Knowledge Base Retrieval and Recall for Cardiovascular

Data Characteristics

Knowledge data for cardiovascular intervention products primarily comes from medical device manufacturers. Sources include product manuals, technical handbooks, clinical research reports, post-market regulatory documents, and internal training materials. Data updates are relatively stable, typically occurring with product version iterations, expanded clinical indications, or regulatory changes. Documents are mainly in PDF, Word, and Excel formats. These documents contain numerous charts, product specifications, operating procedures, contraindications, and complications. Key fields include product model, specifications, materials, coating type, diameter, length, pressure tolerance, expected lifespan, and sterilization methods. Units involve various physical quantities such as millimeters, inches, atmospheres, degrees Celsius, and time.

Constraints on Knowledge Base Retrieval and Recall

The highly structured and parameterized nature of cardiovascular intervention product data requires the knowledge base to effectively retain table and chart information during chunking. This prevents the loss of critical parameters or context fragmentation. Multi-level headings and chapter structures in product manuals challenge the logical and semantic integrity of text segmentation. A large number of specialized terms and abbreviations demand strong domain vocabulary understanding from the model. Furthermore, the need for precise matching of product models and specifications means traditional text similarity retrieval may be insufficient. More accurate filtering requires combining structured information. Although the update frequency is not high, each update can involve critical safety or performance parameters, making timely synchronization of knowledge base content crucial.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Balances the integrity of text context and retrieval efficiency. Avoids single chunks that are too long, diluting key information, or too short, losing semantic connections.
`Chunk Overlap Rate`	`0.1`	Ensures a small overlap between adjacent chunks. This helps capture cross-chunk information during retrieval, improving recall accuracy.
`Recall Count`	`10 entries`	Cardiovascular intervention product information may involve multiple complementary parameters or precautions. Increasing the recall count provides a more comprehensive reference.
`Similarity Threshold`	`0.78`	A higher threshold helps filter out irrelevant general information, focusing on product-specific queries and reducing false recalls.
`Rerank Return Count`	`5 entries`	Performs semantic reranking on the initial recall results. This ensures the most relevant core information is presented first, optimizing user experience.
`File Type Whitelist`	`pdf, docx, xlsx`	Restricts uploaded file types to common product document formats. This ensures the standardization and parsability of knowledge base data sources.

Common Pitfalls

The model states it cannot read uploaded xlsx table content during a conversation. Symptoms include responses like "Sorry, I cannot directly read and understand the specific content of tables" or similar prompts. This occurs because the knowledge base processing does not perform structured parsing of table files. It treats them as ordinary text segments, preventing the model from extracting specific row and column data.
When a user asks for a specific parameter value of a particular product model, the model returns parameters for multiple product models or general introductions. Symptoms include imprecise or irrelevant product information in the results. This happens because the knowledge base chunking does not effectively differentiate contexts for different product models, or the retrieval strategy relies too heavily on general semantic similarity, failing to prioritize precise product identifiers.
After updating a product manual, the model still returns old version information for queries about the new content. Symptoms include model answers that do not align with the latest published product information. This occurs because the knowledge base does not promptly re-index updated files, or the indexing update mechanism has a delay, leading to desynchronization between the knowledge base content and the actual data source.

Verification Steps

Upload an xlsx file containing parameters for multiple products. Attempt to query a specific parameter for a particular product model to verify if the model can accurately return that parameter value.
Ask the model how to perform a complex operation step from a product manual. Verify that the returned steps are complete, accurate, and consistent with the original text logic.
Update a product's technical parameter, for example, changing the maximum pressure tolerance from 10 atm to 12 atm. Then query this parameter to verify if the model can accurately recall and cite the updated value.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.