What this category of data includes
Data sources for this category include ticketing systems, chat logs, product logs, and customer contract SLAs. Update frequency is daily or real-time. Daily processed documents include customer tickets, issue triaging tables, handling records, and service reports. Data fields cover ticket identifiers, customer information, problem descriptions, triaging tags, response time limits, handlers, historical interaction content, and more. Structured data primarily uses ticket tables and triaging tables. Unstructured data primarily uses chat logs and handling logs.
Constraints imposed on context and token handling
Diverse data sources and real-time updates require context loading to balance priorities between structured fields and unstructured content. This prevents irrelevant logs from consuming excessive tokens. Real-time updates to ticket data require the context window to support dynamic content appending. Total token consumption must also be limited to match model capacity. Issue triaging relies on core ticket information. If an overly long context truncates core content, triaging accuracy will be negatively impacted. SLA-related fields must be prioritized for context inclusion. This ensures sufficient data supports compliance checks for processing time limits.
Configuration settings
This document applies to FastGPT v4.17.0. The following configuration items can be set in the corresponding platform modules:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Maximum context window | 8000–12000 token | Matches the average total content length of tickets and historical handling records, and aligns with the token capacity limits of common large models |
| Maximum token count for referenced content | 2000–3000 token | Ensures core information from a single ticket or historical chat record is fully included, and prevents irrelevant details from consuming excessive context |
| Context recall priority | Sort by remaining SLA duration | Prioritizes ticket data nearing timeout, which aligns with business SLA compliance requirements |
| Historical record recall limit | Top 10 entries | Controls total context token consumption, and prevents token overflow from large volumes of real-time updated logs |
maxContext | Calibrated against peak business token usage | Adapts to real-time updated ticket data volumes, and prevents context loading failures |
quoteMaxToken | Calculated based on average character count per ticket | Ensures core problem descriptions and triaging basis are fully presented |
Three common configuration errors
- Configuring an ACCESS_TOKEN fails to pull ticket data. The symptom is empty results returned by the interface. The cause is incorrect configuration of context reference permission rules, leading to token verification failure.
- Total token usage exceeds limits after multiple AI model calls in a workflow. The symptom is truncated return results or error messages. The cause is failure to set the
maxContextparameter to isolate context token consumption across models. - Historical ticket data fails to load in offline environments. The symptom is an empty context. The cause is failure to configure local cache context parameters, and network-based pulling fails.
How to verify correct configuration
- Import a single complete ticket and its historical handling records. Check if the content length displayed in the platform's context preview module matches the configured maximum context range.
- Simulate a multi-model orchestrated call. Check if platform token consumption statistics do not exceed the
maxContextset value. - Trigger ticket data pulling. Verify that returned valid content does not exceed the
quoteMaxTokenlimit. - Validate context recall sorted by SLA priority. Confirm that ticket data nearing timeout is prioritized for inclusion in the context window.
The values provided are common starting points, and should be evaluated against samples from the respective deployments.
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.