What Data for This Category Looks Like
Data for this category originates primarily from advertising platforms, CRM systems, attribution SDKs, and form systems. It supports channel parameter verification, lead deduplication, and attribution determination during code and change reviews. Regularly maintained documents include attribution rule configuration tables, lead distribution tables, channel effect reports, and abnormal lead checklists. Data updates follow a daily or weekly cadence. Fields include channel unique identifiers, lead source parameters, sales attribution accounts, conversion timestamps, and other relevant fields. Some parameters must match format requirements of their corresponding systems.
Constraints on Context and Token
Multi-source data access requires context to integrate structured and semi-structured data from disparate systems such as advertising platforms and CRMs. Variations in field formats across sources increase the complexity of context splicing during code reviews. Total token count per round of context must be limited to avoid overflow. Daily or weekly update cadences require context to be refreshed regularly, preventing expired attribution rules or lead data from reducing the accuracy of change reviews. Business documents vary widely in length, so differentiated token truncation strategies must be configured for each document type to ensure core business fields are not truncated.
Configuration Guidelines
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multi-source business documents, prevents core attribution fields from being lost due to token overflow |
quoteMaxToken | 1500–2000 token per single document | Limits the maximum token quota for single-document references, tailored to business documents of varying lengths such as channel reports and lead distribution tables |
| Context refresh cycle | Once daily or once weekly | Matches the daily/weekly update cadence of this category’s data, ensures context uses the latest attribution rules and lead data |
| Document chunk overlap length | 100–200 characters | Prevents loss of cross-chunk channel parameters and lead identifiers |
| Maximum per-round query token | 3000 token | Limits total token consumption for consecutive queries, prevents exceeding platform quotas |
Three Common Misconfigurations
- Phenomenon: Core channel parameters are truncated when per-round context exceeds the
maxContextlimit. Cause: ThemaxContextconfiguration was not adjusted for the total length of multi-source business documents, and the default value was used directly. - Phenomenon: Context is not refreshed, and expired attribution rules lead to incorrect channel attribution determinations. Cause: No context refresh cycle matching the data update cadence was configured, and old data was used over extended periods.
- Phenomenon: A rerank error occurs after offline access to the reranker model. Cause: The token quota of the reranker model was not included in total context token calculations, leading to token conflicts.
How to Verify Correct Configuration
- Review the application’s context invocation logs to confirm that total token usage per call does not exceed the configured
maxContextvalue. - Trigger a data update and check whether the context automatically refreshes to use the latest attribution rules and lead data.
- Test referencing three or more channel reports simultaneously, and confirm that core fields for all documents are not truncated.
- Run a reranker model test to confirm no rerank error occurs and token consumption aligns with configured requirements.
The values provided are common starting points and should be evaluated using samples from the actual deployment context.
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.