Tool Calling and Plugins for Stability Study

Data Characteristics in this Category

Stability study data primarily originates from storage experiments under various environmental conditions (temperature, humidity, light). It records changes over time in physicochemical properties, content, dissolution rate, and microbial limits. Data typically appears in structured tabular formats, including fields such as batch number, test date, storage conditions, test item, test result, unit, and deviation value. Some data may exist as scanned batch production records or analysis reports, requiring OCR recognition. Data update frequency depends on the stability study period, usually at 0, 1, 2, 3, 6, 9, 12, 18, 24, 36, 48, and 60 months. Document structures are often standardized batch reports or interim summary reports. Field names and units adhere to clear industry specifications; for example, content units are % or mg/g, dissolution rate is min or %, and pH has no unit.

Constraints Imposed by these Characteristics on Tool Calling and Plugins

The highly structured and time-series nature of stability study data requires tool calls to precisely locate and extract data for specific batches, time points, and metrics. For scanned reports, OCR tool accuracy is critical, and multi-page document field correlation must be handled. Querying and analyzing time-series data requires tools to handle date range filtering and trend analysis. For example, when evaluating the stability trend of a batch under specific storage conditions, the tool must call a database query interface, extract data for relevant time points, and potentially call a plotting plugin to generate trend charts. Data unit standardization requires tools to strictly follow predefined unit conversion rules during data processing and result display, preventing misjudgments due to inconsistent units. When data anomalies occur, tool calls should trigger warning mechanisms and provide traceability to original data sources.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`ollama_model_name`	`glm4-chat`	Balances Chinese processing capability with tool calling performance, reducing the risk of empty model stream responses.
`max_tokens`	`2048`	Ensures the model has sufficient space to process complex tabular data and analysis results in stability reports.
`tool_timeout_seconds`	`600`	Accounts for potentially long database query and OCR recognition times, preventing task interruption due to timeouts.
`ocr_engine_endpoint`	`http://[OCRService Address]:port/ocr`	Configured according to the actual deployed OCR service address, ensuring the accessibility of the image recognition tool.
`database_query_api`	`http://[DatabaseAPIAddress]/stability_data`	Specifies the stability data query interface for structured data extraction.
`image_format_whitelist`	`png, jpeg, pdf`	Compatible with common report scan formats, reducing 400 errors caused by image format incompatibility.

Three Common Pitfalls

When calling the image recognition tool, the model stream response is empty. Investigation reveals the OCR service returned an unexpected JSON structure. This typically occurs when the OCR service fails to return the expected result fields upon recognition failure or encountering an error, leading to model parsing failure.
When querying stability data, the number of returned data entries does not match expectations, or some critical fields are empty. This may be due to incorrect parameter mapping in the database query tool, such as passing an empty value for the batch_id field, or an inaccurate query time range.
When processing image-format stability reports, the tool call returns a 400 error, indicating an incorrect image format. This usually happens when an image, despite having a .png suffix, has internal encoding or a corrupted file header, preventing the OCR tool from parsing it correctly.

How to Verify Configuration

Using FastGPT's tool testing interface, verify whether the OCR tool accurately identifies and extracts key fields from a typical stability data report sample, then cross-reference the extracted fields with the original data.
For a known batch number and time point, query the database via a tool call. Check if the returned data is complete and consistent with expectations, especially for numerical data and units.
Upload a mixed document containing various image formats (e.g., PNG, JPEG, PDF). Verify that the image processing toolchain handles all formats without errors, ensuring no 400 errors occur due to format issues.
Simulate a query request with anomalous data. Observe whether the tool call triggers error handling or an alert mechanism as expected. Check log output for detailed error messages to facilitate troubleshooting.

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.