Tool Calling and Plugins for Home Medical Device

Data Characteristics for this Category

Home medical device registration data comes from various sources. These include regulatory documents, standards, test reports, clinical evaluation reports, product technical requirements, and instructions. Documents are typically in PDF, Word, or Excel formats. Data updates are infrequent, occurring mainly when regulations change, standards update, products iterate, or clinical data is added. Document structures are highly standardized, following NMPA registration requirements, such as the "Medical Device Registration Application Data Requirements and Approval Certificate Format." Fields include product name, model specifications, manufacturer information, intended use, contraindications, technical indicators, test methods, and clinical evaluation conclusions. Specific units, such as mm, V, mA, ℃, and kPa, are common. Some data may be in tables, requiring structured extraction.

Constraints from these Characteristics on Tool Calling and Plugins

The highly standardized and structured nature of home medical device registration documents requires tool calling and plugins to have precise structured information extraction capabilities. Specific format requirements for regulatory and standard documents make PDF and Word document parsing crucial. The system must accurately identify sections, headings, and table content. Infrequent updates mean knowledge base construction can focus on high-quality, one-time entry. However, a mechanism is needed to handle localized knowledge refreshes when regulations update. The specificity of fields and units requires tools to understand and validate the meaning and correctness of these specific fields and units during information extraction. For example, when comparing technical indicators, unit consistency must be ensured. Additionally, since registration documents involve multiple organizations, such as testing and clinical institutions, tool calling needs to support multi-source data integration and consistency verification. This can involve API calls to external databases for cross-verification of enterprise qualifications or product models.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192 tokens`	Ensures complete transmission of longer text segments, such as regulatory clauses and technical requirements, for analysis.
`Chunk size`	`500 characters`	Balances semantic integrity of text with model processing efficiency, preventing excessive truncation of long texts.
`Recall count`	`Top 8 entries`	Considering the rigor of registration documents, increasing recall quantity covers potentially relevant information.
`Similarity threshold`	Calibrate empirically	Adjust based on actual document content and retrieval effectiveness to filter irrelevant content without missing important information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Addresses parsing demands for large PDFs or complex Word documents, preventing parsing timeouts.
`pluginRetryLimit`	`3 times`	Plugin calls may fail due to network fluctuations or transient external service failures; retries improve stability.

Three Common Mistakes

After tool execution, the output lacks critical technical parameters, such as an empty Measurement accuracy field for a medical device. This usually means the document parsing failed to accurately identify or extract the specific field, or the model did not correctly reference it during generation.
In a workflow, subsequent AI model calls fail to use structured data generated by a previous model call, leading to information discontinuity. This indicates a mismatch between the tool's output format and the subsequent model's expected input format, or improper context passing mechanism configuration.
When a plugin calls an external interface, HTTP 401 Unauthorized errors frequently occur. This often happens due to an expired API key, incorrect permission configuration, or credentials not being passed correctly to the plugin module.

How to Verify Configuration

Run the tool calling process on a specific technical requirements document. Check if extracted key fields like product model and Intended Use in the output match the original text exactly.
Simulate a test report containing typical tabular data. Verify if the tool can correctly identify and structure information like test item, Results, and unit from the table, and check if data types meet expectations.
Configure an external information query plugin, such as an API for querying enterprise qualifications. Manually trigger the call and check plugin logs to confirm correct API request parameters, expected response data, and that the FastGPT interface displays the correct return content.

The values given 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.