Tool Calling and Plugins for IVD Diagnostic Reagent

Data Characteristics for this Category

IVD diagnostic reagent registration documents draw from diverse data sources. These include internal R&D reports, clinical trial data, manufacturing process files, and quality control records. External sources include regulatory standards, guidelines, and approved product databases. Data update frequency varies by stage. Clinical trial data, for example, generates continuously during trials, while regulatory standards may be revised annually or published irregularly. Document structure is highly standardized, adhering to the National Medical Products Administration (NMPA) requirements for registration documents. This typically includes modules like technical requirements, instructions for use, label drafts, manufacturing processes, and quality control. Fields and units are strict. For instance, Limit of Detection (LOD) is often expressed in ng/mL or IU/mL, Accuracy in percentages, and Batch No. as an alphanumeric combination.

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

The standardized structure and strict field requirements of IVD diagnostic reagent registration documents necessitate precise data extraction and format conversion capabilities for tool calling. Diverse data sources and varying update frequencies require plugins to flexibly connect to multiple data sources and handle data versions with different timestamps. For example, generating technical requirements involves extracting specific parameters from multiple internal documents and populating them into a pre-set template. This requires the tool to handle various file formats like PDF, Word, and Excel. The units and numerical precision of clinical trial data demand high-quality data validation from plugins to avoid compliance issues due to unit mismatches or incorrect numerical formats. Regulatory updates may require localized adjustments to existing documents. This means tool calling must identify and apply the latest regulatory changes, quickly locating and modifying affected fields.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`maxContext`	`8000 tokens`	Accommodates the length of a single regulatory document or clinical report, ensuring completeness.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Most IVD document files are large and require sufficient time for content parsing.
`Recall count`	`Top 10 entries`	Ensures retrieval of sufficient relevant regulations or technical references from the knowledge base.
`Similarity threshold`	`0.75`	Ensures high relevance of retrieved content to the IVD registration and declaration scenario.
`Chunk size`	`500 characters`	Balances text semantic integrity with model processing efficiency, reducing truncation risk.
`Tool Call Timeout`	`60 seconds`	External database queries or complex calculations can be time-consuming; avoids premature interruption.

Three Common Mistakes

After calling a database plugin, specific fields in the returned tool output are empty. This may be due to a mismatch between the field names in the database query statement and the actual database table structure, or insufficient permission configuration to access the target fields.
A database connection plugin is configured, but a connection failure error occurs during a conversation. This may be due to an incorrect IP address or port number in the database connection string, or a firewall restricting the FastGPT instance's access to the database.
When using a batch execution node to loop and call a large model to process a large number of documents, the API call method fails to process all documents, while online debugging succeeds. This may be because API calls in long-running sessions encounter session state or resource limitations, preventing some tasks from being properly submitted or executed. Check API call concurrency limits or retry mechanisms.

How to Confirm Correct Configuration

Execute a simple query containing SELECT COUNT(*) via an external database plugin. This confirms database connectivity and data access permissions.
Use a conversation simulating an IVD registration document generation scenario. Observe if the tool call's input and output meet expectations, especially for the accurate extraction of key parameters like LOD and batch number.
Configure a file parsing plugin. Upload a typical IVD product instruction PDF file. Check if the parsed text content is complete and free of garbled characters. Verify the extraction effect of specific sections (e.g., 【Inspection Methods】).
Execute a workflow with a batch processing step. Observe the status codes and time consumption of each tool call in the logs. Ensure all steps complete successfully without unexpected error responses.

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