Data Characteristics
Home healthcare device quality documentation includes design and development documents, production process control records, inspection reports, risk management files, user manuals, and post-market surveillance reports. These documents are typically in PDF, Word, and Excel formats, with PDFs being the most common. Data sources are primarily internal R&D, production, and quality departments, as well as external testing agencies and regulatory bodies.
Update frequency varies: design and development documents are updated frequently early in the product lifecycle; production records and inspection reports are generated continuously per batch; risk management files and regulatory reports are revised periodically or immediately based on regulatory requirements and market feedback.
Documents have a rigorous structure, often containing numerous charts, batch numbers, serial numbers, specific standard numbers, and units of measurement. Fields include material specifications, process parameters, inspection indicators, and tolerance ranges.
Constraints on Tool Calling and Plugins
The rigorous nature and multi-format characteristics of home healthcare quality documentation demand high accuracy and robustness from tool calling and plugins.
Charts and scanned images in documents mean pure text extraction may miss critical information. OCR and image understanding plugins are necessary.
Strongly formatted data like batch numbers and serial numbers require tool calls to precisely match regular expressions or specific data structures to avoid misidentification.
Frequently updated record-type documents require knowledge base indexing to support incremental updates and version management. Tool calls must specify or identify the latest version.
Diverse units of measurement and complex tolerance ranges require plugins to perform unit conversions and numerical range validation. Otherwise, this can lead to incorrect judgments or non-compliant recommendations.
Frequent updates to regulations and standards require plugins to acquire and apply the latest regulatory provisions promptly.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the potential size of large quality documents (e.g., extensive design archives or multi-year production record summaries) to ensure smooth file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large PDF documents or scanned files with complex charts require longer parsing times. This prevents parsing timeouts. |
maxContext | 3500 tokens | Quality documents contain dense technical terms. Sufficient context length supports understanding complex regulatory clauses and technical details. |
Chunk size | 800–1200 characters | Ensures each segment can contain a complete technical parameter description or regulatory clause, preventing semantic fragmentation. |
Similarity threshold | 0.78–0.85 | The medical device field demands high information accuracy. A higher threshold recalls more relevant and precise matches. |
ENABLE_OCR_PLUGIN | True | Critical information in many inspection reports, charts, and scanned images requires OCR recognition. |
Common Pitfalls
- External interface calls return
aiPointsNotEnough. This typically means insufficient platform credit to perform AI model inference or specific advanced plugin functions. - After uploading an XLSX file, AI dialogue or key data extraction fails. This might be because the file content consists only of images or complex macros, lacking text data recognizable by standard parsers. Enable or configure multimodal system plugins.
- Tool call returns fields that do not match expectations or are empty. This could be because the regular expression or JSON path configured for the plugin does not match the actual document format, preventing correct extraction of target information.
Verification Steps
- Upload representative multi-format quality documents (PDF, Word, Excel). Verify that the knowledge base correctly segments and extracts text content.
- Execute a query involving tool calls. Check the log output for a
plugin_call_successstatus code and confirm the returned result structure matches expectations. - For documents containing scanned images or charts, ask questions to verify if the AI correctly identifies and references key data or text within the images. This confirms the effectiveness of the OCR plugin.
- Simulate a scenario requiring unit conversion or numerical range validation. Verify if the plugin provides judgments or recommendations that comply with home healthcare device standards.
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.