This document applies to FastGPT v4.17.0.
What the data for this category looks like
Core data for this category originates from CMDB, cloud resource inventories, automated discovery tools, and procurement ledgers. Update frequency is daily or weekly. Daily deliverables include configuration item lists, service component relationship topologies, change records, and asset inventory sheets. Document fields include configuration item unique identifiers, dependency relationships, change times, procurement batches, and additional relevant fields. There are no universal fixed units; matching must align with specific asset types. Core users include operations and maintenance asset administrators, SREs, and ITSM platform administrators. The most time-consuming daily tasks are configuration item collection, relationship verification, and post-change data updates. Data deviations directly affect fault localization and asset account-physical consistency.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Multi-source data sources require multi-turn dialogue to support cross-data source field matching. Prompts must explicitly specify exclusive fields such as configuration item IDs and dependency relationships. Daily or weekly update frequency requires dialogue contexts to sync the latest CMDB change records, and avoid expired data. The diversity of document structures requires prompts to adapt to inputs of different formats including configuration item lists and topology diagrams, and split long texts to fit model context limits. The most time-consuming collection and verification links require multi-turn dialogue to assist in extracting key configuration items, reducing manual repetitive operations. The serious consequences of data deviations require dialogue outputs to include verification logic, ensuring the accuracy of configuration items and dependency relationships.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Fixed session ID associated with the current CMDB change context | Maintains context consistency for multi-turn dialogue, binds historical records of asset changes |
messages | Multi-turn history split according to CMDB document structure, no single turn exceeds 800 characters | Adapts to the field length of CMDB documents, avoids model context overflow |
variables | Pass 2-3 variables including configuration item ID and change time range | Narrows dialogue retrieval scope, accurately matches core CMDB data fields |
| Single search limit | Top 6 matching results | Focuses on core configuration items and dependency relationships, avoids redundant information interfering with dialogue logic |
| Maximum context | 1200-1500 token | Covers core fields of CMDB inventories and topology diagrams, stays within model processing limits |
Three Common Mistakes
- Symptom: Workflow terminates halfway, only returns a single AI reply result. Cause: Persistent context for
chatIdis not configured, and multi-turn dialogue does not retain the intermediate configuration item status of CMDB changes. - Symptom: Dialogue times out with no response. Cause: Single-turn input length for
messagesis not limited, passing overly long CMDB topology documents causes model processing timeouts. - Symptom: Plugin triggers only once, and cannot continue multi-turn prompt interaction. Cause: Dynamic update configuration for
variablesis not enabled, and real-time CMDB change fields are not bound.
How to Verify Successful Configuration
- Pass a test configuration item ID, check if
variablescorrectly loads the corresponding CMDB data fields. - Simulate a multi-turn dialogue, confirm that
chatIdremains consistent, and that the context retains the previous round's configuration item verification results. - Submit a long text containing CMDB change records, verify that the splitting logic for
messagescomplies with the preset length limit. - Trigger a plugin call, confirm that multi-turn prompt requests can be initiated consecutively without single-trigger restrictions.
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.