What Data for This Category Looks Like
Crowdsourced annotator management data draws from multiple sources. These include worker registration information from crowdsourcing platforms, annotation quality spot check data, online training exam records, and settlement lists from financial systems. Data update frequencies fall into two categories: daily and monthly. Annotation quality data updates daily. Worker access rules, training materials, and settlement lists update monthly. Daily associated documents include worker qualification access forms, training assessment score sheets, monthly performance ranking tables, and settlement details. Fields cover basic information such as worker unique identifiers, qualification levels, assessment scores, and settlement amounts.
Constraints on Context and Token Usage
This category has diverse data types and uneven update schedules. Token consumption varies widely across different documents. Structured settlement lists and performance tables use more tokens. Unstructured training materials have relatively stable token usage. Multi-source data requires classification and filtering during context loading. This prevents unrelated data from consuming excessive token quotas. Daily updated annotation quality data must load the latest entries first. Monthly updated settlement lists need controlled loading frequency to reduce token consumption from context refreshes. The number and length of fields in worker data directly affect token usage per single context entry. A reasonable token limit for single referenced data must be set to avoid context overflow.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers total token consumption of all core documents for this category, prevents data loss from context overflow |
quoteMaxToken | 2000 token | Limits token upper limit for single referenced annotator data, prevents long single settlement details or performance tables from occupying too much context |
| Context Refresh Interval | 12 hours | Adapts to update rhythms of daily annotation quality data and monthly settlement lists, balances data timeliness and token consumption |
| Context Source Filtering | Filter by document type | Only loads three target data types: worker access rules, performance tables, and settlement lists, eliminates token usage from unrelated documents |
| Single Context Load Count | Top 3 latest entries | Controls token consumption of initial context, avoids overloading context from loading too much historical data |
Three Common Configuration Mistakes
- Phenomenon: The model returns context missing some monthly performance data. Cause: No context filtering by document type is configured, leading to automatic truncation of non-target data.
- Phenomenon: Token statistics from MongoDB queries do not match token consumption returned by the model API. Cause: No distinction is made between token consumption from internal context loading and token consumption from model inference. No separate statistics are tracked for token usage of annotator data.
- Phenomenon: The new context field in the AI conversation node is empty. Cause: No reasonable
maxContextupper limit is set, causing context data to be cleared by the system after overflow.
How to Verify Successful Configuration
- Access the context preview panel of FastGPT v4.17.0, confirm only three target documents are loaded: worker access rules, performance tables, and settlement lists.
- Trigger a model call, compare token consumption from MongoDB query logs with context token statistics from the FastGPT backend, confirm the difference between the two aligns with configured expectations.
- Manually trigger a data update, wait 12 hours, then check the context, confirm the latest annotation quality data has been loaded into the context.
- View the
quoteMaxTokenconfiguration item, import a complete settlement detail sheet, confirm the system does not perform forced truncation.
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.