Data Characteristics
Cold chain logistics quality documents include temperature and humidity monitoring records, transport vehicle and equipment calibration records, GSP/GMP compliance inspection reports, inbound and outbound quality inspection sheets, and anomaly handling reports. Data sources are diverse, comprising IoT sensor data, manually entered forms, and scanned third-party audit reports. Temperature and humidity data update frequently, typically at minute or hour intervals. Equipment calibration and compliance reports update monthly, quarterly, or annually. Document structures are primarily semi-structured, extensively using tables and fixed fields such as batch number, production date, expiration date, temperature range, and deviation value. Field units are strict; for example, temperature units are degrees Celsius (°C), humidity is percentage relative humidity (%RH), time is precise to the second, and drug batch numbers or serial numbers are involved.
Constraints Imposed by These Characteristics on Workflow Orchestration
High-frequency temperature and humidity data updates require workflows with real-time or near real-time data ingestion capabilities to detect and address potential temperature deviations promptly. This necessitates configuring a low trigger interval. Key fields like batch numbers and production dates are linked across different documents. Workflows must support multi-document cross-referencing and comparison based on these fields. Semi-structured document characteristics mean precise parsing of table content is required. Traditional text chunking methods may not capture internal table logic, requiring specific parsing strategies. Strict field units and value ranges demand rigorous data validation after data extraction. For example, temperature values must fall within a specific range, otherwise an anomaly handling process triggers. Furthermore, multi-turn Q&A is critical in inspection scenarios. Workflows must maintain context, ensuring the AI can understand and answer complex compliance questions across multiple interactions.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkOverlapRatio | 0.1 | Ensures no loss of boundary information in tables or critical paragraphs while avoiding excessive overlap and redundancy. |
maxContext | 2000 token | Balances multi-turn conversation context requirements and model processing capabilities, covering typical inspection scenario conversation lengths. |
temperatureThreshold | 100.0 | High quality requirements for recall results; lowering this value reduces the recall of irrelevant or low-relevance documents. |
recallTopK | 15 | Ensures coverage of all potentially relevant quality records, addressing complex queries. |
workflowTriggerInterval | 300 seconds | For high-frequency data like temperature and humidity logs, ensures anomalies are detected and processed promptly. |
fileParsingStrategy | table_aware_parser | Addresses the large amount of tabular data in cold chain quality documents, ensuring accurate parsing of table structure and content. |
Common Pitfalls
- During multi-turn Q&A, the AI fails to maintain context, making each question seem like the first. This occurs because
contextWindoworsession_idtransmission mechanisms are not correctly configured in the workflow. - After workflow execution, all AI conversation node outputs are displayed to the user indiscriminately, leading to information redundancy. This happens when the
output_visibilityparameter is not specifically configured for each AI conversation node in the workflow. - When processing temperature monitoring data, the workflow fails to correctly identify and validate units like "°C" or "%RH," leading to data parsing errors or validation failures. This occurs when the file parser is not adapted for specific units or unit validation logic is not added in subsequent nodes.
Validation Steps
- Upload a temperature and humidity record document containing complex tables. Verify its content is correctly chunked and indexed in the knowledge base, especially the relationship between table rows and columns.
- Simulate a compliance query spanning multiple batch numbers. Observe whether the workflow accurately recalls and integrates associated information from different documents and supports multi-turn conversations.
- Intentionally input monitoring data with temperature values outside the preset range. Check if the workflow correctly triggers the anomaly handling process and generates corresponding alert messages.
The values provided are common starting points and should be measured against specific 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.