This content is written based on FastGPT v4.17.0.
What this category of data looks like
Typical data for this category comes from ITSM systems, phone or chat logs, asset systems, and internal knowledge bases. Daily processed documents include service requests, incident tickets, processing records, and user satisfaction forms. Each ticket contains fields such as unique identifier, user information, problem description, processing status, dispatch records, and satisfaction feedback. Data is updated daily or in real time, covering full process information from ticket submission to closure.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Since data sources cover multiple heterogeneous systems, multi-turn dialogue must support cross-data source context linking. This ensures full retrieval of ticket information, asset data, and historical processing records. Use chatId to associate the current ticket's dialogue context. This ensures continuity of multi-turn dialogue. Data updates occur daily or in real time. Prompt engineering must configure logic for regular knowledge base synchronization. This avoids using outdated processing workflows or asset information. Tickets include multiple structured fields. Multi-turn dialogue prompts must explicitly specify the fields to extract. This prevents incomplete requirement documents from being generated. Ticket processing errors can disrupt employee work or cause SLA breaches. Multi-turn dialogue must configure timeout handling and exception feedback mechanisms. This ensures process traceability.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Context window length | 8000–10000 preceding characters | Adapt to the average length of ticket documents, avoid truncating key historical dialogue and ticket information in messages |
| Question optimization toggle | Enabled | Used to refine core issues in tickets, simplifying subsequent prompt input logic |
| Knowledge base association scope | Associate with IT service desk-specific knowledge bases | Match exclusive business documents for this category, avoid interference from irrelevant content |
| Multi-turn dialogue timeout duration | 60 seconds | Adapt to real-time response requirements for IT service desks, avoid impacting employee work |
| Variable binding | Bind ticket ID and user department fields | Ensure generated requirement documents accurately match the context of the corresponding ticket. Binding parameters can be passed via variables |
| Maximum recall count | Top 3 entries | Control the volume of returned knowledge base content, avoid exceeding context window limits |
Three common misconfigurations
- Calling the dialogue interface returns empty associated knowledge base content, making it impossible to retrieve standard IT service desk processing workflows. Cause: Failed to correctly pass knowledge base binding parameters in
variables, or failed to configure the association between dialogue and the exclusive knowledge base. - Parsing Word documents larger than 10M takes more than 120 seconds to process. Cause: Did not configure segmented parsing optimization logic for large files; loading the full document directly causes excessive memory usage.
- A timeout error is triggered when generating requirement documents, preventing completion of output. Cause: Did not set a reasonable multi-turn dialogue timeout duration, or set an overly large context window leading to excessive model inference time.
How to confirm proper configuration
- Initiate a simulated ticket dialogue, enter basic information for a test ticket, and check if the generated requirement document includes the specified structured fields.
- Upload a test large Word document, review parsing progress and completeness of returned results.
- Call the dialogue interface, check if the returned results include relevant business content from the associated knowledge base.
- Initiate consecutive multi-turn dialogues, check if the context correctly retains key information from historical tickets.
The values provided here are common starting points, and should be evaluated 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.