Tool Calling and Plugins for Telemedicine Registration

Data Characteristics in This Category

Telemedicine registration documents involve various data types and sources. Core data includes medical institution qualification files, physician practice licenses, telemedicine service agreements, de-identified patient electronic health records, equipment procurement and calibration records, and service process SOP documents. This data typically exists in both structured (e.g., database records, JSON, XML) and unstructured (e.g., PDF documents, Word documents, scanned images) formats. Data update frequency varies by type; for example, physician practice information may update annually, while service agreements or SOP documents update with policy adjustments or business changes. Document structure often follows fixed templates for medical institution qualification files, while service agreements have diverse clauses. Fields and units involve highly precise medical terminology, dosage units, timestamps, and geographical coordinates.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The data characteristics of telemedicine registration documents impose specific constraints on tool calling and plugins. First, diverse and heterogeneous data requires plugins with robust file parsing and structured extraction capabilities, especially for tables and text content within PDFs and scanned images. Second, inconsistent data update frequencies, such as periodic updates for qualification certificates, mean tool calling needs to support version management and incremental update mechanisms to ensure the use of the latest valid information. Third, the precision of medical professional fields, such as the dosage unit mg/kg, requires plugins to perform unit identification and standardization after data extraction to prevent declaration errors due to unit confusion. Additionally, patient privacy protection mandates that all tool calls involving electronic health records must embed de-identification or access control, adhering to strict compliance requirements. Finally, the complexity of service process SOP documents, which may contain nested logic or conditional judgments, requires plugins to understand and execute these business rules.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Declaration documents often contain numerous scanned images or high-resolution pictures, requiring a larger file upload limit.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large PDF documents or complex tables take longer to parse, preventing parsing failures due to timeouts.
`maxContext`	`8000 tokens`	Legal texts like telemedicine service agreements are lengthy, requiring a larger context window for semantic understanding.
`Chunk size`	`500 characters`	Ensures each text segment retains sufficient semantic integrity, especially when describing medical procedures.
`Similarity threshold`	`0.75`	Medical terminology and regulatory clauses require high similarity for precise matching, avoiding false recalls.
`http_timeout`	`60 seconds`	Allows sufficient response time when calling external compliance verification interfaces or database queries.

Three Common Mistakes

HTTP 504 Gateway Timeout errors occur when calling external APIs. This happens because external compliance or verification interfaces related to telemedicine respond slowly, and the http_timeout parameter is set too short.
After parsing an uploaded xlsx file, the AI conversation fails to accurately identify key fields. The AI response lacks references to tabular data. This occurs because the PARSE_FILE_TIMEOUT_SECONDS parameter is too short, leading to large table files not being fully parsed or errors during parsing.
In tool call results, fields that should be numerical appear empty or with incorrect formats, such as missing dosage units. This happens because the file parsing plugin has insufficient unit recognition capabilities for medical professional fields or lacks configured standardization logic.

How to Confirm Correct Configuration

Upload and parse a PDF file of a telemedicine service agreement containing complex tables and multiple pages. Check if the AI conversation can accurately extract and cite key clauses and data, such as service scope and liability division, to confirm parsing and context processing capabilities.
Configure a tool to call an external National Medical Products Administration database. Simulate a query for specific medical device registration information. Check if the returned results include expected approval numbers and validity periods to confirm correct external API calls and data mapping.
Upload an image file containing a scanned physician practice license. Configure an OCR tool to recognize information such as the physician's name and license number. Then, verify if the AI conversation can make judgments or generate reports based on this information to confirm unstructured data extraction capabilities.

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.