What the data for this category looks like
For medical device marketing content in financial scenarios, data sources include product registration certificate attachments from partner manufacturers, clinical controlled study reports, official compliant promotional materials, in-house promotional materials created by institutions, and dealer training slides. Update rhythm falls into two categories: Core product parameter content is updated when manufacturers revise registration certificates or gain approval for new indications, with low update frequency. Promotional material content is updated alongside marketing cycles and compliance policy adjustments, with high update frequency. Most documents use long text paragraphs, and include fields such as product model, specification parameters, applicable departments, contraindicated populations, and clinical effect descriptions. Some content includes units, such as dimensions, dosage, and applicable population age ranges.
Constraints on vector models and indexing workflows
The characteristics of medical device marketing content in financial scenarios create multiple constraints for vector models and indexing workflows. High proportions of long text paragraphs, combined with tightly linked parameters and clinical descriptions, require chunk splitting to avoid breaking semantic integrity. Fields contain precise parameters with units. Vector encoding must retain the linked semantics of numerical values and units to prevent confusion between different specification products during retrieval. Content update frequency is stratified. Core parameter content updates slowly but has strict compliance requirements. Promotional material content updates frequently. The workflow must support both incremental and full index update modes. All indexed content must comply with dual compliance requirements for medical device advertising and financial marketing. Prohibited expression filtering must be completed in the pre-indexing stage.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Balances semantic integrity and index density, and adapts to the long text and associated parameter characteristics of medical device content |
chunk_overlap | 10–15 % | Retains parameter association information across segments, and avoids breaking the context of product specifications and applicable scenarios during splitting |
RECALL_TOP_K | Top 6–8 results | Adapts to the professional requirements of medical device content, filters irrelevant results while covering complete retrieval needs |
EMBEDDING_MODEL | bce-embedding-v1 or similar medical professional embedding model | This type of model provides better semantic encoding effects for medical terminology and parameters with units for this scenario |
INDEX_UPDATE_MODE | Incremental update (promotional materials) + Full update (core parameters) | Matches the differences in update frequency and compliance requirements between the two types of content, and improves index execution efficiency |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-correlation retrieval results, and avoids retrieval deviations caused by parameter confusion |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: After uploading content using the chunk mode pushdata API, the interface remains in the "Indexing" state for an extended period. Cause: A reasonable timeout threshold for the
PARSE_FILE_TIMEOUT_SECONDSparameter was not configured, or uploaded content exceeds the model encoding limit, causing index task blocking. - Phenomenon: The content of the final segment cannot be correctly retrieved during knowledge base searches. Cause: No reasonable
chunk_overlapvalue was set during chunk splitting, leading to loss of context association information for the final segment and preventing encoded vectors from matching retrieval semantics. - Phenomenon: Overall relevance of search return results is low, and retrieval results for different specification products are mixed. Cause: The selected vector model has not been optimized for medical terminology and unit-attached parameters, so encoded vectors cannot accurately match retrieval needs.
How to verify correct configuration
- Upload a single piece of medical device marketing content with unit parameters. Check during retrieval to confirm returned results include complete specification parameters and unit information, verifying that semantic associations are intact.
- Upload core parameter content and promotional materials separately. Check the execution status of index update tasks to confirm the layered update mode is active.
- Adjust the
RECALL_TOP_Kparameter. Compare changes in the number of search results to confirm the retrieval logic is working correctly. - Trigger compliance verification. Check that prohibited promotional expressions are filtered from indexed content, confirming the pre-verification workflow operates as expected.
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.