Knowledge Base Retrieval for Cardiovascular Intervention

Data Characteristics

Cardiovascular intervention quality documents originate from medical device manufacturers, regulatory bodies (e.g., NMPA, FDA), and hospital quality management systems. Data updates are stable, typically aligning with product iterations, regulatory revisions, or clinical practice guideline updates. Major updates may occur every six months to a year, with minor revisions daily. Documents are primarily hierarchical PDFs, including quality manuals, Standard Operating Procedures (SOPs), inspection standards, risk assessment reports, product specifications, and clinical trial reports. These documents contain numerous charts, flowcharts, and specialized terminology. Fields include model_id, batch_no, prod_date, and sterilization_method. Units strictly adhere to international standards, such as mm, mg, ml, ℃, and Pa, demanding high precision.

Constraints on Knowledge Base Retrieval and Recall

The hierarchical structure and dense technical terminology of cardiovascular intervention quality documents require context preservation during document chunking to prevent information fragmentation. Charts and flowcharts mean pure text parsing may lose critical information, necessitating multimodal or enhanced text extraction capabilities. Infrequent but important update cycles require the knowledge base to support incremental updates and version management, ensuring retrieval timeliness and accuracy. Strict field and unit requirements challenge precise matching and numerical range queries, such as retrieving performance data for a specific device model within a temperature range. Compliance requirements also demand that retrieval results trace back to original document page numbers or sections for auditability.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`800–1200 characters`	Preserves the complete context of complex concepts and processes in cardiovascular intervention documents, preventing semantic fragmentation.
`Overlap Size`	`100–200 characters`	Ensures sufficient contextual overlap between adjacent chunks, improving retrieval relevance.
`Recall Count`	`Top 8–12 items`	Given the specialized nature and cross-referencing in cardiovascular intervention documents, increasing recall covers potentially relevant information.
`Similarity Threshold`	Calibrate by measurement	Adjust based on actual document content and query types using a test set to ensure high-precision recall.
`Rerank Count`	`Top 5 items`	Reranks recalled items to prioritize professional content most relevant to the query intent.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles parsing large PDF documents or those with complex charts, preventing timeouts.

Common Mistakes

Retrieval results contain many irrelevant or low-relevance items. This happens when document chunking is too granular or the similarity threshold is too low, leading to context loss or noise.
Queries for specific device models or batch information return empty or incomplete results. This occurs when fields like model_id or batch_no are not effectively identified and indexed, or when critical data in charts is not extracted during document parsing.
Users report an inability to retrieve the latest regulatory updates. This indicates outdated document versions in the knowledge base, often due to incorrect incremental update triggers or issues in the parsing and ingestion process for new documents.

Validation Steps

Select core cardiovascular intervention queries, such as "sterilization method for XX model stent" or "clinical trial results for XX device." Check if retrieval results contain key information and accurately point to the corresponding sections in the original documents.
For documents with charts and flowcharts, perform relevant queries. Verify if retrieval results reflect critical data or process steps from the charts. Check PARSE_FILE_TIMEOUT_SECONDS logs for timeout errors.
Upload a document with the latest regulatory revisions. After the knowledge base processes it, query the revised content to confirm correct recall of updated information.
Use numerical queries with different precision requirements, such as "products with catheter diameter greater than 2.5mm." Check if retrieval results precisely match or filter out relevant document snippets, and observe the performance of the Similarity Threshold.

The values provided are common starting points and should be measured against the reader's own samples.

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.