Tool Calling and Plugins for Preclinical Safety Assessment

Preclinical Safety Assessment Data Characteristics

Preclinical safety assessment data originates from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) raw records, instrument analysis data, pathology reports, and study protocols and summaries. This data exists in both structured and unstructured formats. Structured data includes animal dosing, observation metrics (e.g., body weight, temperature, blood count, biochemical indicators), and pathology scores. This data is typically stored in databases or standardized Excel tables. Unstructured data includes experimental records, imaging data, pathological descriptions, and expert evaluations, existing as PDFs, Word documents, and images.

Data updates are continuous throughout a project. Interim reports are generated periodically, and final reports are compiled once a study concludes. Document structures strictly follow ICH guidelines and national regulatory requirements. Fields and units are highly standardized; for example, dose units are mg/kg, time units are h or day, and biological indicator units are clearly defined.

Constraints on Tool Calling and Plugins

The strict standardization and highly specialized nature of preclinical safety assessment documents impose specific requirements on tool calling and plugins.

First, data contains numerous technical terms and abbreviations. Models require the ability to accurately understand biomedical vocabulary and use tool calls to query specialized dictionaries or databases for definitions.

Second, the mix of structured and unstructured data requires tools to flexibly handle different data sources. Examples include extracting key numerical values from PDF reports or retrieving specific experimental batch information from databases.

Third, the detail and rigor of raw records demand precision in numerical values, units, and relationships during information extraction. Any deviation can affect assessment results. Therefore, tool calling must support multi-format file parsing, structured data querying, and integrate with biomedical knowledge graphs or ontologies to enhance understanding and reasoning. The high demand for data accuracy means plugins performing data validation or comparison tasks must operate with high confidence.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Preclinical safety assessment reports often contain numerous charts and raw data, leading to large file sizes.
`maxContext`	`8000`	Ensures coverage of critical information in a single experimental report, facilitating model context understanding.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large file parsing can be time-consuming; this provides sufficient time to prevent parsing interruptions.
`Chunk size` (Segment Length)	`1000–1500 characters` (characters)	Balances semantic completeness and retrieval efficiency, preventing information loss in long paragraphs.
`Recall count` (Retrieval Count)	`Top 10 entries` (top 10)	Guarantees sufficient contextual information, especially when complex logical judgments are involved.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Addresses the need for precise matching of specialized terms and numerical values; optimization requires a test dataset.

Common Pitfalls

401 Unauthorized errors occur when calling external APIs due to incorrect or expired API keys or authentication credentials.
Model returns empty or incorrectly formatted fields after tool execution. This happens when the API's returned data structure does not match the preset parsing rules, or the model fails to correctly interpret the tool's JSON response.
In advanced orchestrations, the model fails to select appropriate tools for subsequent tasks. This indicates insufficient model capability to link user intent with tool functionality, or the tool description is not clear or accurate enough.

Verification Steps

Upload typical preclinical safety assessment reports (PDF, Word). Verify successful parsing and knowledge base segmentation. Check that segments accurately retain key information and tabular data from the original text.
Design queries containing specialized terms and data lookups. Test if the model can accurately retrieve relevant information via tool calls. Validate that numerical values and units in the results match the original data.
Simulate complex scenarios, such as asking the model to determine the incidence of a specific toxic reaction based on report data. Observe if the model correctly selects and executes multiple tools (e.g., data query, calculator plugin) and provides logically coherent answers.
Check FastGPT backend logs. Confirm tool call requests and responses, focusing on status_code and response_body. Ensure external services respond normally and data formats meet expectations.

The values provided are common starting points. Measure them against specific 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.