Data Characteristics for This Category
Rehabilitation equipment, a critical component of medical devices, has distinct quality documentation characteristics. Data sources include product design specifications, manufacturing process regulations, inspection and test reports, risk management files, clinical evaluation reports, and user feedback records. Document update frequency can be high, especially with regulatory changes, product iterations, or batch quality issues, though core design files remain relatively stable. Document structure typically follows medical device registration and quality management standards (e.g., ISO 13485, GMP), including detailed sections and attachments. Fields often cover device model, serial number, batch number, production date, expiration date, key component suppliers, performance indicators (e.g., power, frequency, treatment modes), and safety parameters (e.g., leakage current, insulation resistance). Units strictly adhere to the International System of Units (SI) or industry standards, such as W (watts), Hz (hertz), V (volts), Ω (ohms), and mm (millimeters).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The characteristics of rehabilitation equipment quality documentation impose specific requirements on tool calling and plugin configuration. First, documents contain extensive structured and semi-structured data, such as numerical indicators and qualification judgments in inspection reports. Tools must accurately parse and extract this data, for example, using regular expressions or specific field parsing plugins. Second, the periodic and triggered nature of document updates necessitates designing scheduled or event-driven tool calling strategies to ensure knowledge base timeliness. An example is automatically triggering data extraction and updates when a new inspection report is uploaded. Additionally, specialized terminology and measurement units in documents require standardized entity handling in tool calls to prevent data misinterpretation or calculation errors due to inconsistent units. For Failure Mode and Effects Analysis (FMEA) tables in risk management files, plugins need the capability to process tabular data and perform logical judgments to support defect traceability or risk assessment.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 4000 | Quality documents are often lengthy; this ensures context completeness and avoids critical information truncation. |
Chunk size | 500 characters | Balances semantic integrity and retrieval efficiency, preventing dilution of key information by overly long segments. |
Similarity threshold | 0.75 | Medical device quality documentation demands high precision; increasing the threshold reduces irrelevant recalls. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient time for parsing large PDFs or scanned documents, preventing timeout failures. |
toolCallTimeout | 120 seconds | Database queries or external API calls may involve complex logic or large datasets; this ensures completion. |
Rerank result count | Top 5 entries | Ensures the final answer focuses on a few most relevant, high-quality pieces of information. |
Three Common Pitfalls
- Tool call nodes execute without response or stall. This is typically due to external service interface response timeouts or unexpected return formats, preventing the Agent from further processing.
- Base64 encoded images in Markdown rendering fail to display correctly. This can be caused by special characters in the encoded string not being properly escaped, or the image size exceeding rendering limits.
- Database connection tools return a
404error. This likely indicates incorrect Alibaba Cloud Lingjun API addresses or authentication information configured in OneAPI, leading to a failure to access the target service.
How to Verify Proper Configuration
- Upload an inspection report PDF containing structured data. Verify that the tool accurately extracts device model, serial number, key performance parameters, and corresponding units, writes them to the specified database table, and check the correctness of field values in the database.
- Simulate a scenario where a product batch has a quality issue. Trigger a toolchain that includes risk assessment logic. Check if the system can automatically identify the risk level based on document content and output corresponding handling suggestions. Compare the output with expected logic.
- For a document containing complex charts, test whether its Base64 encoded images render correctly in knowledge base Q&A. Verify image clarity and check if the rendered image content matches the original.
Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.
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.