What data looks like for this category
Data sources for medical device investment research include officially released medical device registration certificates, clinical trial reports, industry technical standard documents, manufacturer-published product technical white papers, and medical insurance payment adaptation documents. Update schedules adjust irregularly based on new certificate approvals, standard revisions, and industry news releases.
Document structures include both structured fields and unstructured content. Structured fields include registration certificate numbers, model specifications, technical parameters with units such as mm, Gy, and patient visits, and applicable clinical departments. Unstructured content includes clinical trial details and product compliance explanations. The length of individual documents varies widely.
What constraints these characteristics impose on vector models and indexing
The multi-type characteristics of medical device investment research data create multiple constraints for the vector model and indexing workflow. Structured fields and technical parameters with units require models to support semantic and numerical association matching, to avoid confusing similar parameters with different units. The wide variation in document length requires a flexible chunking strategy to adapt to both short registration certificates and long clinical trial reports. Irregular update cycles require the indexing workflow to support incremental updates, to avoid resource consumption from full reindexing. The need for multi-field combined retrieval requires index structures to support hybrid indexing of structured and unstructured data, to improve recall accuracy.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Medical device documents contain long technical parameter sections and structured fields. Excessively long chunks will lose the logical association of parameters, while excessively short chunks will disrupt the narrative coherence of clinical reports |
chunk_overlap | 100–150 characters | Cross-chunk technical parameters (such as continuous descriptions of model specifications) require contextual association to avoid information fragmentation that impacts semantic matching |
vector_db_type | Vector database supporting multi-field combined indexing | Medical device data includes structured registration certificate numbers and unstructured clinical text. Combined indexing can match both semantic and structured fields simultaneously |
top_k | 5–8 results | Investment research scenarios require precise matching of technical parameters and indications. Too many results will increase the screening cost for analysts |
similarity_threshold | 0.72–0.85 | Semantic similarity of medical device technical parameters requires a high threshold to filter irrelevant results, avoiding recall of non-similar products with low matching scores |
index_batch_size | 20–30 items per batch | The data volume of individual medical device documents varies widely. An overly large batch will cause index timeouts, while an overly small batch will reduce overall construction efficiency |
The 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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: In a Docker deployment environment, the knowledge base indexing task remains in a running state with no progress. Cause: When connecting to an external vector model service, a reasonable
index_batch_sizeis not configured. Excessively large batch data causes service timeout and no response, leading to repeated task retries. - Phenomenon: The vector model call returns
503 Service Unavailable, with a prompt that no availabletext-embeddingmodel exists in the current group. Cause: A dedicated group routing is not configured for the vector model. The default group has exhausted resources or does not have authorization for the corresponding model permissions. - Phenomenon: Recall results mix medical device technical parameters with different units, resulting in insufficient matching accuracy. Cause: A reasonable
similarity_thresholdis not set, or structured field combined indexing is not enabled, causing semantic matching to fail to distinguish parameter units and applicable scenarios.
How to confirm correct configuration
- View the vector database index monitoring panel, confirm that the time taken for single-batch index tasks matches the carrying capacity of the current environment, and adjust
index_batch_sizeto a reasonable range. - Randomly select multiple different types of medical device documents, perform vector recall tests, and verify that the similarity scores of returned results fall within the preset threshold range.
- Check the vector model call logs, confirm there are no
503or timeout errors, and verify the connectivity and stability of the model call link. - Manually split a single long document, confirm that the logic of technical parameter sections and clinical reports is not overly fragmented, and the split content has complete semantic relevance.
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.