Data Characteristics in this Category
Stability study data primarily comes from sample analysis reports generated during drug development. These reports originate from laboratory analytical instruments or Contract Research Organizations (CROs). They are typically in PDF, Excel, or LIMS export formats. Data updates occur periodically, such as every 3, 6, or 12 months for long-term stability studies. Accelerated stability studies might have shorter cycles. The document structure is consistent, including batch information, time points, storage conditions, test items, test methods, raw data, and summarized results. Fields include drug name, batch number, production date, expiry date, test date, and environmental parameters like temperature, humidity, and light intensity. Quality attribute indicators include content, dissolution, related substances, pH, and moisture. Units encompass percentages, mg/tablet, minutes, and degrees Celsius.
Constraints Imposed by these Characteristics on Workflow Orchestration
The multi-source and heterogeneous nature of stability study data requires robust file parsing and structuring capabilities within the workflow. The workflow must effectively process tabular data from PDFs and Excels and identify data correlations across different batches and time points. Periodic updates necessitate support for cyclical triggers and incremental data processing, preventing redundant imports. Consistent document structures and fields, such as batch numbers, test items, and time points, mean the RAG (Retrieval Augmented Generation) component must precisely match these key entities during retrieval to ensure accurate context. Numerical ranges and units for quality attribute indicators are crucial for AI model data validation and anomaly detection, requiring standardization during data preprocessing. Detailed environmental parameter records demand the workflow establish clear links between these conditions and test results when building the knowledge graph, facilitating trend analysis and risk assessment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
File Type Whitelist | pdf, xlsx | Covers primary report formats for unified processing. |
Chunk size | 500-800 characters | Ensures sufficient context per segment while avoiding redundancy from excessive length. |
Recall count | Top 8 entries | Balances recall precision and computational overhead, covering multiple relevant time points and batch information. |
Similarity threshold | 0.75 | Guarantees high relevance of retrieved results to the query intent, reducing irrelevant information. |
Rerank result count | Top 3 entries | Further refines retrieval results, improving the accuracy of the final output. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides ample timeout duration, considering the parsing time for large PDF reports. |
Three Common Pitfalls
- AI chat component reports "Unhandled exception":
OPENAI_API_KEYorOPENAI_BASE_URLenvironment variables are incorrectly configured in a private deployment. - Workflow fails to correctly extract tabular data from Excel: The file parsing component does not correctly identify table regions or merged cells, causing data misalignment.
- Model answer lacks critical test indicators: The RAG component fails to match knowledge blocks containing specific test item names during retrieval, resulting in insufficient context.
How to Confirm Correct Configuration
- Upload a typical stability study report (PDF or Excel). Check if the file parsing component correctly extracts all batch, time point, and test item data.
- Construct a query with key entities (e.g., "batch XXX 3 months content"). Check if the RAG component retrieves knowledge blocks containing detailed test data for the corresponding time point.
- Use the AI chat component within the workflow. Ask about the stability trend for a specific batch. Observe if the model generates reasonable analysis or summaries based on the retrieved information.
- Simulate a data update scenario. Upload new stability data files. Check if the workflow incrementally processes and updates the knowledge base, and if historical data remains unaffected.
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.