What the data for this use case looks like
The data sources for ticket auto-classification are ticket systems from state-owned enterprises, government agencies, and public service hotlines. Collected data includes fields such as appeal text entered by agents, call time, ticket ID, ticket status, and user level. Data updates occur near-real-time. A classification request triggers each time a new ticket is submitted. Appeal text length varies widely, from tens to thousands of characters. There are no fixed format requirements. Some tickets include additional fields like agent notes and transfer records. The overall data structure shifts slightly based on business scenarios.
What constraints these characteristics impose on HTTP interfaces and external systems
Near-real-time ticket data flow requires HTTP interfaces to support low-latency requests. This prevents exceeding response time limits for hotline services. Ticket fields are not fixed and include unstructured text. Interface requests must support dynamically passing business fields as classification criteria. They must also accommodate varying lengths of appeal text to avoid request failures from overly long content. Ticket data contains sensitive information related to public services. Interface transmission must comply with security authentication requirements. This prevents data leaks or unauthorized access.
How to set up configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer ${custom API key} | Follow HTTP standard authentication specifications, compatible with most third-party ticket systems |
| Interface timeout | 600 seconds | Match standard response times for hotline tickets, avoid blocking agent processing workflows |
variables | Map ticket ID, appeal text, agent note fields | Extract core business data required for ticket auto-classification |
responseData | Extract two core fields: classification label, confidence score | Only return results necessary for business purposes, reduce network transmission overhead |
| Request retry count | 2 times | Address temporary network fluctuations, reduce the probability of classification failure |
Three Common Mistakes
- Phenomenon: Interface calls return a 403 Forbidden status code, or the FastGPT test panel shows an insufficient permissions prompt. Cause: The
Authorizationparameter is not configured correctly, or the external system’s API key does not grant access permissions to FastGPT. - Phenomenon: Interface calls return a 200 status code, but the
responseDatafield is empty or contains no classification-related content. Cause: Core business fields are not correctly mapped viavariables, so the external system cannot obtain ticket data required for classification. - Phenomenon: The browser console displays a cross-origin request blocked error, and interface calls cannot be initiated. Cause: FastGPT’s request source is not allowed in the external system’s interface configuration, or cross-origin requests are not handled via backend forwarding.
How to Confirm Configuration Is Complete
- On the FastGPT v4.17.0 interface debugging page, input the
variablesdata of a test ticket, send the request, and confirm the returned status code is 200. - Review the
responseDatareturned by the interface, and confirm it includes the two preset core fields: classification label and confidence score. - Connect to a real ticket flow, observe whether classification results align with business expectations, and adjust configuration items until alignment is achieved.
- Simulate a temporary network fluctuation scenario, confirm the interface retry mechanism takes effect, and no consecutive call failures occur.
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 2026-09-28.