Data Characteristics in this Category
Stability study data primarily originates from laboratory test reports, environmental condition monitoring records, and batch production information during drug development. This data has a relatively low update frequency, typically collected and recorded per batch or at specific time points (e.g., 0, 3, 6, 9, 12, 18, 24, 36, 48, 60 months). Document structures are usually structured or semi-structured, containing detailed experimental protocols, testing methods, raw data, analysis results, and conclusions. Fields and units are highly specific, such as temperature (℃), humidity (%RH), light intensity (Lux), active ingredient content (%), degradation products (ppm), pH (unitless), solubility (mg/mL). Key metadata like batch number, production date, expiration date, and storage conditions often accompany these fields.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The low update frequency of stability study data means high-density polling is unnecessary for workflow trigger mechanisms. Event-driven triggers, based on batch completion or specific time-point report generation, are more suitable. Structured or semi-structured document characteristics require powerful document parsing capabilities within the workflow to accurately extract key fields. For example, the workflow must identify fields like active ingredient content and degradation products and their corresponding values from PDF analysis reports. The specificity of fields and units demands strict unit validation and conversion during data processing to prevent calculation errors due to inconsistent units. Additionally, a large volume of batch and time-point data requires workflow support for complex data aggregation and trend analysis to determine drug stability trends. Workflows also need to integrate external databases to verify storage conditions against ICH guidelines.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale for this Approach |
|---|---|---|
Trigger Mode (Trigger Method) | Event trigger (new batch report generation) | Stability data has low update frequency; event triggers are more efficient |
Document Parsing Model | Specially trained OCR model | Adapts to potential tables, charts, and special symbols in reports |
Data Extraction Fields | Batch Number, Production Date, Storage Conditions, Time Point, Test Item, Result Value, Unit | Ensures all key information is extracted for subsequent analysis and judgment |
Data Validation Rules (Data Validation Rules) | Regular expression matching and range validation for Result Value and Unit | Ensures data quality and prevents input errors from affecting analysis |
Recall count (Recall Count) | Top 5 entries (Top 5 entries) | Quickly locates recent stability data for relevant batches during pre-screening |
Similarity threshold (Similarity Threshold) | 0.85 | Ensures recalled data is highly relevant to the current drug query |
Three Common Mistakes
- After calling the workflow via an API, the running data in the conversation log is empty. This occurs because the workflow lacks an explicit
Return Resultnode, preventing external calls from retrieving intermediate processing data. - When an AI conversation is modified to use
variable reference, model parameters like temperature cannot be set. This is because invariable referencemode, model parameters are determined by the referenced variable and must be configured at the variable definition. - When a workflow calls a sub-workflow, the sub-workflow often does not execute completely. This usually happens because
code executionnodes orspecify replynodes within the sub-workflow are not correctly configured for output, causing the parent workflow to terminate prematurely because it does not receive the expected results.
How to Confirm Proper Configuration
- Upload a typical stability study report and observe if the workflow accurately parses key fields like
Batch Number,Production Date,Active Ingredient Content, and their corresponding values. - Perform a simulated query using a drug name and a specific time point as input. Check if the data recalled by the workflow is limited to the stability study records for that drug and verify if the
Recall count(Recall Count) matches expectations. - Examine the workflow's log output to confirm if
Data Validation Rules(Data Validation Rules) correctly identify and flag data that does not conform to preset ranges or unit formats, such asactive ingredient contentfalling outside the 90-110% range.
The values given 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.