Tool Use and Plugins for Stability Study Quality

Data Characteristics

Stability study data originates from quality attribute test results of drugs or biological products under specific conditions (temperature, humidity, light) over time. This data is typically generated batch by batch. Update frequency depends on the sampling points designed in the study protocol, ranging from weekly to monthly. Document structures usually include study protocols, raw records, and analysis reports. Key fields include batch number, sample number, test item, test method, test result, unit, test date, expiration date, and storage conditions. Test results often involve numerical values (e.g., content, dissolution, pH) and qualitative observations (e.g., appearance, clarity). Units vary, such as percentage, μg/mL, count, and hours. Reports also include statistical data like trend charts and regression analyses.

Constraints Imposed by These Characteristics on Tool Use and Plugins

The batch-oriented and time-series nature of stability study data requires tool calls to support multi-dimensional query filtering, for example, by batch number, test item, and time range. The large volume of numerical data with diverse units demands that plugins accurately identify and perform unit conversions or comparisons to prevent calculation errors due to inconsistent units. Although the update frequency is relatively low, the historical data volume is substantial. Plugins need to efficiently extract specific field information from structured or semi-structured documents. Furthermore, judging qualitative results may require combining predefined rules or reference standards, necessitating the ability to pass context information during tool calls. The need for historical trend analysis means plugins might require integration with data visualization or statistical analysis functions to assist in generating compliance reports.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`2048`	Stability study reports often contain detailed test data and descriptions. Increasing the context length helps the model understand the full content of the report, preventing loss of critical information.
`Recall Count`	`10-15 entries`	Ensures that enough historical data points or reports from relevant batches are recalled for batch comparison or trend analysis, covering necessary comparison dimensions.
`Similarity Threshold`	`0.75-0.85`	Terminology and descriptions in stability study documents are relatively standardized. A higher similarity threshold allows for more precise matching of specific test items or batch information, reducing interference from irrelevant content.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Stability report files, especially PDFs with many charts and tables, can take a long time to parse. Increasing the timeout ensures large files are processed completely.
`ENABLE_RAG_TOOL`	Enabled	Stability studies rely on extensive historical data and standard texts. Enabling the RAG tool ensures the model can cite accurate test results and regulatory requirements when generating responses.
`tool_request_timeout`	`60 seconds`	External data sources (e.g., LIMS systems) may experience network latency or processing time. Appropriately extending the tool request timeout prevents requests from failing due to brief delays.

Common Pitfalls

Calling an external system returns a 401 Unauthorized or 403 Forbidden error. This often indicates incorrect API Key or Token configuration, or insufficient access permissions.
Numerical results returned by a plugin do not match expectations, for example, incorrect content units or decimal places. This usually occurs because the plugin did not correctly handle numerical type conversion or unit parsing internally.
Input fields for subsequent steps are empty after an external tool call in a workflow. This can happen if the data structure returned by the external tool does not match the expected output field names in the workflow, leading to data mapping failure.

Verification Steps

Use FastGPT's debugging interface to observe tool call request and response. Check if external API request parameters are correct and if the returned data structure matches expectations.
Select a typical stability study report and conduct multi-turn dialogue tests. Verify if the model can accurately extract batch numbers, test items, test results, and units from the report, and perform correct numerical comparisons.
Simulate a scenario requiring trend analysis. Check if the tool successfully calls an external statistical analysis plugin and returns results including trend judgments or anomaly alerts.

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.