Tool Calling and Plugins for High-Value Consumables

Data Characteristics for This Category

High-value consumable pharmacovigilance data originates from healthcare institution reporting systems, manufacturer proactive monitoring, and regulatory body inspection results. Data update frequencies vary. Post-market surveillance data typically aggregates quarterly or annually, but serious adverse event reports require submission within a specified timeframe (e.g., 72 hours). Document formats are diverse, including structured database records, unstructured clinical report texts (e.g., PDF format), and imaging data. Key fields include the consumable's unique identifier (e.g., UDI), production batch number, model specifications, implantation or usage date, basic patient information, adverse event description (ICD-10 code or free text), handling measures, event outcome, and reporting source. Some data also includes technical parameters like material composition and sterilization methods.

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

The complexity of high-value consumable data sources and varying update frequencies require tool calling and plugins to have robust data integration capabilities, handling multiple data formats. The presence of unstructured text and imaging data means relying solely on structured query interfaces is insufficient for comprehensive information retrieval; text parsing and image recognition plugins are also necessary. The consumable's unique identifier UDI and production batch number batch_number are critical for traceability. Any tool call must accurately transmit and match these parameters. Additionally, free-text content in adverse event descriptions requires Natural Language Processing (NLP) tools for entity extraction and event classification to enable subsequent analysis. For serious adverse events requiring high timeliness, plugins need to support real-time or near real-time API calls to ensure data update immediacy.

Configuration Settings

Configuration Item	Suggested Value	Rationale for This Value
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	High-value consumable adverse event reports often include multi-page documents or image attachments, requiring support for larger file uploads.
`maxContext`	`3000 Tokens`	For processing longer adverse event descriptions and clinical reports, ensuring context completeness.
`PARSE_FILE_TIMEOUT_SECONDS`	`180 seconds`	Parsing complex PDF reports or documents containing multiple images can be time-consuming.
`PluginCall Timeout Period`	`60 seconds`	External API calls, such as querying consumable batch information or code mapping, require sufficient response time.
`Parameter Passing Format`	`JSON`	Widely supported and easy to process complex structured data, such as queries containing `UDI` and `batch_number`.
`Error Retry Count`	`3 times`	To improve the robustness of plugin calls against network fluctuations or transient external service failures.

Three Common Pitfalls

When calling a plugin, the file type variable file_content is empty, preventing the external service from processing it. This typically occurs because the frontend file upload component did not correctly encode the file content as a Base64 string, or the backend file parameter was not configured as a string type.
Frequent invalid_request_error messages appear when combining the Claude model with tool calling. The reason might be a mismatch between the tool_code definition and the actual plugin interface's function definition, or the arguments structure passed to the tool does not conform to expectations.
After calling an interface and setting stream: true, the final result is not received correctly. This usually happens because the client, when processing streamed responses, does not correctly aggregate all chunked data, leading to a missing or incomplete final_answer field.

How to Verify Correct Configuration

Upload a PDF file containing a high-value consumable adverse event report. Verify that the system can correctly parse and extract key fields such as UDI, batch_number, and adverse event description.
Using FastGPT's debugging interface, simulate a plugin call with UDI and batch_number parameters. Check if the external system receives the correct parameters and returns the expected result.
Send a complex query that triggers text parsing and external tool calls. Observe if the response time is within the PluginCall Timeout Period setting and if the final result includes key information returned by the external tool.

The values provided 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.