What the data for this use case looks like
Environmental monitoring research reports are core reference data sources for environmental-themed investment and environmental risk assessment in the financial sector. Data sources primarily include publicly available monitoring station data from ecological environment authorities at all levels, official submission documents from third-party environmental monitoring institutions, and original record files generated from on-site sampling. Update cycles are divided by monitoring frequency, including real-time hourly data, daily summary data, and monthly and annual analysis reports. Document structures typically include fields such as monitoring site number, pollutant name, measured concentration, sampling time, and quality control qualification mark. Units cover specific types such as mg/m³, μg/m³, decibels, and milligrams per liter. Some documents are structured table formats, while others are analysis reports with charts.
Constraints on workflow orchestration
Since research report retrieval in financial scenarios must balance timeliness and accuracy, and monitoring data involves batch fields across multiple sites and pollutants, workflows must support filtering by site number and pollutant type to avoid returning irrelevant data. Since real-time and daily data update frequencies are high, workflows must include scheduled data source pull nodes to ensure retrieval results use the latest available data. Since quality control identification fields are included, workflows must add pre-verification steps to filter unqualified monitoring data and avoid impacting financial analysis conclusions. Since document formats include structured tables and analysis reports, workflows must support multi-format parsing configurations to avoid losing field association information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Environmental monitoring research reports may contain long tables across multiple sites, leading to extended parsing times |
Segment Length | 800–1200 characters | Monitoring data has numerous fields. Excessively long segments cause context confusion, while excessively short segments lose site association information |
Recall Count | Top 8–12 entries | A single monitoring research report typically covers no more than 10 sites. Too many recalled entries introduce irrelevant data |
Similarity Threshold | 0.75–0.85 | High precision is required for matching monitoring data fields to avoid incorrectly recalling reports from non-target sites |
UPLOAD_FILE_MAX_SIZE | 200 MB | Batch-uploaded monitoring datasets may include multiple monthly reports, resulting in large single-file sizes |
WORKFLOW_TRIGGER_MODE | Scheduled trigger + Manual trigger | Monitoring data requires regular updates, while temporary queries of historical reports are also supported |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test with your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: After triggering a workflow, the conversation log panel for yesterday and today shows no content. Cause: The workflow does not have the "save conversation context" configuration enabled, or only saves context for the current session and does not cover the historical session scope.
- Symptom: When attempting to read an uploaded monitoring file via a variable in the workflow, a file not found prompt is displayed. Cause: The output variable of the upload component is not correctly bound to the input parameter of the file reading node, or the variable scope is set to only visible to the current node.
- Symptom: In a Docker-deployed FastGPT, model testing returns normal results and background model response logs exist, but the workflow conversation interface shows a failure. Cause: Local file paths are configured between workflow nodes, and the mount directory inside the Docker container is not adapted, or container network policies restrict communication between workflow nodes.
How to confirm the configuration is correct
- Trigger a manual workflow run, check that the parsed text includes pollutant concentration data for the target monitoring site, and verify that the fields match the original document.
- Adjust the
similarity thresholdvalue, verify that the number of recall results meets expectations, and confirm that no monitoring data from irrelevant sites is included. - Upload a small test monitoring dataset, check that the workflow can correctly read and process the file with no timeout or parsing failure prompts.
- Configure a scheduled trigger task, wait for the preset cycle, and check that the workflow automatically pulls the latest monitoring data with no abnormal errors.
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.