Characteristics of Data in this Category
Data sources for home medical device R&D documents are diverse. They include product requirement specifications, design verification reports, risk management files, software test reports, user manual drafts, and regulatory compliance documents. These documents typically exist in formats such as PDF, Word, Excel, and scanned images. Data update frequency is relatively low, primarily concentrated during different product development stages, such as concept design, prototype development, verification testing, and pre-market submission. Document structures usually follow medical device industry standards, such as ISO 13485 quality management system requirements, including fixed chapter titles and content layouts. Fields and units are highly specialized. Examples include biosignal parameters (millivolts mV, hertz Hz), mechanical dimensions (millimeters mm, grams g), battery life (hours h), and measurement accuracy (percentage %, parts per million ppm). There is an extremely high requirement for numerical accuracy and consistency.
Constraints Imposed by these Characteristics on "Multi-turn Conversation and Prompts"
The specialized and standardized nature of home medical R&D documents requires multi-turn conversation systems to accurately identify and link to specific standards, regulations, or test methods when understanding user queries. The low frequency of document updates means knowledge base construction and maintenance can be relatively stable. However, each update requires strict version control and incremental synchronization to ensure conversation results are based on the latest, audited data. Documents contain numerous charts and scanned images, posing challenges for optical character recognition (OCR) and chart parsing capabilities. This directly impacts the accuracy of knowledge recall and can lead to "information not found" feedback during conversations. The strictness of fields and units requires prompt design to guide the model to focus on the context and units of numerical values. This prevents misleading answers due to unit confusion or misinterpretation of values, such as misreading mg as g. The conversation system must also handle user queries for specific chapters or page numbers, requiring knowledge chunks to retain original document structural information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters (characters) | Balances paragraph length and information density in home medical documents, preventing single chunks from being overloaded or too sparse. |
Recall count (Recall Count) | 8–12 entries (items) | Ensures sufficient relevant document segments are covered for complex queries, while avoiding interference from irrelevant information. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures recalled results are highly relevant to the query intent, filtering out vague matches and improving precision. |
maxContext | 4096 tokens | Accommodates the context requirements of lengthy R&D documents, retaining enough conversation history and document segments. |
Rerank result count (Reranked Return Count) | 3–5 entries (items) | Reranks recall results to prioritize a small number of the most relevant pieces of information for the user. |
Temperature | 0.3–0.5 | Ensures the model's answers are accurate and consistent, reducing the risk of generating irrelevant information, aligning with the rigor required in the medical field. |
Three Common Mistakes
- Errors when quoting database connection plugins in a conversation typically occur due to an invalid
API KEYorconnectionStringin the plugin configuration, or insufficient database permissions preventing access to specified tables. - Setting the
Similarity threshold(Similarity Threshold) too high leads to "information not found." This happens because the system too strictly filters potentially relevant document segments, failing to recall even weakly related information. - Users copying conversation content encounter formatting errors or missing line breaks. This usually indicates that the frontend rendering or backend
Markdownconversion failed to correctly process special characters or paragraph markers in the text, causing symbols like\nto not be parsed as line breaks.
How to Verify Configuration
- Test the conversation system with a series of questions containing specific parameters, units, and regulatory standards. Verify that it accurately provides numerical values and cited chapters from the corresponding documents, and manually compare to confirm answer precision.
- Simulate typical questions asked by users at different R&D stages. Check if the recalled document segments cover all key information points and verify if the settings for
Recall count(Recall Count) andSimilarity threshold(Similarity Threshold) are appropriate. - Compare the system's parsing ability for relevant content after processing documents with charts or scanned images. Check if the answers correctly cite chart titles or key data to confirm the effectiveness of the
OCRand chart parsing modules.
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.