This document applies to FastGPT v4.17.0.
What this category of data looks like
Data for this category primarily comes from advertising platforms, CRM systems, attribution SDKs, and form systems. Daily processed documents include attribution rules, lead distribution tables, channel reports, and exception checklists. Data updates follow a daily or weekly rhythm. Common data fields include channel identifiers, lead source parameters, assigned sales information, and conversion results. Core processing steps involve channel parameter verification, duplicate lead deduplication, and sales attribution confirmation. Improper processing can lead to distorted channel ROI, sales disputes, and budget misalignment.
What constraints these characteristics impose on the context and token link
Multi-source data integration increases token consumption during context consolidation. Reasonable control of total context length is required. Daily or weekly update cycles require context to sync latest data regularly. Avoid using expired attribution rules and channel reports. Format differences across documents—such as parameter definitions in attribution rules and field structures in lead distribution tables—require context processing to retain original text semantics. Avoid excessive token splitting. Additionally, multi-field verification and deduplication logic require sufficient historical matching records to be retained in the context. Prevent loss of critical channel parameters or attribution information due to insufficient token count.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext (Maximum Context Length) | 8000–12000 token | Adapts to the total length of multi-source attribution data and lead reports, avoids exceeding model token limits |
quoteMaxToken (Maximum Token per Quote) | 1500–2000 token | Limits the reference length of a single channel report or attribution rule, prevents single content from occupying excessive context |
| Context Sync Interval | Daily or Weekly | Matches the daily/weekly update rhythm of data, ensures context uses the latest rules and reports |
| Historical Reference Retention Count | Top 10 entries | Focuses on matching records of recent key leads, reduces invalid token consumption |
| Exception Record Context Length | 3000–5000 token | Reserves sufficient tokens for complete verification logic for detailed content of exception checklists |
Three Common Misconfigurations
- Scenario: After setting
quoteMaxTokento 2000, single reference content still exceeds token limits. Cause: Did not account for original document splitting logic. Some long channel reports were not chunked in advance, causing single reference content to exceed the configured limit. - Scenario: Conversation context is lost, continuous conversation cannot be maintained. Cause: The
maxContextparameter is not configured, or the value is too small, causing historical lead matching records to be automatically truncated. - Scenario: Specific attribution rules are not correctly identified. Cause: Critical text containing channel parameters was forcefully split, destroying the semantic integrity of the rules.
How to Verify Proper Configuration
- Review logs for total context length, confirm that the value of
maxContextmatches the total token consumption of current integrated data. - Test reference of a single long channel report, check if the limit set by
quoteMaxTokentakes effect. - Trigger a complete lead distribution process, verify that the context includes the latest attribution rules and channel data.
- Simulate an exception verification scenario, confirm that critical text containing channel parameters was not excessively split, and retains complete semantics.
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.