Tool Calling and Plugins for Rehabilitation Equipment

Data Characteristics for This Category

Rehabilitation equipment data originates primarily from manufacturer product manuals, technical specifications, clinical evaluation reports, and medical device registration information. This data typically exists as PDFs, Word documents, XML, or structured databases. Update frequency involves bulk updates when new products launch or existing products upgrade. However, core technical parameters and indications remain relatively stable throughout a product's lifecycle, with infrequent revisions. Document structure often includes sections like product model, technical parameters, functional descriptions, scope of application, contraindications, and maintenance. Fields may involve physical dimensions (millimeters, centimeters), weight (kilograms), power (watts), voltage (volts), frequency (hertz), treatment modes (e.g., pulse, continuous), intensity range, and time setting precision. Units are clearly defined and generally conform to international standards.

Constraints on Tool Calling and Plugins Due to These Characteristics

The documented nature of rehabilitation equipment data sources requires tool calling to efficiently parse key information from unstructured text and structure it. For example, extracting technical parameter tables from PDFs or identifying indication descriptions from clinical reports. Due to the low data update frequency, knowledge base update strategies can focus on periodic full synchronization or incremental updates for major manufacturers' product lines. The precision of fields and standardization of units mean that tool calling requires strict data type validation and unit conversion to avoid errors from inconsistent data formats. Furthermore, the uniqueness of product models and serial numbers dictates that queries need to support a combination of exact matching and fuzzy searching to handle diverse user input. The presence of sensitive information like contraindications requires caution in tool calling responses, potentially needing integration with specific business logic for risk warnings.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`6000 characters`	Rehabilitation equipment product manuals are of moderate length; this covers most core information.
`Chunk size`	`500 characters`	Ensures a single segment can contain a complete functional description or parameter list.
`Recall count`	`Top 8 entries`	Balances recall efficiency with information completeness, covering multiple highly relevant product features.
`Similarity threshold`	`0.78–0.85`	The medical device field demands high information precision, avoiding irrelevant or low-relevance results.
`toolCallTimeout`	`60 seconds`	Most external tool API response times fall within this range, preventing excessively long waits.
`toolSchemaVersion`	`v1.2`	Ensures compatibility with the current FastGPT plugin interface protocol version, supporting new features.

Three Common Pitfalls

External API calls returning a 400 status code often result from the data parameter structure not matching API expectations. This includes missing required fields or incorrect field types.
A workflow has context set, but the model does not use context information after an API call. This happens when the model parameter during the API call points to a general model not configured for context, failing to specify the model with context logic within the workflow.
Product parameters extracted from PDF documents appear as empty values or have incorrect formats. This typically occurs because the PDF parser has insufficient capability to recognize tables or specific layouts, leading to failed structured information extraction.

How to Confirm Correct Configuration

Use the FastGPT debugging interface to query the rehabilitation equipment knowledge base. Observe if the returned results include accurate product models, technical parameters, and scope of application. Check the cited original passages.
For scenarios with tool calling configured, simulate user inquiries. Verify if the tool triggers correctly and if the model effectively integrates the tool's returned data into the response.
Examine log outputs to confirm that the parameters and response content of external tool API calls meet expectations, especially regarding data format and units.

Note: The values provided are common starting points. Measure them against specific 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.