Data Characteristics
Cold chain logistics data for clinical trial pre-screening originates from several systems: temperature and humidity monitoring, GPS tracking, cargo status sensors, and transportation contracts with compliance documents. Temperature and humidity data updates every 5–15 minutes, recording timestamps, temperature values (Celsius, one decimal place), and humidity values (percentage). GPS data updates in real-time or every 1–2 minutes, including latitude, longitude, and speed. Cargo status data may include binary or enumerated values for tilt, vibration, or light. Transportation contracts, typically PDF, have a relatively fixed field structure, covering item names, batch numbers, storage requirements, transport routes, and liability. Compliance documents are largely unstructured text detailing regional regulatory requirements.
Constraints on Multi-Turn Conversations and Prompts
The heterogeneous nature of cold chain logistics data requires multi-turn conversation and prompt design to account for data integration and context management. High-frequency updates from temperature, humidity, and GPS data necessitate the system's ability to process time-series information. Conversations must quickly locate data for specific timeframes or areas. Specialized terminology and regulatory clauses in documents demand advanced semantic understanding from prompts. The RAG retrieval model must accurately match relevant passages.
Clinical trial pre-screening requires traceability. Model responses must cite specific data sources and document clauses to ensure accuracy and compliance. In multi-turn conversations, users may progressively refine queries, for example, from "temperature anomaly for a specific batch" to "exact latitude and longitude at the time of the anomaly." Prompts must support this incremental information extraction.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Balances long text comprehension with computational cost, suitable for cold chain logs and document snippets. |
Recall count (Recall Count) | Top 7 | Ensures coverage of multi-source data while avoiding irrelevant information. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves recall precision, reducing misjudgments due to specialized terms or numerical differences. |
Rerank result count (Rerank Return Count) | Top 3 | Focuses on the most relevant information, enhancing multi-turn conversation fluency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large transportation contracts or compliance documents, preventing timeouts. |
Chunk size (Segment Length) | 400 characters | Optimizes long document segmentation, ensuring each segment contains sufficient context for retrieval. |
Common Pitfalls
- Knowledge base content is imported, but the model reports it as empty during conversation: This typically results from incomplete knowledge base index reconstruction or synchronization delays. Check the knowledge base status and index update times.
- Model fails to understand queries for specific temperature/humidity ranges or time periods: Prompt design did not sufficiently guide the model to identify numerical and temporal entities, leading to incorrect query condition extraction.
- Image URLs returned by the conversation interface are inaccessible: The image URL may not be correctly encoded, or the image server might have access restrictions. Ensure the URL is resolvable in the model environment and the image resource is publicly accessible.
Validation Steps
- Conduct multi-turn conversation tests for simulated anomaly scenarios across different batches and transport routes. Verify the model's ability to correctly identify and cite relevant temperature, humidity, and GPS data.
- Prepare queries containing specialized terminology and regulatory clauses. Test the model's ability to accurately retrieve and integrate information from transportation contracts and compliance documents within multi-turn conversations.
- Examine the accuracy of data source and document clause citations in each conversation response. Ensure consistency with actual data and document content, and that specific sources can be located.
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.