What this category of data looks like
This category of data originates from assessment tool outputs, interview records, system inventories, and security device logs. Four standardized documents are routinely generated: classification determination reports, gap analysis reports, rectification plans, and assessment reports. Data is updated per individual assessment project or annually. Fields include control item number, rectification status, evidence association path, and others. The layered structure corresponds to the classification determination, gap verification, rectification, and closed-loop assessment stages of cybersecurity level protection assessment. There is no unified global batch data format.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
The project-based data update requirement mandates that multi-turn dialogue binds to the unique identifier of a single project to avoid cross-project context confusion. The layered document structure means prompts need to be split by assessment stage, with each turn of dialogue focusing on a single stage task to reduce interference from irrelevant information. Multi-source data requires gradual integration via multi-turn dialogue: first pull the system inventory, then associate security logs, and gradually improve assessment materials. Fixed project data does not require frequent adjustments, so prompt templates can be fixed as project-specific configurations to improve dialogue interaction efficiency.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of a single assessment project | Ensure multi-turn dialogue only associates data from the current project, such as classification determination and rectification data, to avoid cross-project context confusion |
messages | Split conversation history by assessment stage, retaining only interaction records for the current stage per turn | Adapt to the layered structure of cybersecurity level protection assessment documents, reducing irrelevant context overhead |
variables | Bind project-specific static data such as system inventories and log paths | Avoid repeated entry of project-fixed information, improving dialogue interaction efficiency |
| Maximum dialogue turn limit | 10–15 rounds, calibrated via actual testing | Match the length of the sorting and verification process for a single assessment project, preventing redundant dialogue |
| Tool call timeout | 300–600 seconds | Adapt to the time required for integrating multi-source assessment data, avoiding timeouts due to large data volumes |
Three common misconfigurations
- A null result is returned after dialogue timeout, with no assessment-related content generated. Verifiable troubleshooting item: The tool call timeout configuration was not adjusted based on the time required to integrate assessment data.
- Assessment data from other projects appears in multi-turn dialogue, resulting in cross-project context confusion. Verifiable troubleshooting item: A dedicated
chatIdwas not bound to each assessment project. - The rectification evidence association chart generated after calling the chart tool is empty. Verifiable troubleshooting item: Project-specific system inventory and security log path parameters were not correctly bound in
variables.
How to confirm configurations are correctly set
- Check the App/Admin server environment variables for FastGPT v4.17.0, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured and not empty, and meet official deployment requirements. - Create a dialogue node for a single assessment project, bind a dedicated
chatId, initiate two rounds of dialogue, and verify that the context only retains interaction records for the current project, with no cross-project data included. - Trigger a tool call to pull assessment data, confirm that the timeout time falls within the preset range, and no timeout errors or null results are returned.
- Check the project-specific data bound in
variables, confirm that the dialogue node can normally read and use this data to generate assessment-related content.
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.