What the data for this category looks like
Environmental monitoring data comes from distributed IoT sensors, fixed monitoring stations, and mobile inspection collection devices. Update frequency ranges from seconds to hours, depending on the monitoring parameter type, with no fixed uniform cycle. The data structure consists of structured time-series data. Each record includes a unique monitoring point identifier, collection timestamp, and measured values for multiple environmental parameters. Common fields include monitor_point_id (point number, no unit), collect_time (ISO 8601 formatted time), pm25_concentration (fine particulate matter concentration, unit μg/m³), wind_speed (wind speed, unit m/s), temperature (ambient temperature, unit ℃). The number of fields per single record varies based on monitoring item configuration. This data is primarily used to generate environmentally related marketing content for the financial industry, such as green credit compliance reminders and corporate environmental rating promotional materials, to support customer acquisition needs.
What constraints do these characteristics impose during deployment and upgrade?
Distributed multi-source data requires deploying plugins that support multi-protocol access during the deployment phase. These plugins must adapt to communication protocols including MQTT and HTTP API for different devices, avoiding single-point access restrictions. High-frequency real-time updates require optimizing streaming data processing logic during the upgrade phase. Prioritize incremental embedding instead of fully regenerating vectors to reduce resource consumption. Structured multi-field design requires configuring precise field mapping rules during deployment. This ensures monitoring data is correctly associated with marketing content templates, preventing content generation errors caused by field mismatches. Variable parameter density per single data record requires adjusting batch parameters for recall and embedding during upgrades. This balances processing efficiency and content accuracy.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Raw collected log files imported for environmental monitoring scenarios are usually large in size, so this setting adapts to single-file size limits |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Structured monitoring data requires multi-field cleaning and mapping, so parsing takes longer than generic document types |
EMBEDDING_BATCH_SIZE | 32–64 | The embedding vector dimension of environmental monitoring data is fixed. This batch range balances memory usage and processing efficiency |
RECALL_TOP_N | Top 8–12 entries | The effective information density of single monitoring data is moderate. Excessive recall leads to redundant marketing content |
SIMILARITY_THRESHOLD | 0.75–0.85 | For customer acquisition marketing in the financial industry, low-correlation historical monitoring data must be filtered to avoid generating inaccurate marketing prompt content |
PLUGIN_REQUEST_TIMEOUT | 300 seconds | Sufficient interface response time must be reserved when calling external monitoring data interfaces to pull real-time data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: Timeouts or pull failures occur when running
docker pull fastgpt/fastgpt. Cause: Adapted plugin images required for environmental monitoring scenarios are larger than generic images, and the default pull timeout duration is insufficient. - Issue: An
internal server errorprompt appears in the interface when importing plugins associated with monitoring data during private deployment of version V4.14.1. Cause: ThePLUGIN_REQUEST_TIMEOUTparameter is not configured, or its value is set too small, causing plugin call timeouts. - Issue: The detailed disk usage composition of the knowledge base built with locally deployed FastGPT cannot be viewed. It is impossible to confirm whether it includes original files, split chunks and embedding vectors. Cause: FastGPT's storage statistics function is not enabled, or the monitoring data storage directory is not mapped correctly.
How to confirm proper configuration
- Run
docker logs fastgptto view container logs, and confirm there are no file over-limit error messages related toUPLOAD_FILE_MAX_SIZE. - Import a single structured monitoring data file, and check whether the field mapping in the parsing result is complete, with no null values or incorrect conversions.
- Trigger a plugin call, confirm that real-time monitoring data can be pulled normally and corresponding marketing content is generated, with no timeouts or parameter errors.
- View the FastGPT storage statistics panel, confirm that the usage of original monitoring files, split chunks and embedding vectors is correctly counted.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.