Data characteristics of this category
The data for medical device intelligent due diligence reports primarily originates from medical device registration certificates, original clinical trial data, supplier procurement lists, compliance review documents, and similar sources. Update frequency varies by document type: registration certificates follow scheduled update cycles, clinical trial data is updated in stages alongside trial progress, and procurement lists are updated in real time during procurement workflows. Document structures include nested tables, long-form compliance descriptions, and structured field entries. Fields include registration certificate numbers, model numbers, manufacturers, expiration dates, detection accuracy, measurement ranges, and additional parameters. Most parameters are tied to specific units, such as millimeters, pascals, newtons per meter, and other standard units.
Constraints on document parsing and chunking
The characteristics of medical device due diligence documents create multiple constraints for the parsing and chunking process. Nested tables require parsing tools to retain associations between cells and their context, to avoid splitting parameters away from their associated units during chunking. The mixed structure of long-form compliance descriptions and short parameter entries requires chunking granularity to balance semantic completeness and appropriate granularity, preventing short parameters from being assigned to unrelated chunks. Some documents are PDFs converted from scanned images, so enhanced parsing support is required to accurately extract text and table content. The association between structured fields and their tied units requires retention after parsing, to avoid separation of parameters and units during subsequent vector retrieval.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
enable_minerU_pdf_parse | Enabled | Adapts to scanned registration certificate and clinical trial PDFs common in medical device documents, retaining the layout structure of tables and text |
chunk_size | 800–1200 characters | Medical device documents include long compliance text and short parameter entries. This range balances semantic completeness for long text and appropriate chunking granularity for short parameters |
chunk_overlap | 100–150 characters | Prevents parameters and units across chunk boundaries from being separated, retaining contextual association |
enable_table_vector | Enabled | Tables make up a large portion of medical device documents. Enabling this setting treats tables as independent vector units, preventing table content from being split across multiple chunks |
parse_timeout | 600 seconds | Some medical device due diligence documents include multi-page clinical trial data, requiring a sufficient timeout to complete full parsing |
max_table_cell_length | 2000 characters | Adapts to long descriptive cell content in medical device parameter tables, preventing content from being truncated |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Scenario: Knowledge base parsing tasks show as failed, with
parse_timeouterrors in logs. Cause: Theparse_timeoutconfiguration was not adjusted to a sufficient duration. Medical device due diligence documents often include multi-page clinical trial data, and standard timeout settings are insufficient to complete full parsing. - Scenario: Fields are empty after table parsing, and parameter units cannot be matched during vector retrieval. Cause: The
enable_table_vectorconfiguration was not enabled. Table content was treated as regular text and split, breaking the association between parameters and units. - Scenario: The enhanced PDF parsing function cannot be enabled in the plugin interface. Cause: The minerU dependency was not configured correctly during local deployment, or the
enable_minerU_pdf_parseswitch was not enabled in the knowledge base settings.
How to confirm configurations are correctly applied
- Upload a single-page medical device registration certificate PDF, and check if the parsed text retains the row and column structure of tables, and if parameters and units are displayed as bound pairs.
- Navigate to the knowledge base settings page, and confirm that the
enable_minerU_pdf_parseandenable_table_vectorswitches are enabled. - Upload a document containing multi-page clinical trial data, and check if the parsing task duration meets expectations, with no early termination errors.
- Initiate a vector retrieval test, enter parameter keywords, and confirm that the retrieved results include complete parameter and unit information.
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.