What Data for This Category Looks Like
Data for this category typically originates from customer ITSM systems, monitoring platforms, asset inventories, and on-duty records. Four document types are produced regularly: on-site daily reports, service desk tickets, inspection reports, and SLA monthly reports. Data updates follow daily and monthly cycles. Core fields include ticket numbers, inspection items, SLA compliance rates, and on-site staff duty hours. No unified mandatory format exists, and adjustments must align with the delivery requirements of the customer’s IT department.
Constraints on Context and Token Processing
Data sources for this category are scattered and formats are inconsistent. Fields in tickets and inspection reports exported from different systems vary significantly. Additional field alignment is required during context stitching, or token quotas will be wasted. Daily updated on-site tickets and on-site daily reports require frequent loading, so token call frequency must match high-frequency business scenarios to avoid triggering platform call limits. Document lengths vary widely, from hundred-word inspection records to thousands-word SLA monthly reports. The context window must flexibly adapt to input content of different lengths. Additionally, since missing information directly leads to customer penalties, key business fields must be prevented from being truncated by tokens.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the input length of mixed documents such as on-site daily reports, service tickets, and SLA monthly reports, preventing truncation of core business fields |
quoteMaxToken | 1500–2000 token | Limits the token usage of a single referenced document, preventing a single long document from occupying too much context quota |
| Context recall count | Top 3–5 entries | Matches the daily business data scale of on-site outsourced operations services, reducing redundant token consumption |
| Reference similarity threshold | 0.75–0.85 | Filters low-relevance inspection records or tickets, accurately matching the needs of drafting requirements and design documents |
| Single-call token limit | 10000 token | Adapts to daily high-frequency document drafting tasks, preventing single calls from exceeding platform quota limits |
Three Common Misconfigurations
- Context truncation error occurs even after configuring
maxContext: This happens when thequoteMaxTokenparameter is not adjusted synchronously. The token usage of a single referenced document exceeds the remaining quota of the global context, resulting in truncation of core business fields. - The number of context recall results does not match the configured value: This occurs when a reasonable reference similarity threshold is not set. Low-relevance inspection records are incorrectly recalled, occupying recall quotas and preventing core ticket data from being included in the context.
- Global variable token cannot be passed when calling MCP tools: This happens when context variable mapping is not configured in the workflow, and the global token variable is not bound to the input parameters of the MCP tool, preventing the tool from obtaining required authentication information.
How to Verify Correct Configuration
- Import a typical on-site daily report, service ticket, and SLA monthly report, test the context stitching effect, and check whether core business fields are included and there is no obvious truncation.
- Initiate a simulated call for drafting requirements and design documents, verify whether the returned context content matches the configured recall count and similarity threshold.
- Check the platform’s token consumption logs to confirm that the token usage of a single call does not exceed the preset single-call limit.
- Configure the input parameters of the MCP tool, bind the global token variable, initiate a test call, and confirm that the tool can normally obtain and use the variable.
The values given 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.