What This Category’s Data Looks Like
Daily documents for this category include duty logs, early warning notices, consultation minutes, rapid disaster reports, emergency response plans, and material allocation slips. Data sources include meteorological early warning systems, hydrological monitoring systems, emergency command platforms, grassroots disaster reporting systems, and video surveillance. Data updates once per day. Fields include rainfall and water level values, disaster risk levels, material types and quantities, transferred personnel lists, and more. Associated units include millimeters, cubic meters, number of people, tons, and others. Core content focuses on real-time disaster conditions and response actions.
Constraints for Multi-turn Dialogue and Prompt Configuration
This category’s data updates daily. Ensure dialogue context only retains valid information from the current day. Cross-day redundant data will interfere with real-time analysis. Document structures vary, and fields are specific to each document type. Prompts must clearly define call rules for different document types. Cross-department scheduling requires multi-turn dialogue to retain key parameters from every stage. Examples include rainfall monitoring points and material allocation quantities. Real-time response cycles are short. Limit the number of retained dialogue history turns. Excessive non-current disaster content will slow response speeds.
Configuration Settings
Refer to the following configuration guidelines for FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Context window size | 8000–12000 characters | Must accommodate complete content from multiple daily consultation minutes and rapid disaster reports, matching the information density of emergency response |
chatId association duration | 24 hours | Matches the daily update frequency of this category’s data, avoids cross-day redundant data interfering with current duty dialogue |
messages history truncation threshold | First 10 valid dialogue turns | Adapts to the short cycle of emergency response, filters out redundant early dialogue content unrelated to current disaster conditions |
| Prompt template | Must include "Respond based on rainfall, water level, and material allocation slip data" | Explicitly specifies calls to this category’s exclusive data fields, prevents generic responses |
variables parameter binding | Bind to daily meteorological early warning and hydrological monitoring data interfaces | Automatically introduces real-time updated category-specific data, reduces manual input errors |
| Attachment upload limit | Single file no larger than 50 MB, formats limited to PDF, JPEG, CSV | Matches the size and type of common attachments such as duty logs, disaster photos, and allocation slips |
Three Common Configuration Errors
- Issue: Prompt configuration does not take effect. Model responses do not meet the specific requirements of the duty scenario. Cause: The prompt is not bound to the corresponding
variablesparameter, or the prompt does not explicitly specify calls to category-specific data fields such as rainfall and water levels. - Issue: Dialogue history cannot be exported, or its storage location cannot be found. Cause: Persistent logging for
chatId-associated dialogue logs is not enabled, or the correct log storage path is not configured. - Issue: Attachments cannot be uploaded to continue dialogue, or the model cannot read uploaded attachment content. Cause: Attachment upload permission for the dialogue scenario is not enabled, or the attachment format exceeds the preset allowed range.
How to Verify Successful Configuration
- Initiate a test dialogue that includes rainfall data and material allocation information. Confirm that the model response automatically associates the corresponding category data and complies with preset prompt requirements.
- View the
chatId-associated dialogue list. Verify that today’s dialogue records are correctly stored and can be exported via the specified path or interface entry. - Upload a duty log PDF file. Confirm that the model can read the file content and reference its field information in subsequent dialogue.
- Call the image-text request interface, upload an image of a disaster site. Confirm that the interface returns model-generated response suggestions normally.
The values listed above are common starting points. They should be evaluated against the user’s own sample data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.