Data Characteristics in this Category
Stability study data primarily comes from a series of test reports. These reports document the physical, chemical, content, and microbial limit properties of pharmaceuticals or medical devices. Data is collected after sampling at preset time points under various storage conditions (temperature, humidity, light). This data typically exists in structured or semi-structured document formats, such as batch production records, inspection reports, and stability study protocols and summary reports. The update frequency depends on the stability study cycle, ranging from several months to several years. Documents contain key fields like batch number, sample number, test item, test method, test result (value, unit, judgment standard), test date, and expiration date. Units are diverse and include concentration (mg/mL), pH value, percentage content, and microbial counts (CFU/g).
Constraints Imposed by these Characteristics on "Tool Calling and Plugins"
The long-term and multi-batch nature of stability study data requires tool calls to efficiently aggregate and compare test results from different time points and batches. Diverse units and data types demand robust data parsing and format conversion capabilities from plugins to ensure accurate numerical comparisons. Semi-structured report documents require more intelligent text extraction and structuring tools to identify key fields and prevent manual entry errors. Additionally, due to the long data update cycles, tool calls need to support continuous traceability and analysis of historical data. They also need to trigger corresponding comparison and warning mechanisms when data updates occur. Result judgments often rely on preset criteria, which requires tools to integrate rule engines or external standard libraries for automatic evaluation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Stability reports often contain continuous descriptions of test results. An appropriate length ensures contextual completeness. |
Recall count (Recall Count) | Top 8–12 entries (top 8–12 items) | Stability-related questions require more context to support judgments on trends or anomalies. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures that recalled regulation or SOP segments are highly relevant to the query, avoiding the introduction of irrelevant information. |
Max Concurrent Tool Calls | 3 | Stability study questions may involve queries across multiple batches or multiple test indicators. Moderate concurrency can improve response speed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Stability report documents can be large, requiring a longer file parsing timeout. |
Max Retries | 2 | External services or tools may occasionally experience transient failures. A retry mechanism can improve success rates. |
Common Pitfalls
- Tool call failure, with logs showing
The tool call is not supported: This typically occurs because the model's tool-calling capability is not correctly configured, or the tool function is not properly registered with the model's recognizable interfaces. - Stability trend analysis results are empty or inaccurate: This happens when the data extraction plugin fails to correctly parse time-series data from reports, leading to missing key values or timestamps.
- Querying stability data for a specific batch returns information from unrelated batches: This is due to an improper knowledge base segmentation strategy that fails to effectively distinguish independent data for different batches.
Verification of Configuration
- Query multi-batch stability reports to verify the model's ability to accurately identify and compare test result differences between batches at the same time point.
- Query the trend of specific test items over time. Cross-reference the model's output trend description with the raw data and check if it correctly identifies abnormal fluctuations.
- Upload new stability study summary reports and test if tool calling automatically triggers data parsing and updates relevant indicators. Subsequently, query to confirm if the new data has been incorporated into the knowledge base.
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.