Data Characteristics for this Category
IVD diagnostic reagent data originates primarily from product manuals, registration certificates, clinical trial reports, literature, and internal experimental data. This data updates infrequently, typically with product registration, changes, or upgrades, a cycle that can range from months to years. Document structures are highly standardized. Product manuals follow formatting requirements from the National Medical Products Administration (NMPA) or international standards (e.g., IVDR). They include fields such as intended use, assay principle, main components, storage conditions, sample requirements, operating procedures, reference ranges, assay methodology, performance indicators (sensitivity, specificity), limits, precautions, result interpretation, and limitations. Field content is rigorous, often containing specific values, units (e.g., nmol/L, IU/mL, ℃), and professional terminology. Accuracy and consistency requirements are extremely high.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The highly standardized and specialized nature of IVD diagnostic reagent data requires multi-turn dialogue systems to precisely match professional terminology and context when understanding user intent, avoiding ambiguity. The inclusion of values and units in fields means the system must perform numerical comparisons and logical judgments, for example, determining if a test result falls within a reference range. Low update frequency makes knowledge base maintenance costs relatively manageable, but each update requires ensuring data completeness and consistency. The structured nature of documents facilitates building accurate knowledge graphs or metadata, thereby improving retrieval and question-answering accuracy. Due to the rigorous data, prompt design needs to guide the model to focus on factual answers, avoid generating speculative or subjective content, and handle in-depth user inquiries about product performance, operational details, or result interpretation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6 | IVD consultation dialogues typically revolve around specific products or technical points. Six turns of context are sufficient to maintain coherence and avoid performance degradation caused by overly long contexts. |
Chunk size (Segment Length) | 500 characters | Ensures each knowledge block contains enough information while avoiding redundancy or semantic drift due to excessive length, especially for key sections in manuals. |
Recall count (Recall Count) | 8–12 entries | Covers more potentially relevant knowledge, addressing the multi-faceted and complex nature of user questions, particularly for performance indicators and precautions. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves recall precision and reduces interference from irrelevant information. The IVD field demands high accuracy in answers. |
Rerank result count (Rerank Return Count) | 5 | After reranking, the top 5 entries typically provide the most relevant and high-quality information for the model's comprehensive judgment. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | IVD product manuals or reports can be large. This provides ample time to complete parsing, preventing file processing failures due to timeouts. |
Three Common Mistakes
- Dialogue errors such as "image cannot be recognized" or
400 invalid imagetypically occur because multimodal APIs have strict restrictions on image format, size, or content, failing to correctly process uploaded IVD test images or packaging images. - The model provides incorrect numerical values or unit confusion when answering product performance indicators. This happens because numerical fields were not strictly standardized for units or validated for numerical ranges during knowledge base construction, leading the model to acquire inaccurate information.
- When users ask about product operating procedures, the model provides only partial information or cannot offer coherent guidance. This occurs because knowledge segmentation is too fine-grained or the retrieval strategy fails to effectively link context between different steps, preventing the formation of a complete operational workflow.
How to Confirm Proper Configuration
- Select 5-10 typical IVD diagnostic reagent consultation scenarios. Simulate multi-turn dialogues to evaluate the accuracy, completeness, and professionalism of the answers, especially regarding product parameters and precautions.
- Randomly select 20 IVD product manuals. Upload them to the knowledge base and check the file parsing results to ensure key fields (e.g., intended use, components, reference ranges) are correctly extracted and indexed.
- For numerical questions about product performance indicators (e.g., sensitivity, specificity), verify that the model's numerical values and units match the original documentation. Also, check if it can perform basic numerical comparisons and judgments.
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.