Data Characteristics for This Category
High-value consumable registration documents draw from various sources. These include product technical requirements, inspection reports, clinical evaluation reports, risk management reports, instructions for use, labels, manufacturing process flows, and raw material supplier qualifications. Documents typically exist in PDF, Word, or Excel formats. They contain extensive specialized terminology, charts, and data. Update frequency varies due to product iterations and regulatory revisions; multiple revisions usually occur throughout a product's lifecycle. Document structures are rigorous, with strict logical connections and references between sections. Fields and units involve medical device-specific terminology, International System of Units (SI), and industry-standard measurement units like millimeters (mm), milligrams (mg), units (U), and flow rate (ml/min). Precision requirements are extremely high.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The specialized and rigorous nature of high-value consumable registration documents demands high accuracy for multi-turn conversations and effective prompts. Complex document structures and dense specialized terminology mean knowledge base chunking and vectorization must prioritize semantic integrity. This prevents information deviation from misinterpretations. High-precision data requirements mean the model must strictly adhere to original data when generating responses. It must avoid any speculation or generalization. During multi-turn conversations, users may frequently refer back to specific data points or concepts from previous turns. This requires the conversational system to have strong context understanding and referencing capabilities. Furthermore, regulatory updates and product iterations lead to document changes. The knowledge base needs an efficient incremental update mechanism to ensure conversations use the latest, most accurate information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 24000 characters | High-value consumable documents have strong contextual relevance. A longer conversation history is needed to maintain semantic coherence. |
Chunk Length | 800–1200 characters | Balances semantic integrity of professional documents with vectorization efficiency. Prevents key information from being split. |
Recall Count | Top 8 | Ensures coverage of multi-source, multi-perspective registration document information. Improves relevance recall rate. |
Similarity Threshold | 0.78–0.85 | Strictly filters knowledge chunks highly relevant to high-value consumable professional questions. Reduces noise. |
Rerank Return Count | Top 4 | Selects the most relevant core information. Reduces model processing burden and improves response accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF and Word documents often takes a long time. This provides sufficient parsing time. |
Three Common Mistakes
- A response in the conversation states "no relevant regulatory terms found." This may occur if knowledge base chunks are too fine-grained, leading to incomplete semantics for individual regulatory terms. Alternatively, the similarity threshold may be set too high, failing to recall relevant content.
- The system provides inaccurate answers to inquiries about specific parameters in registration documents. This manifests as returned data not matching the original or incorrect units. This happens when the knowledge base fails to effectively structure or recognize units when processing tabular data or numerical values with units.
- After uploading a large registration document file, the file parsing status remains stuck or reports an error. This appears as
The value of "offset" is out of rangeorUPLOAD_FILE_MAX_SIZEerrors. This typically occurs because file size or parsing timeout limits are not adjusted, preventing the file from being fully processed.
How to Verify Correct Configuration
- Select key regulatory terms from high-value consumable registration documents. Ask questions through multi-turn conversations. Verify if the system accurately cites the original text and explains its scope of application.
- Simulate user inquiries about specific product technical parameters, such as dimensions, materials, and performance indicators. Verify if the system can precisely extract and provide numerical values with correct units from the knowledge base.
- Upload a registration document file containing complex charts and tables. Observe if the file parsing process completes successfully. Then, try asking questions about the data in the charts to check response accuracy.
- For a product instruction manual with multiple revision versions, verify if the system can identify and cite the latest version based on context during multi-turn conversations.
Note: The values provided are common starting points. Always measure them against your 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.