What the data for this category looks like
Data for this category originates from server configurations, middleware configurations, security device policies, and output results of baseline tools. Update frequency is monthly or quarterly. Daily associated documents include baseline checklists, configuration verification records, rectification suggestions, and exception approval forms. Document fields cover configuration item number, current configuration value, compliance status, rectification measures, and approval status. No additional custom units are used. The data structure is built around configuration compliance verification, supporting daily work for security operations, internal control audit, and similar scenarios.
What constraints these characteristics impose on the multi-turn dialogue and prompts workflow
Decentralized data sources that rely on multiple system configurations require multi-turn dialogue to link configuration item information from different data sources, preventing context breaks. The low update frequency of source data means prompts must consistently match the fixed fields of baseline checklists, reducing frequent calls to dynamic data. The fixed document structure requires prompts to clearly define the output format for fields such as configuration item number and compliance status. For the most time-consuming verification and review steps, prompts must include preset guidance logic for configuration item checks. Associated queries for exception approval forms must support multi-turn follow-up questions. Given the severe consequences of errors, the dialogue process must perform real-time verification of configuration item compliance status to avoid missing weakly configured items.
How to configure the settings
The following configurations are formulated for FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the baseline inspection task | Used to link the baseline checklist and configuration verification records of the corresponding task, ensuring that the dialogue context is bound to a specific inspection task |
messages | Historical dialogue + current question sorted according to the baseline inspection workflow | Matches multi-type configuration item information from decentralized data sources, preventing dialogue context breaks |
variables | Preset configuration item number and compliance status fields | Consistently match the fixed fields of baseline checklists, reducing frequent calls to dynamic data |
| Maximum context length | 8000–12000 characters | Adapts to the total length of multiple documents including baseline checklists and rectification suggestions, ensuring complete context transmission |
| Timeout threshold | 600 seconds | Adapts to the processing duration of time-consuming steps such as configuration item verification and exception reason confirmation, preventing mid-run interruptions |
Three common misconfigurations
- Phenomenon: After calling the dialogue interface, the operation to stop the current dialogue cannot be triggered, and an error prompt returns that the corresponding termination interface cannot be found. Cause: The
chatIdparameter is not correctly bound, causing the system to fail to locate the specified dialogue session. - Phenomenon: Results from multiple AI dialogue nodes in the workflow are all displayed, instead of only retaining the last output. Cause: The context truncation logic is not enabled in the workflow configuration, or the output fields for only retaining the last round of dialogue are not specified via
variables. - Phenomenon: When accessing Feishu or WeChat channels, the dialogue content contains a large number of #* and similar punctuation marks, and the format cannot be displayed normally. Cause: No format filtering rules are configured in the prompt, and no format adaptation parameters for the channel are passed via
variables, causing the original Markdown format to not be converted.
How to confirm that the configuration is complete
- Initiate a test dialogue containing baseline inspection configuration items, and verify whether the dialogue context is linked to the baseline checklist and configuration verification records of the specified task.
- Trigger the workflow run, and confirm that only the output content of the last AI dialogue node is displayed, with intermediate results from other nodes not exposed externally.
- Initiate a test dialogue when accessing Feishu or WeChat channels, and verify that the output content does not contain redundant #* and similar punctuation marks, and the format meets channel requirements.
- Initiate a time-consuming configuration item verification dialogue, and confirm that the dialogue does not interrupt unexpectedly, matching the preset timeout threshold setting.
The values provided here are common starting points and should be evaluated against the user’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.