What the data for this category looks like
Data for medical device intelligent due diligence reports primarily comes from medical device registration certificate documents, clinical trial data, production compliance documents, and official parameter manuals for devices or consumables. Updates occur in irregular batches, triggered by new model launches, registration certificate renewals, or updates to compliance standards. Document structure includes both structured parameter tables and long-form descriptive text.
What constraints these characteristics impose on vector models and indexing
The mixed structured and unstructured nature of medical device data requires vector models to adapt to both parameter-based short text and compliance-focused long text encoding. Irregular batch updates require indexes to support incremental construction and rapid refresh, avoiding performance losses from full reconstruction. Fields with professional units like mm, kV, or Pa require vector encoding to retain field identifiers, preventing semantic confusion between different parameter types. Chunking strategies must balance parameter integrity and contextual coherence for documents with mixed long text and table layouts.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-v2 | Supports encoding medical domain terminology, with better matching accuracy for medical device parameter text than general embedding models |
chunk_size | 800–1200 characters | Preserves complete context for single sets of technical parameters common in medical device documents |
chunk_overlap | 100–150 characters | Prevents loss of associated information across parameter chunks during retrieval |
index_type | HNSW | Enables fast retrieval of high-dimensional vectors, adapts to the multi-dimensional characteristics of medical device parameters, and balances retrieval speed and recall accuracy |
retrieve_top_k | Top 8–12 results | Covers the multi-dimensional parameter requirements of medical device due diligence reports, while avoiding irrelevant information from overly large result sets |
similarity_threshold | Calibrated via actual testing | Adjust based on the field density and retrieval needs of medical device documents, balancing precise recall and coverage |
Three common misconfigurations
- Phenomenon: Retrieval returns irrelevant medical device parameter content. Cause: No separate vector encoding is configured for structured fields in medical device documents, so general vector models cannot distinguish the priority between parameter fields and descriptive text.
- Phenomenon: Index status shows not ready, and retrieval cannot be triggered. Cause: Either the
index_refresh_intervalparameter is not set, or batch-imported medical device documents exceed theUPLOAD_FILE_MAX_SIZElimit, causing index construction to time out without completion. - Phenomenon: All columns of the entire database table are used directly as the knowledge base index. Cause: Non-due-diligence-related fields in medical device documents are not filtered, introducing noisy data into the vector database and reducing retrieval accuracy.
How to confirm proper configuration
- Upload a single medical device registration certificate PDF, and check if the vector encoding results retain the semantic association of core fields such as registration certificate number and technical parameters.
- Initiate a retrieval targeting the parameters of a specific medical device model, and verify that the fields of the recalled results match the parameter type in the query.
- View index construction logs, and confirm that
index_build_durationdoes not exceed the preset threshold, and there are no error messages for vector encoding failures. - Adjust the
similarity_thresholdparameter, and verify that the number of recalled results changes as expected relative to the threshold.
All parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
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.