What this category of data looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on the workflow orchestration link
Multi-source heterogeneous data sources require workflows to support adaptation to multiple input formats, and unified input parsing rules must be configured. The segmented update feature requires workflows to support incremental processing logic, to avoid repeated processing of already parsed meeting content. The clear field structure requires targeted information extraction nodes to be set up within the workflow, to ensure core fields such as meeting identifiers and action items are accurately identified. Differences in update cadence across scenarios require workflows to flexibly switch between single-trigger and scheduled-trigger modes, to adapt to different meeting minutes generation requirements.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Trigger Mode | Triggered when meeting file upload is completed | Matches meeting minutes generation scenarios, avoids unnecessary execution |
| Context Window | First 1000–1500 token | Covers complete semantics of a single segment of meeting speech, adapts to long text content |
variables | Bind meeting topic and action item fields | Used for subsequent field verification and classification |
nodeId | Associate custom format verification node | Used to standardize the output format of meeting minutes |
| Retry Count | 3 times | Addresses temporary exceptions in large model calls |
| Single Processing Limit | 5 meeting minutes | Avoids excessive load in a single batch execution |
Three common mistakes
- Workflow fails to complete cyclic generation after correct or incorrect judgment, and only runs one AI conversation. The cause is failure to associate the verification result of
variablesvia a conditional branch node, and failure to configure jump rules for cyclic triggering. - After configuring the context window, the conversation details only display a small amount of context content, and the reply content cannot be associated with historical conversations. The cause is failure to correctly bind the historical conversation parameter in
variables, or the value range of the context window is set too small to cover the required context scope. - When a meeting file is uploaded directly, the large model cannot understand the original file content, and an additional parsing step is required. The cause is failure to configure a file parsing node to convert the original file into a text format recognizable by the large model, and failure to pass the parsed content via workflow nodes.
How to confirm the configuration is correctly set
- Upload a test meeting minutes file, check if the workflow triggers automatically, and confirm the trigger logic is effective.
- View the conversation details panel, verify that the number of displayed context entries matches the configured context window value range.
- Check if the meeting topic and action item fields bound to
variablesare correctly called in subsequent nodes, and confirm there are no abnormalities in field transmission. - Run a test workflow containing multiple code components, check if the execution logs of each component are output normally, and confirm there are no conflicts in data flow between nodes.
The values provided here 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 2026-09-28.