What this category of data looks like
The data for this category primarily comes from enterprise OA systems, organizational structure libraries, internal policy documents, and user feedback. Daily produced documents include process configuration sheets, approval node tables, form field descriptions, and change records. Data updates are typically triggered by process changes, with a frequency of at least once per month. Document fields include approval node permissions, form association rules, effective time ranges, and operator information. There are no unified mandatory units, and some fields must match the enterprise’s internal organizational structure codes.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
In FastGPT v4.17.0, scattered data sources and the need to match internal enterprise organizational structures mean that multi-turn dialogue must associate organizational variables in variables to avoid cross-departmental permission conflicts. Document structures are fixed but updated frequently, so chatId must be used to bind the current process configuration session to ensure that conversation history is linked to the correct approval node configuration. A single session must handle multiple types of information such as approval rules and form fields, so prompts must limit the context scope to prevent interference from redundant data. Change records must be synchronized to the conversation history to ensure that subsequent sessions can trace configuration adjustment trails.
How to set configurations
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
chatId | Bind to the unique ID of the current process configuration sheet | Ensure multi-turn dialogue is linked to the correct process context and avoid session confusion |
| Context window length | 800–1200 characters | Match the average single-page length of process configuration documents and prevent information overflow |
variables | Only load organizational and form variables associated with the current process | Reduce redundant data and improve prompt accuracy |
| History retention duration | 30 days | Match the monthly update frequency of process changes to facilitate tracing of configuration adjustments |
| Maximum number of turns per single dialogue | First 5 turns | Limit the dialogue complexity of approval rule sorting and avoid logical confusion |
Three common mistakes
- Phenomenon: Specified reply content is not saved to the conversation history, and automatic replies are empty when the session is reopened. Cause:
chatIdis not bound to the session context, causing historical records to not be correctly associated with the current process configuration. - Phenomenon: Multi-turn dialogue workflow execution efficiency is lower than single-step debugging. Cause: The context scope of
messagesis not limited, and redundant historical configuration data is loaded. - Phenomenon: Format errors occur during voice input dialogue. Cause: The container environment is not configured with dependent tools correctly, and sufficient permissions to install components are not obtained.
How to confirm the configuration is complete
- Initiate a multi-turn dialogue for process configuration, check whether
chatIdmatches the unique ID of the current process configuration sheet. - Save the dialogue, close the session, reopen it, and verify whether the automatic reply content matches the historical records.
- Adjust the
variablesloading scope and confirm that only variables associated with the current process are loaded. - Test the voice input function to confirm that container environment dependent tools load normally.
The values provided 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 2026-10-19.