What this category of data looks like
Data for this category comes primarily from SIEM, EDR, NDR, and threat intelligence sources, with update frequencies of real-time or daily. Daily associated documents cover alarm disposal sheets, duty logs, threat intelligence notifications, and daily/weekly reports. Each alarm disposal sheet includes fields such as alarm ID, associated assets, trigger time, and disposal status. Duty logs record alarm judgment results per shift. Threat intelligence notifications include the latest threat characteristics and disposal suggestions.
What constraints do these characteristics impose on multi-turn dialogue and prompt words
Data sources for this category are scattered, and update rhythms vary significantly. Multi-turn dialogue contexts must synchronize the latest alarm and intelligence data in real time, avoiding reliance on expired cached content. Daily document types are diverse, so prompt words must clearly distinguish usage scenarios for different documents such as alarm disposal sheets and duty logs. This ensures the data source called by the dialogue matches the current task. Documents like alarm disposal sheets include fixed fields, so prompt words must specify field extraction rules to avoid confusing field information across different documents. Real-time updated threat intelligence data requires variables in the dialogue to be pulled in real time; static cached content cannot be used.
How to set the configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind alarm ID as the unique identifier | Ensure correct association of multi-turn dialogue contexts for the same alarm, avoiding confusion across alarms |
messages | Retain the most recent 20 dialogue context entries | Cover the full alarm judgment process, while limiting context length to no more than 8000 tokens |
variables | Pull associated asset data for the current alarm in real time | Use real-time SIEM data sources to ensure asset information used in dialogue is up to date |
| Maximum context length | 8000 tokens | Match the total length of documents such as alarm disposal sheets and duty logs, avoiding exceeding model processing limits |
| Temperature parameter | 0.6–0.8 | Balance accuracy and flexibility for alarm judgment, meeting rigor requirements for security scenarios |
| Single dialogue document recall limit | Top 3 alarm disposal sheets | Focus on historical disposal records related to the current alarm, reducing interference from irrelevant information |
Three common mistakes
- Phenomenon: The data returned by the conversation record list interface has unclear association between AI replies and user questions, with field misalignment. Cause: The
chatIdwas not used to bind the unique alarm session identifier, leading to mixed dialogue data from different alarms. - Phenomenon: After modifying global variables in the same conversation, subsequent rounds cannot read updated values. Cause: The
variableswere not configured for real-time update mode, only initial static variable values were loaded. - Phenomenon: Independent content of three questions in the same conversation cannot be retrieved; only merged text is returned. Cause: The
messagesparameter was not correctly configured to retain single-turn dialogue context, with multiple rounds of content incorrectly spliced into a single entry.
How to confirm the configuration is correct
- Initiate a multi-turn dialogue for a simulated alarm, check if
chatIdmatches the alarm ID, and confirm the dialogue context only associates with the current alarm. - Call the conversation record list interface, verify that the returned
messagesarray shows correct order and matching content for each user question and AI reply. - Modify the global variable value for the test case, initiate subsequent dialogue, and confirm the model uses the updated variable content.
- Check the dialogue context length, confirm it stays below the configured maximum context threshold to avoid content truncation.
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.