Data Characteristics for this Category
Stability study data primarily originates from long-term monitoring. It focuses on changes in various physical and chemical indicators of batch products over time under different storage conditions. Data sources include laboratory analysis reports, environmental monitoring system records, and production batch information. Data update frequency is typically low, with sampling and testing occurring every 3 months, 6 months, or 1 year. Document structure is organized by batch-sample-time point-test item. Fields include batch number, production date, expiration date, storage conditions (temperature, humidity, light), sampling time point, test item name, test method, test result value, unit, judgment criteria, and deviation analysis. Some data may exist as scanned paper reports or unstructured text.
Constraints Imposed by these Characteristics on "Deployment and Upgrade"
The low update frequency of stability study data means model training or knowledge base updates do not require high-frequency triggers. However, the large volume of historical data necessitates consideration of storage capacity and retrieval efficiency. The data contains numerous numerical indicators and units, requiring text processing components to identify units and compare numerical values. This prevents misjudgments due to inconsistent units. The prevalence of unstructured reports demands higher capabilities in document parsing and information extraction, requiring dedicated pre-processing workflows. Furthermore, time-dimensional information such as batch and expiration dates in long-term stability data directly impacts the accuracy and robustness of time series analysis components. Deployment must reserve sufficient storage space and optimize knowledge base indexing strategies to handle massive historical data.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Stability reports often contain high-resolution images or scanned documents, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDFs or scanned documents can take a long time. |
Chunk size | 800–1200 characters | Ensures each knowledge segment contains sufficient context to understand batch, condition, and result associations. |
Recall count | Top 10 entries | Stability queries typically require comparing results from multiple batches or time points. |
Similarity threshold | Calibrate based on actual measurements | Ensures retrieval of data for similar test items across different batches. |
reRankTopN | Top 5 entries | Further filters the most relevant batch and time point data. |
Three Common Pitfalls
- Workflow code execution component errors, even for simple code: This typically indicates missing runtime dependencies or improper permission configurations in the deployment environment. Examples include incorrect Python interpreter paths or overly strict sandbox environment restrictions.
- File processing becomes unresponsive or fails after uploading large stability reports: This may be due to
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSbeing set too low, causing file upload or parsing timeouts. - Numerical unit confusion or incorrect comparisons in query results for stability data: This occurs when the text processing component fails to correctly identify and standardize unit information in reports, or when unit conversion rules are not configured.
How to Confirm Proper Configuration
- Upload a typical stability study report containing data from multiple batches and time points. Confirm that all key fields (e.g., batch number, test item, value, unit) are correctly extracted and stored.
- Execute queries involving time dimensions and numerical comparisons, such as "query the change in indicator Z for batch X at 6 and 12 months under temperature Y." Verify the temporal logic and numerical accuracy of the query results.
- Simulate concurrent upload and query operations. Monitor system resource utilization (CPU, memory, storage) to ensure it remains within expected ranges, confirming system stability.
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.