Data Characteristics
Cold chain logistics clinical trial pre-screening data comes from temperature and humidity sensor logs, transport trajectory records, equipment calibration reports, supplier qualification certificates, and anomaly reports. This data combines structured formats (e.g., CSV, JSON for sensor data) and unstructured formats (e.g., PDF qualification documents, Word or image anomaly reports). Temperature and humidity logs update frequently, typically every 5–15 minutes. Equipment calibration reports and supplier qualifications update less frequently, perhaps annually or semi-annually. Data fields include timestamp, temperature (Celsius), humidity percentage, GPS coordinates, device ID, and batch number. Unstructured documents contain descriptive text, charts, and tables covering operating procedures, risk assessments, and compliance statements.
Constraints on Citation and Traceability
The high timeliness and mixed structure of cold chain logistics data impose specific requirements on citation and traceability. High-frequency temperature and humidity logs require the knowledge base to quickly index and recall data segments from specific time periods. Citations must precisely point to the exact timestamp in the original record. Charts and tables in unstructured documents need special handling during knowledge chunking to maintain semantic integrity and prevent critical information loss. For example, calibration parameters and validity periods in equipment calibration reports must be accurately identified and traced back to specific sections or page numbers in the original report. Compliance review necessitates precise citation content and verifiable original sources. The knowledge base must support joint retrieval and citation from multiple data sources to handle complex cross-source queries.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 400–600 characters | Balances the short time-series nature of temperature/humidity logs with the paragraph integrity of reports, preventing over-segmentation. |
Recall count (Recall Count) | top 8–12 items | Ensures sufficient context coverage for sensor time-series data and relevant compliance documents. |
Similarity threshold (Similarity Threshold) | 0.75–0.80 | Improves the relevance of recall results, filtering out temperature/humidity anomalies or irrelevant reports not closely related to the query. |
Rerank result count (Rerank Count) | top 5 items | Further optimizes sorting based on initial recall, prioritizing the most critical temperature/humidity data segments or compliance clauses. |
PARSE_FILE_TIMEOUT_SECONDS | 180 seconds | Accommodates parsing time for large PDF calibration reports and operating manuals, preventing parsing interruptions. |
maxContext | 16000 token | Ensures complete information from multiple temperature/humidity log segments and related qualification documents can be carried, supporting complex reasoning. |
Common Misconfigurations
- Inaccurate time ranges for cited temperature and humidity data in responses, often due to improper timestamp parsing or chunking granularity settings for raw log data.
- The model fails to extract critical validity period fields from supplier qualification reports, leading to missing citation information. This typically occurs when the PDF parsing module incorrectly identifies text within tables or images.
- In mixed retrieval scenarios, query results for transport routes include irrelevant equipment maintenance records. This happens when the
Similarity threshold(Similarity Threshold) is set too low, failing to filter effectively.
Configuration Verification
- For temperature and humidity anomaly queries within specific time periods, verify that the model's cited content precisely points to the start and end timestamps of the original sensor logs, and compare it against the raw data records.
- Randomly select multiple supplier qualification certificates or equipment calibration reports. Verify that the model accurately extracts and cites key compliance fields, such as calibration date, validity period, and certification number, and that these match the original document content.
- Execute complex queries containing both structured elements (e.g., specific batch numbers) and unstructured elements (e.g., "reason for temperature fluctuation in a certain cold storage box"). Check that the cited sources list includes both relevant sensor data segments and links to anomaly report documents, and that these links are accessible.
Note: The values provided are common starting points. Measure them 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.