Knowledge Base Retrieval and Recall for Cardiovascular

Data Characteristics for this Category

Registration and declaration documents for cardiovascular interventional medical devices primarily originate from regulatory documents, guidelines, and technical review reports published by the National Medical Products Administration (NMPA) and international medical device regulatory bodies (such as FDA, CE MDR). Internal company documents, including product development documentation, clinical trial reports, risk analysis reports, and manufacturing process files, also contribute. Data update frequency is relatively low. Regulatory documents are typically revised or new guidelines issued annually, while internal company documents are updated continuously throughout the product lifecycle. Document structure is primarily unstructured text, containing extensive specialized terminology, charts, experimental data, and references. Fields and units are highly specialized, for example, "stent diameter" (unit: mm), "balloon pressure" (unit: atm), "device length" (unit: cm), and various biocompatibility indicators (e.g., "hemolysis rate").

Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall

The authoritative and rigorous nature of regulatory documents demands high precision and recall in knowledge base retrieval to ensure no critical clauses are missed. The update frequency and version management of internal company documents require the knowledge base to support incremental updates and version traceability to handle document changes due to product iterations. The presence of extensive specialized terminology and measurement units means traditional keyword-based retrieval can be prone to errors, necessitating stronger semantic understanding to match user queries with document content. Furthermore, the need to retrieve non-text content like charts and experimental data challenges the knowledge base's ability to handle heterogeneous data; purely text-based retrieval may not meet comprehensive requirements.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Balances semantic completeness with recall efficiency, preventing overly long paragraphs from diluting key information and overly short paragraphs from losing context.
`Recall count` (Recall Count)	`8–12 entries` (chunks)	The complexity of cardiovascular intervention data requires more recall results to improve coverage, but the quantity must be controlled to avoid interference from irrelevant information.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures the precision of recalled content, filtering out document fragments with low semantic relevance, thereby reducing noise.
`Rerank result count` (Rerank Return Count)	`3–5 entries` (chunks)	Optimizes results through a reranking model, focusing on the most relevant few results to improve the quality of the final presented content.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large regulatory documents and clinical reports requires longer parsing times to prevent parsing failures due to timeouts.
`embeddingModel`	`text-embedding-v3`	Employs an advanced vector model to enhance semantic understanding, better handling specialized terminology and complex sentence structures.

Common Pitfalls

Knowledge base status remains "indexing" for an extended period: This typically occurs when individual imported files are too large or the number of files is excessive, leading to a backlog in the background parsing task queue or parsing timeouts.
Retrieval results show abnormally high semantic similarity (e.g., 10000+): This indicates issues with the vector model or similarity calculation configuration, possibly due to the use of non-standard similarity metrics or missing data normalization.
Workflow interrupts during the knowledge base search step: This might be because the PARSE_FILE_TIMEOUT_SECONDS configured for the knowledge base is too short, failing to successfully parse large PDF documents or scanned images, leading to a lack of data for subsequent retrieval.

How to Verify Proper Configuration

Select a regulatory document containing specialized cardiovascular intervention terminology, perform a query, verify that the recalled results include key paragraphs from the document, and check the returned similarity value.
Upload a typical clinical trial report, observe the knowledge base parsing status, confirm it eventually becomes "ready," and check if the parsedChunkCount field value is reasonable.
Simulate queries for multiple common registration and declaration questions, compare retrieval results under different Recall count (Recall Count) and Similarity threshold (Similarity Threshold) settings, and determine a configuration combination that covers most expected answers.

Note: 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.