Tool Calling and Plugins for Stability Study Registration

Data Characteristics in this Domain

Stability study data primarily originates from analytical instruments. This includes raw spectrograms, chromatography data, physical and chemical indicator reports, microbial limit test reports, and time-series monitoring records (e.g., appearance, content, degradation products) from long-term stability observations.

Data exists in a mixed format:

Structured: Physical and chemical indicators in Excel or CSV files.
Unstructured: Analytical reports, experimental records, and image-based spectrograms in PDF format.

Data updates occur in batches at predefined time points (e.g., 0, 1, 3, 6, 9, 12, 18, 24, 36, 48, 60 months). Document structures typically follow guidelines like ICH Q1A(R2), including study protocols, raw data, analysis results, and statistical analysis reports.

Fields and units are highly specialized:

"Content" might be "% (w/w)" or "mg/mL".
"Degradation products" might be "peak area percentage".
"pH" is a unitless numerical value.

Key linking fields include batch number, storage conditions, and test time point.

Constraints Imposed by these Characteristics on Tool Calling and Plugins

The mixed structured and unstructured nature of stability study data requires robust multimodal data parsing capabilities for tool calling and plugins. Raw spectrograms and image-based experimental records need image recognition and OCR for key information extraction.

Periodic batch data updates demand efficient data synchronization and incremental processing to avoid redundant imports. Specialized fields, units, and adherence to specific document structures mean general text understanding models may struggle to accurately identify and extract key information. This necessitates custom entity recognition models or predefined schemas for data mapping. For example, the system must distinguish between initial content and content at a specific time point.

Associated fields like batch number and storage conditions are fundamental for data comparison and trend analysis. Tool calls must accurately transmit and match these fields for statistical analysis plugins to function correctly.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Ensures each segment contains sufficient context for stability trend analysis while preventing excessive length that reduces model processing efficiency.
`Recall count`	`Top 10 entries`	Stability study reports are highly interconnected. Multiple relevant data points aid comprehensive evaluation, such as the same indicator data at different time points.
`Similarity threshold`	`0.78–0.85`	Balances recall accuracy and coverage. Avoids omissions due to specialized terminology differences or the introduction of irrelevant information.
`Rerank result count`	`Top 5 entries`	Further refines recall results, improving relevance for the user and focusing on core data points.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Stability report files can be large, containing numerous charts and text, requiring longer parsing times.
`MAX_TOKENS`	`4000 Token`	Ensures the model can process multiple stability data points and related background information within a single query.

Common Pitfalls

Tool call fails with "missing required parameter batch_number". This occurs when the uploaded stability study report's batch number field is not correctly parsed.
Plugin execution returns an empty trend analysis chart. This might be due to inconsistent units for content data at different time points during data import, preventing the statistical plugin from performing effective aggregation.
The frontend calling api/v1/chat/completions cannot pass specific prompts via the messages field to guide the model to focus on particular degradation products. This happens when the current API design does not expose the prompt parameter for external calls, or its priority is lower than system-preset prompts.

Verification Steps

Upload a standard stability study report containing multiple batches and time points. Verify that the knowledge base correctly identifies and stores batch numbers, storage conditions, test time points, and various physical and chemical indicators.
Ask the AI via the chat interface: "What is the trend of content for batch 20230101 at 3 months?" Confirm that relevant data is accurately recalled and a preliminary trend analysis is generated by the plugin.
Upload files in different formats (e.g., PDF reports, Excel data sheets). Verify that all files are successfully parsed under the PARSE_FILE_TIMEOUT_SECONDS configuration, with no parsing timeout errors.
Simulate user questions to confirm the AI accurately cites data sources when answering stability study questions and can invoke external statistical analysis plugins when necessary.

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.