Data Characteristics
Cardiovascular intervention quality documentation sources include research and development records, manufacturing process files, quality inspection reports, clinical trial data, adverse event reports, and post-market surveillance documents from medical device manufacturers. These documents update frequently, especially after product iterations, regulatory revisions, or clinical feedback. Document structures are highly standardized, often adhering to ISO 13485 or NMPA regulations, with clear section divisions, figures, and appendices. Common fields include device model, batch number, production date, expiration date, sterilization method, main material components, performance parameters (e.g., guidewire diameter 0.014 inches, balloon diameter 3.0 mm, stent length 28 mm), inspection results, and corresponding regulatory clause numbers. Units use international standards like millimeters, inches, milligrams, and Newtons, often accompanied by upper and lower limits.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The standardized structure and high update frequency of cardiovascular intervention quality documents require multi-turn dialogue systems to perform rapid indexing and precise matching. The large volume of specialized terminology, technical parameters, and regulatory provisions means prompt design must be highly focused to avoid ambiguity. For example, a query about "stents" may require clarification on whether it refers to "coronary stents" or "peripheral stents," or "bare-metal stents" or "drug-eluting stents," and further tracing of its batch or model. Numerical ranges and units in documents require the system to understand and process numerical queries, such as "find products with balloon diameters greater than 3.5 mm." The citation and relevance of regulatory clauses necessitate multi-turn conversations that support cross-document and cross-chapter knowledge integration to answer regulatory compliance or traceability questions. Due to data sensitivity, dialogue systems also have strict requirements for access permissions and data isolation to ensure query accuracy and compliance.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 8 | Ensures sufficient historical information is covered in multi-turn conversations, facilitating tracing technical details and regulatory clauses. |
Chunk size (Segment Length) | 500 characters (characters) | Accommodates paragraphs containing extensive technical descriptions and parameter tables in quality documents, ensuring semantic completeness. |
Recall count (Recall Count) | 10 entries (items) | Increases the coverage of relevant document segments recalled from the knowledge base, improving the hit rate for complex queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision and recall rate, reducing interference from irrelevant technical documents and improving answer accuracy. |
Rerank result count (Reranked Return Count) | 5 entries (items) | Selects the most relevant document segments for the large model, reducing model processing load and improving inference efficiency. |
queryExtension | true | Performs semantic expansion of user queries, which helps process specialized terminology and abbreviations, enhancing recall capability. |
Three Common Mistakes
- A
404error during a conversation often indicates incorrect URL configuration or network issues between the external system and the FastGPT API. - Inability to retrieve specific session records based on
customUidusually means thecustomUidparameter was not correctly passed in the API request, or the backend query logic does not filter by this field. - Missing key technical parameters or regulatory clauses in conversation results typically occur when knowledge base document segmentation is too fine, leading to context loss, or when prompts do not explicitly guide the model to focus on this information.
How to Verify Configuration
- Simulate various cardiovascular intervention device quality queries to check if the system can accurately recall relevant technical parameters and regulatory clauses, and verify if the number of recalled items matches the expected threshold.
- Test complex multi-turn conversation scenarios, such as tracing the production process or adverse event reports for a specific batch of products, to confirm if the conversation context is effectively maintained and evaluate the reasonableness of the
maxContextconfiguration. - Attempt to query parameters involving numerical ranges, such as "products with balloon diameters greater than
3.5 mm," to verify if the system can correctly understand and filter for matching results, and confirm that units in the output are correct.
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.