What this category of data looks like
Data originates from ticketing systems, chat logs, product logs, and customer contract SLAs. Update frequency is daily or real-time. Daily documents include customer tickets, issue tiering tables, processing records, and service reports. Data fields include ticket unique ID, issue classification tags, tier level, processing time limit, associated chat snippets, and SLA terms. The platform retains only native structured and unstructured business content, with no additional custom statistical fields. This category initiates processing workflows from customer feedback entry points, and differs from defect management and regression testing scenarios. This page applies to FastGPT v4.17.0.
What constraints these characteristics impose on the Context and Token workflow
The system must filter duplicate ticket chat snippets during context recall to avoid redundant token usage, given scattered data sources and real-time updates. Daily documents include structured fields such as tier level and processing time limit. The ticket unique ID must be used as the context association identifier to ensure precise matching of recalled context to the current ticket. SLA terms are compliance-related content. The system must limit the token proportion of SLA terms in the context to prevent exceeding configured thresholds. The system must control the token length of a single context, as unstructured ticket description content varies widely in length, to avoid overall context overflow that affects model responses.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | Top 10–15 entries | Ticket acceptance context typically focuses on conversations and processing records from the most recent 15 entries. Excessive entries consume excess tokens and slow response speeds |
quoteMaxToken | 800–1200 tokens | This range fits the typical length of single ticket descriptions and processing records, avoiding exceeding token limits for individual entries |
| Context recall matching threshold | 0.75–0.85 | Filters low-similarity unrelated ticket content to prevent context pollution |
| Single context recall upper limit | Top 5 matching results | Controls overall token usage to avoid exceeding platform quotas |
| Context refresh frequency | Real-time | Matches the real-time update rhythm of ticket data to ensure context timeliness |
Three common misconfigurations
- Context pollution: Irrelevant ticket processing records and chat snippets are mixed into conversation details. This occurs when a reasonable context recall matching threshold is not set, and low-similarity unrelated content is recalled.
- JSON format errors in AI responses: Reply content contains JSON fragments that do not meet business requirements. This occurs when the token limit for a single context is not enforced, and overly long context content interferes with the model's format output logic.
- Abnormal token carried in file paths: When accessing uploaded ticket attachments via HTTP requests, the returned relative path includes extra tokens that cannot be processed normally by Flask. This occurs when the automatic context token attachment configuration is not disabled, causing a verification token to be injected into the file path.
How to verify correct configuration
- A historical ticket dataset is imported, a simulated conversation is initiated, and context recall results are reviewed to confirm only content associated with the current ticket is included, with no unrelated snippets.
- A ticket with a long description is submitted, and the AI reply is checked to ensure it does not contain JSON content with format errors.
- After configuring the maximum number of context entries, multi-turn conversations are initiated, and the number of context entries displayed in conversation details is verified to match the configured value.
- A ticket attachment is uploaded and an HTTP request is initiated, and the returned file path is confirmed to not include extra tokens and can be parsed normally.
The values provided here 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.