What the data for this category looks like
Data sources for medical device marketing content mainly include registration certificates and filing documents issued by the national medical products authority, product manuals and clinical research materials provided by manufacturers, and marketing script templates reviewed by compliance departments. Update cycles vary by category: consumables have a higher update frequency, while large equipment has a longer update cycle. The structure of a single document usually includes fields such as registration certificate number, model specification, applicable departments, clinical indications, and contraindications. Parameter content needs to be marked with exclusive units such as mm, mSv, and number of scan layers, and marketing scripts must be bound to corresponding product models.
What constraints do these characteristics impose on knowledge base retrieval and recall
Compliant data sources for medical devices require that retrieval and recall results must match the latest registration certificates and policy documents, otherwise compliance risks will be triggered. Differences in document structures across multiple categories require precise grouped recall by product model and registration certificate number during retrieval, to avoid cross-category confusion. Parameter content with exclusive units requires retaining unit associations during recall, otherwise information deviation may occur in clinical scenarios. Frequently updated documents for consumables require the recall link to support incremental synchronization, to avoid returning expired content. Long documents for large equipment require limiting the splitting granularity of single documents, to avoid context overflow.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Medical device documents often contain high-resolution images and clinical research data, with single-file volume larger than that of general product categories, so the upload limit needs to be relaxed |
chunk_size | 800–1200 characters | Medical device documents include long paragraphs of clinical indication descriptions. Excessive splitting will destroy semantic associations, while too short splitting will increase retrieval redundancy |
recall_top_k | Top 6 entries | Medical device marketing content needs to balance compliance and scene matching. Too many recall entries will increase context token consumption, while too few will fail to cover all compliance points |
similarity_threshold | 0.75–0.85 | Medical device parameters and indications have high matching accuracy requirements, so a high similarity threshold needs to be set to avoid recalling irrelevant documents |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large medical device documents requires processing multi-page images and complex tables, so the timeout period needs to be extended |
enable_incremental_sync | Enabled | Documents for consumable medical devices have high update frequency, and incremental synchronization can ensure that recalled content is always the latest version |
The parameter values provided on this page are all common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the configuration.
Three common mistakes
- Phenomenon: Retrieval results do not match the queried medical device model, or the response content is unrelated to the knowledge base documents. Cause: The
similarity_thresholdis not set or the threshold is too low, and recall is not grouped by product model, resulting in irrelevant content being recalled. - Phenomenon: A prompt for file size limit is displayed when uploading large medical device documents. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration is not adjusted, and the default limit for general product categories is used, which cannot accommodate medical device documents with high-resolution images. - Phenomenon: An
insufficient_quotaprompt is displayed during calls, indicating that upstream load is saturated. Cause: The context length of single-round question answering and the number of recalled entries are not limited, resulting in LLM call requests exceeding the group load limit.
How to confirm the configuration is correct
- Upload a single medical device document containing high-resolution images, confirm that the upload is successful and there are no parsing errors, and check whether the configuration items match business requirements.
- Initiate a query containing specific product models and parameters, check whether the recall results include compliant content of the corresponding models, and confirm that the similarity threshold and number of recalled entries meet accuracy requirements.
- Upload updated medical device policy documents, confirm that the knowledge base automatically synchronizes the latest content, and check whether the incremental synchronization configuration is enabled.
- Initiate multiple consecutive queries, confirm that the response speed is stable and there is no obvious abnormal token consumption, and check whether the context length limit configuration is reasonable.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.