Data Characteristics
Cold chain logistics clinical trial pre-screening data primarily originates from IoT sensors, Logistics Management Systems (LMS), and drug/sample batch information. Sensor data includes environmental parameters like temperature, humidity, light, and vibration, typically recorded as time series every few seconds to minutes. LMS data contains structured information such as transport routes, vehicle details, driver status, and estimated arrival times, with update frequency dependent on logistics node changes. Metadata for drug or sample batches, including production dates, expiry dates, and storage requirements, is generally static but verified and updated upon each inbound or outbound event. Data structures are predominantly semi-structured, such as CSV files or JSON formatted sensor logs, and relational database logistics records. Field units include degrees Celsius (℃), relative humidity (%RH), lux (Lux), and gravitational acceleration (g), with high requirements for data precision and real-time availability.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The real-time requirements of cold chain logistics data directly impact FastGPT's data ingestion and processing capabilities. Large volumes of sensor data streams demand efficient real-time parsing and vectorization. This means maxContext and PARSE_FILE_TIMEOUT_SECONDS parameters require adjustment based on data stream peaks to prevent data backlog and processing delays. The diversity of semi-structured data sources necessitates FastGPT's flexibility in file parsing and content extraction, requiring specific parsing rules configured for different data sources. Furthermore, the vast volume of historical data requires consideration of efficient index rebuilding or data migration during upgrades to avoid extended downtime. Strict requirements for data precision and units mean special attention to the representation of numerical fields during vectorization, ensuring numerical differences are accurately reflected in the vector space and preventing data misinterpretation due to unit mismatches.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the size of individual batch logistics reports or historical sensor log files, ensuring complete uploads. |
maxContext | 3000 Tokens | Adapts to the context length of time-series data in sensor logs and logistics events, ensuring information completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 180 | Provides sufficient time to process large CSV or JSON formatted sensor data files, preventing parsing timeouts. |
Chunk size (Chunk Size) | 500 characters (characters) | Balances text block granularity with contextual information, suitable for logistics event descriptions and environmental parameter change analysis. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures coverage of sufficient relevant logistics or environmental anomaly records during pre-screening queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances accuracy and recall, identifying highly relevant logistics statuses or anomaly events related to the query intent. |
Three Common Mistakes
- Slow query responses or timeout errors after deployment. This often results from
maxContextbeing set too low, leading to significant data truncation, orPARSE_FILE_TIMEOUT_SECONDSbeing insufficient, causing file processing failures and subsequently impacting queries. - Inability to correctly parse some uploaded logistics log file content, appearing empty or missing critical fields. The cause is often a failure to configure appropriate parsing rules for specific logistics log formats (e.g., containing non-standard delimiters or multi-level nested JSON), preventing FastGPT from extracting valid information.
- Inaccurate query results for some historical data or invalid
laffunction call credentials after a FastGPT version upgrade. This may be due to changes in vector models or index structures between old and new versions, requiring re-indexing or updating credential configurations inlaf.runto ensure compatibility.
How to Confirm Correct Configuration
- Upload a typical cold chain logistics sensor log file (e.g.,
temperature_log_20231026.csv). Verify that the parsed chunks contain complete temperature, humidity readings, and timestamps, and that the AI Agent accurately identifies them. - Execute a query involving complex logistics event descriptions, such as "Find all vaccine transport batches with temperatures exceeding 8 degrees Celsius last week." Verify that the recall results accurately correspond to actual data and that the
Recall count(Recall Count) meets expectations. - Simulate a cold chain anomaly (e.g., a sudden temperature increase). Import relevant anomaly data into FastGPT and query the anomaly using natural language. Confirm that the AI Agent correctly identifies and locates the anomalous batch and timestamp, validating the effectiveness of the
Similarity threshold(Similarity Threshold). - After a FastGPT upgrade, randomly select a batch of data processed by the old version and re-query it. Compare the consistency of query results between the old and new versions to ensure correct data migration and index rebuilding.
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.