batches cannot be matched accurately. Document length spans widely, from hundreds of characters of quality standards to tens of thousands of characters of clinical trial reports. This requires indexes to support variable-length segmentation, to avoid long text encoding distortion or short text information loss. High-frequency updated batch release data requires indexes to support incremental update mechanisms, to avoid performance loss caused by full reconstruction. Multi-field mixed retrieval requirements require indexes to support field weighting configuration, to prioritize matching core retrieval fields such as batch numbers and active ingredients.
How to Set the Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the paragraph length of clinical trial sections in biologics due diligence reports, avoids including too many structured tables in a single segment to prevent encoding distortion |
chunk_overlap | 100–150 characters | Retains association of active ingredient and batch information across segments, avoids structured fields being truncated |
vector_model | Domestic/open-source vector models that support structured encoding | Adapts to the multi-field mixed biologics data scenario |
recall_top_k | Top 8–12 results | Balances recall accuracy and query latency, adapts to multi-field retrieval requirements |
similarity_threshold | 0.72–0.78 | Differentiates similarity of biologics across batches, avoids confusion of batch information |
index_update_strategy | Incremental update + daily full verification | Adapts to the high-frequency update rhythm of batch release data, prevents index lag |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Receiving a 401 status code when calling the vector model. API keys or permission verification parameters for the target model are not configured, resulting in authentication failure.
- FastGPT being rejected when calling the ollama vector model, but curl tests pass. The FastGPT vector model proxy address or port is not configured correctly, causing the platform to fail to connect to the local service.
- Duplicate batch data appearing after index merging. Index deduplication configuration is not enabled, or the batch number is not specified as the deduplication field, resulting in duplicate data not being filtered.
How to Confirm the Configuration Is Correct
- View the vector model configuration page, confirm that the API address and valid key of the target model have been added.
- Upload a single biologics due diligence report, check that the length of the segmented results matches the
chunk_sizeandchunk_overlapsettings. - Execute a batch retrieval task, verify that the number of recalled results matches the
recall_top_ksetting. - Trigger a manual index update task, check that the system log shows that the incremental update has been executed normally.
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.