Tool Calling and Plugins for Preclinical Safety Assessment

Data Characteristics in this Category

Preclinical safety assessment data originates primarily from drug safety evaluation reports, GLP (Good Laboratory Practice) laboratory data management systems, toxicology databases, and published literature. This data updates relatively infrequently, typically with advancements in drug development phases or the publication of new research. Document structures are a mix of structured reports and unstructured text, such as toxicology study reports, pathological descriptions, and clinical biochemical indicator analyses. Fields and units are highly specialized, including drug dosage (e.g., mg/kg), administration routes, animal species, observation indicators (e.g., ALT, AST, histopathological score), and their corresponding measurement units (e.g., U/L, ng/mL). The data also contains extensive descriptive text, such as qualitative descriptions of lesion severity and incidence.

Constraints Imposed by these Characteristics on Tool Calling and Plugins

The specialized and complex nature of preclinical safety assessment data places specific demands on tool calling and plugins. First, unstructured text in toxicology reports requires robust natural language processing capabilities for key information extraction, such as identifying toxic effects, target organs, and dose-response relationships. This necessitates plugins that support multimodal or advanced text analysis tools. Second, unit consistency and field mapping for structured data are critical. Inconsistent field names from different sources (e.g., liver enzymes vs. ALT) or unit discrepancies (ug/kg vs. mg/kg) require tool calling for standardization or conversion. Furthermore, the low data update frequency means that version control and traceability for historical data are more important, ensuring each pre-screening is based on a stable and reproducible dataset. SyntaxError messages often indicate data format mismatches or inconsistencies with the tool's expected input structure.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`32000 tokens`	Ensures that longer toxicology report texts can be fully loaded, facilitating context understanding and key information extraction.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the parsing time for large PDF or DOCX format safety evaluation reports, preventing file processing failures due to timeouts.
`Chunk size` (Chunk Size)	`800–1200 characters`	Balances textual semantic completeness and model processing efficiency, preventing long sentences from being truncated while reducing token consumption.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves the accuracy of recalling relevant toxicology literature or historical cases, reducing interference from irrelevant information.
`Rerank result count` (Reranked Return Count)	`Top 10 entries`	After recalling a large number of potentially relevant results, highlights the most relevant preclinical safety assessment data points through a reranking mechanism.
`tool_call_retries`	`3 retries`	Enhances the robustness of tool calls, addressing network fluctuations or occasional transient failures of external APIs.

Three Common Pitfalls

Tool call returns SyntaxError or Your model may not support tool_call: This usually occurs because the JSON format returned by the model does not conform to tool calling specifications, possibly containing extra text or formatting errors.
Plugin execution yields empty or inaccurate results: This happens when parameter field names passed to the plugin do not match the plugin's expectations, or when data units are not standardized, leading to calculation discrepancies.
Plugin references are lost after workflow export/import: This is due to the plugin not being registered in the target environment or path inconsistencies, preventing the system from locating the corresponding plugin definition.

How to Verify Configuration

Use FastGPT's debugging interface to observe the JSON input and API responses for each tool call request, confirming that data fields, units, and formats are as expected.
Execute pre-defined test cases covering common toxicology indicator extraction and safety assessment logic. Compare the output results with manually determined expected results and analyze any discrepancies.
Simulate preclinical safety assessment reports from different data sources and formats to verify the stability of file parsing, information extraction, and tool calling, ensuring no timeouts or parsing failures.
Check the workflow logs to confirm successful tool calls and that external API response status codes are 200 OK or other success indicators.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.