Data Characteristics in This Category
Core data for cold chain logistics products typically originates from Supply Chain Management (SCM) systems, Warehouse Management Systems (WMS), and Internet of Things (IoT) devices. Data updates are frequent, especially for environmental parameters like temperature and humidity, which can update every second or minute. Document structures are often structured data, including product batch numbers, production dates, shelf life, and storage conditions (temperature ranges, humidity requirements). Unstructured data, such as transport logs and anomaly reports, is also present. Fields and units have industry-specific characteristics. For example, temperature units are typically Celsius (℃), humidity is percentage (%RH), timestamps are precise to milliseconds, and geographical coordinates are included. Product identifiers often use batch numbers and serial numbers, linked to manufacturer and supplier information.
Constraints Imposed by These Characteristics on "Forms and Interactions"
High-frequency environmental parameter updates require form designs that can quickly respond to data changes, particularly in anomaly monitoring scenarios. Structured data predominates, leading forms to focus on precise data input and filtering, reducing the proportion of free-text input. The large volume of batch and serial number information requires forms to provide efficient retrieval and association functions, such as automatic population via scanning devices. Fields with units, like temperature and humidity, need clear unit prompts and validation to prevent input errors. Geographical location information may require integrating map components for visual display or range queries. For unstructured transport logs, forms need to provide file upload or rich text editing areas, with capabilities for keyword extraction or summarization.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 Tokens | Ensures sufficient capacity for detailed product information, historical transport records, and environmental parameters for comprehensive analysis. |
Chunk size (Chunk Size) | 500 characters (characters) | Accommodates the relatively short descriptive texts and structured data fragments found in cold chain product documentation. |
Recall count (Recall Count) | Top 10 entries (top 10) | Provides enough relevant information for users to choose from when querying multiple batches and product models. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures query results are highly relevant to user intent, filtering out irrelevant product or reagent information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Allows the system to process larger product manuals or batch data files, preventing parsing timeouts. |
History Message Limit | 8 entries (messages) | Retains sufficient conversational context to support multi-turn follow-up questions and condition refinement. |
Three Common Mistakes
- Forms submit without a response for a long time or return a
504 Gateway Timeouterror. This occurs when backend services do not have a sufficiently long timeout configured for processing large amounts of associated data or complex calculations. - The system returns empty or inaccurate results when a user inputs a temperature range. This happens because field validation does not strictly limit units or numerical formats, leading to a mismatch between query parameters and actual stored data.
- In workflow orchestration, the
code executionmodule fails validation if its input includes historical records. This is due to thecode executionmodule having strict requirements for input parameter types and structures, and the unstructured or dynamically changing nature of historical records conflicts with preset validation rules.
How to Confirm Correct Configuration
- Submit product query forms containing different temperature units (e.g., Celsius, Fahrenheit). Check if the system correctly converts or prompts for unit mismatches.
- Upload a product list file containing multiple batch and serial numbers. Observe if the system can complete parsing within the specified time and correctly extract key fields.
- Simulate an abnormal temperature alarm scenario. Submit relevant data via a form and confirm if the system triggers the predefined alert process and displays the correct handling suggestions.
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.