What data for this category looks like
Data for this category comes primarily from customer-submitted warranty work orders, system operation logs, project contract warranty clauses, and software version update records. Daily produced documents include warranty work orders, defect confirmation sheets, repair records, and customer confirmation sheets. Data updates occur daily or with each business order. Document fields include work order number, defect details, responsibility boundary determination results, repair execution records, and customer confirmation status. No unified mandatory unit fields exist. Only matching identifiers are used based on business scenarios.
Constraints on context and token from these characteristics
Multi-source data characteristics of this category create multiple constraints for context and token configuration. Warranty-related data from different sources has significant length differences. Short-text work order descriptions and long-text operation logs, contract clauses require different context interception rules. Frequently updated business data requires context to link to the latest work orders and repair records in real time. This prevents use of expired warranty clauses or status data. The multi-field document structure requires precise matching of core fields such as responsibility boundary and customer confirmation status during retrieval. Irrelevant content token usage must be limited to prevent invalid data from occupying valid context quotas.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to integration needs of multi-source warranty data, balances context completeness and token consumption limits |
quoteMaxToken | 1500–2000 token per single reference | Controls token usage for a single warranty document, prevents single data entries from excessively occupying context quotas |
| Single search limit | Top 8 entries | Matches the volume scale of core documents for this category, avoids excessive low-relevance data consuming tokens |
| Context real-time refresh | Enabled | Adapts to frequently updated warranty work orders and repair records, ensures retrieval uses latest business data |
| Query optimization | Calibrated via actual testing | Demand descriptions for this category often include professional terminology. Adjust optimization strategies via actual testing to match retrieval accuracy |
Three common errors
- Symptom: Single context token consumption during retrieval approaches or exceeds the set limit, leading to query timeouts or incomplete returned content. Cause: Failed to reasonably limit the value of
maxContext, and included multi-source warranty data in the context without restriction. - Symptom: When calling tools in a workflow, global variable token parameters cannot be accessed, leading to tool call failures. Cause: Failed to enable the global variable pass-through switch in context configuration, and failed to include global tokens in the context transfer scope.
- Symptom: Token consumption when calling a model to query associated data does not match the token count returned by the actual API. Cause: Failed to accurately configure context interception rules, leading to invalid content being included in token statistics.
How to confirm correct configuration
- View retrieval logs in FastGPT v4.17.0, check whether single context token consumption falls within the set
maxContextrange. Adjust values until consumption matches expectations. - Simulate submitting a warranty work order query, check whether the returned context includes the latest repair records and customer confirmation status. Verify that the real-time refresh configuration is active.
- Run a tool test, confirm that token parameters in global variables can be passed normally. Verify that the context pass-through configuration is correct.
- Count the number of documents returned by retrieval, confirm that it matches the single search limit setting. Adjust matching rules until returned content aligns with business requirements.
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 2026-10-19.