What this category of data looks like
This type of data typically comes from CRM systems, contract order databases, identity management systems, and product backends. Daily associated documents include customer onboarding application forms, tenant configuration sheets, administrator account lists, and delivery confirmation documents. Data updates occur per order or daily. Fields cover basic customer information, tenant permission configurations, administrator account permission scopes, and more. No unified fixed unit standard exists. Field formats are matched only to business scenarios.
Constraints for context and token processing
Dispersed data sources require cross-system retrieval. Multi-source data format alignment must be completed first during context splicing. This avoids invalid token consumption. Individual data entries have high update frequencies. Set a reasonable expiration mechanism for context caching. This prevents use of expired tenant configuration information. Fields lack unified units. Skip non-content fields during token counting. This avoids token count deviations. Documents such as delivery confirmation sheets must be included as review basis in the context. Control token usage per individual document. This prevents exceeding the model’s context limit.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 10000–14000 token | Adapts to the model context compatibility range of FastGPT v4.17.0, covering the splicing requirements of multi-source tenant data |
quoteMaxToken | 1800–2200 characters | Limits the reference length of a single onboarding application form or configuration sheet to avoid invalid token consumption |
| Context cache expiration duration | 6 hours | Matches the per-order or daily update rhythm of this category, ensuring the latest tenant configuration information is used |
| Global token statistics switch | Enabled | Accurately counts token usage for customer data retrieved across systems, preventing context overflow |
| Tenant data aggregation rule | Aggregate by application form dimension | Aligns with the source logic of business data, preventing unrelated data from being mixed into the context |
Three common misconfigurations
- Symptom: Tool call node returns Invalid JSON: Bad control chara error. Cause: When splicing context from tenant configuration sheets and onboarding application forms, invisible control characters in documents are not filtered, leading to token parsing exceptions.
- Symptom: Model returns a prompt about insufficient context length, or actual available context is far lower than the configured value. Cause: Data is not aggregated by tenant dimension, causing unrelated cross-tenant data to occupy excessive token count in the context.
- Symptom: Tenant identity information is not loaded correctly in global variables, causing review nodes to fail to call model interfaces with corresponding permissions. Cause: Tenant identity tokens are not included in the global cache during context splicing, or cache expiration duration is set too short, leading to token invalidation.
How to verify correct configuration
Import a single onboarding application form document. Confirm if the token count in the context preview panel falls within the configured maxContext range. Simulate cross-tenant data retrieval. Confirm if the context splicing result only includes configuration information for the target tenant. Trigger a tool call node. Confirm the returned JSON format contains no invisible control characters. Wait longer than the configured context cache expiration duration. Confirm the context automatically refreshes to the latest tenant configuration data.
The values provided are common starting points and should be validated against local 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.