Tool Calling and Plugins for Infection Control Registration

Data Characteristics in this Category

Infection control registration document data typically originates from hospital internal infection surveillance systems, microbiology lab reports, drug and consumable procurement records, healthcare worker training records, and relevant regulations. This data updates frequently. For example, infection surveillance data might update daily or weekly, while regulations could revise quarterly or annually. Document structures vary, including structured database records, semi-structured electronic medical record text, unstructured PDF regulatory files, and Word-formatted SOPs. Fields and units are industry-specific, such as pathogen names, antimicrobial susceptibility profiles, infection sites, antimicrobial drug dosages (e.g., mg/kg), infection rates (e.g., ‰), and hand hygiene compliance (e.g., %).

Constraints on Tool Calling and Plugins

The diversity of infection control data challenges tool calling to integrate different data sources. High-frequency data updates require tools to support real-time or near real-time data fetching and synchronization, ensuring the timeliness of registration documents. For instance, rapid updates to microbiology test results necessitate plugins that can promptly retrieve and parse the latest resistance information. Complex document structures demand tools capable of processing various file formats and extracting key information, such as identifying regulatory clauses from PDFs or extracting SOP steps from Word documents. Furthermore, recognizing and standardizing industry-specific fields and units places higher demands on a plugin's data parsing and conversion capabilities. Examples include unifying drug dosages from different units to mg or converting infection rates from ‰ to percentages. These constraints directly influence API call parameter design and error handling logic.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`API_TIMEOUT_SECONDS`	`120 seconds`	Handles network delays when external systems respond slowly or have large data volumes, preventing frequent timeouts.
`MAX_RETRIES`	`3`	Allows automatic retries for transient network fluctuations or service unavailability, improving stability.
`CHUNK_SIZE_TOKENS`	`800-1200 characters`	Balances model processing capacity with information completeness, accommodating paragraph lengths in regulatory documents and SOPs.
`EXTERNAL_TOOL_SCHEMA_PATH`	`plugins/infection_control_schema.json`	Specifies the location of the tool description file, ensuring FastGPT correctly identifies tool capabilities.
`API_KEY_ENV_VAR`	`IC_DATA_API_KEY`	Manages sensitive credentials via environment variables, enhancing security and simplifying deployment.
`RATE_LIMIT_DELAY_MS`	`200 ms`	Adheres to external API rate limits, preventing bans due to excessive request frequency.

Three Common Mistakes

getaddrinfo ENOTFOUND 406 error when calling an external API: This indicates the target service address cannot be resolved, typically due to DNS configuration errors or network connectivity issues preventing the lookup of the corresponding IP address.
Tool call returns HTTP 400 Bad Request, but the same model works without tool invocation: This means the request body format or parameters do not conform to the external tool's API specification, such as mismatched field types or missing required parameters.
Specific fields are empty after document content extraction: For example, the "antimicrobial susceptibility profile" field in a microbiology report is not correctly parsed. This can happen if regular expressions or JSON Path configurations are inaccurate and fail to match the diverse expressions of fields within the document.

How to Confirm Correct Configuration

Check FastGPT's log system for an HTTP 200 OK status code for each tool call and verify that the elapsed time is within the API_TIMEOUT_SECONDS threshold.
Perform end-to-end tests for critical business scenarios, such as "querying antimicrobial resistance information for a specific pathogen," to confirm that key fields (e.g., 耐药率, Antibacterial drug name) in the returned results are accurate and not empty.
Compare data obtained by FastGPT's tool calls with raw data directly accessed from external systems. Confirm data consistency, especially that units and formats meet expectations, for example, mg/kg is unified to mg.
In FastGPT's "Plugin Management" interface, verify that the tool description pointed to by EXTERNAL_TOOL_SCHEMA_PATH is successfully loaded and that all defined action and parameter entries are recognizable and callable by the model.

The values provided are common starting points and should be measured against your 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.