What Data for This Category Looks Like
The data for this category comes from ticketing systems, chat logs, product logs, and customer contract SLAs. Update frequency is daily or real-time. Daily maintained documents include customer tickets, issue categorization tables, processing records, and service reports. Data fields include ticket unique identifier, customer associated information, problem description text, categorization tags, response time limit requirements, and assignment team information. All fields are generated around customer feedback and issue disposal processes, with no additional custom unit fields.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Ticket data consists mainly of text descriptions and updates in real time. Multi-turn dialogue must retain session context and associated ticket information to avoid repeating questions about content already provided by customers. Issue categorization relies on standardized categorization tables. Prompts must consistently reference the fixed format of categorization tags to ensure output results can directly match categorization rules. Ticket data includes SLA time limit requirements. Multi-turn dialogue must embed time limit verification logic in the response link to prevent overdue breach triggers. Data sources are dispersed. Historical processing records must be automatically pulled during dialogue initialization to reduce manual entry costs.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current ticket | Associate historical sessions with corresponding ticket data, ensuring consistency between context and business subject |
messages | Retain the first 15 rounds of dialogue and historical records of associated tickets | Cover complete problem traceability links, avoid repeating questions about information already provided by customers |
variables | Pass two preset variables: ticket ID and SLA response time limit | Automatically populate business parameters, reduce manual entry operation costs |
| Maximum Context | 8000–12000 token | Cover the total information volume of complete tickets and multi-turn dialogue, calibrated based on actual testing |
| Question Optimization | Enabled | Convert vague customer descriptions into standardized categorization query statements, match preset issue categorization rules |
Three Common Misconfigurations
- Call the API to initiate a ticket-related dialogue, but the generated response does not reference the configured knowledge base documents. The associated knowledge base identifier of the linked ticket is not passed via
variablesin the request parameters, causing the dialogue to not trigger the knowledge base recall process. - When using FastGPT v4.17.0, the dialogue page or knowledge base page crashes. Session cache does not have a reasonable expiration time set, causing a large amount of
chatIdbound data to occupy memory resources. - After deploying a model with Ollama, preset guidance prompts do not take effect. The guidance prompts are not written to the system message list in
messages, causing the model to not load preset issue categorization rules.
How to Verify Successful Configuration
- Initiate a test dialogue simulating a customer ticket, input preset problem descriptions, and check whether the returned results automatically associate historical processing records of the corresponding ticket.
- View dialogue request logs, confirm that
chatId,messages, andvariablesparameters are correctly passed in the request body. - Adjust the customer problem description used in the test, verify whether the categorization tags output by the model conform to preset categorization table rules.
- Switch between different model types, confirm that all configured guidance prompts can be normally recognized and executed by the model, with no abnormal error messages.
The values provided here are common starting points, and should be evaluated against local test 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.