Data Characteristics
Home medical device registration documents originate from various sources. These include product technical requirements, inspection reports, clinical evaluation reports, risk management reports, instruction manuals, labels, and internal quality management system files. Document updates are relatively stable, typically occurring during product design changes, regulatory updates, or periodic audits. The document structure is hierarchical, organized into chapters and clauses, often in PDF or Word format. Fields include product model, technical parameters, intended use, contraindications, and manufacturer information. Units strictly adhere to national or industry standards; for example, power is in W, voltage in V, and dimensions in mm.
Constraints on Multi-Turn Conversations and Prompts
The hierarchical structure and strict unit requirements of home medical device documentation impose specific demands on multi-turn conversation accuracy and prompt construction. The model must precisely identify numbers and units when extracting information to avoid confusion or misinterpretation, given the rigor of technical parameters and regulatory clauses. Multi-turn conversations require context tracking to accurately link to previously mentioned products or sections when users inquire about specific technical details. Document updates are infrequent, so the knowledge base synchronization mechanism must ensure timely updates during significant regulatory or product changes to maintain the timeliness and authority of conversation results. Prompt design needs to guide the model to focus on key fields and handle information extraction challenges from different document formats.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 6 turns | Covers the typical depth of user follow-up questions on complex technical issues |
Chunk size (Segment Length) | 800–1200 characters | Balances completeness of long technical documents with model processing efficiency |
Recall count (Recall Count) | top 8 | Increases coverage of relevant information for complex queries |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures precision of recalled content, avoiding interference from irrelevant information |
Rerank result count (Rerank Return Count) | top 3 | Focuses on the most relevant key information, reducing model processing load |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates parsing large PDF documents, preventing file processing failures due to timeouts |
Common Pitfalls
- The model suddenly fails to answer specific technical parameters in a multi-turn conversation. This happens when the
maxContextwindow is too small, causing the model to lose product details or document section information mentioned earlier in the conversation. - After a user uploads images or multimodal documents, the system displays an "incorrect format" error. This usually indicates that the model or plugin does not support specific image formats (such as
TIFFmedical images) or multimodal data types. - The conversation system becomes unresponsive for an extended period during knowledge base searches and eventually times out. This can occur if the knowledge base segment length is too large, leading to prolonged vector retrieval times, or if the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is insufficient for the current document size.
Verification Steps
- Select typical home medical device registration documents. Simulate continuous user inquiries, from product overview to specific technical parameters, to verify the model's ability to maintain context accurately in multi-turn conversations.
- Upload various document formats (e.g., PDFs with images, scanned documents). Check if the system successfully parses them and extracts key fields, confirming no "incorrect format" or parsing timeout errors.
- For scenarios like regulatory updates or product changes, update a small amount of knowledge base content. Then, ask relevant questions to verify if the model reflects the latest information promptly, ensuring the knowledge base synchronization mechanism is effective.
The values provided are common starting points. Measure performance against specific 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.