What this category of data looks like
This category of data primarily comes from personnel registration and qualification information on crowdsourcing platforms, quality spot check results for annotation tasks, training exam answer sheets of annotators, and settlement records from financial systems. Core documents maintained daily include personnel access rules, annotation training materials, monthly performance sheets, and settlement lists. Data update cycles are divided into daily (annotation quality, real-time performance data) and monthly (settlement lists, personnel access rule updates). Core fields include unique personnel identifiers, access qualification levels, annotation accuracy, settlement amounts, and similar items. There is no unified fixed unit standard, which adjusts based on the type of each annotation project.
What constraints these characteristics impose on tool calling and plugins
The data sources for this category are scattered, covering multiple independent systems such as platforms, quality inspection, training, and finance. Tool calling must adapt to format conversion rules for multi-source heterogeneous data. The update cycles of different data vary, requiring tools to support two trigger modes: daily batch processing and real-time single-record calling. Core documents include different types such as access rules, performance sheets, and settlement lists. Tools must bind processing logic for corresponding scenarios to avoid confusing settlement data with quality assessment data. Personnel information and settlement data are strongly correlated. Tools must verify field matching relationships during calls to prevent settlement risks caused by mismatches.
Configuration Settings
This page is compatible with FastGPT v4.17.0. The following configuration items are formulated based on the tool calling and plugin logic of this version.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool Calling Mode | Switch between toolChoice and functionCall based on scenario | Adapt to different needs of batch quality inspection and single-record verification, match the processing scale of annotation tasks |
| Multi-Model Scheduling Rule | Allocate models based on input data type. Call multi-modal models for multi-modal data, call Qwen2 series models for pure text data | Match model adaptability for different annotation tasks, address configuration needs for multi-model traffic splitting |
| Scheduled Task Trigger Frequency | Set daily batch tasks to 86400 seconds, trigger real-time tasks as needed | Match the daily/real-time data update cycles, avoid repeated calls or missed updates |
| Data Source Verification Rule | Verify the association consistency of personnel ID and settlement amount | Prevent settlement risks caused by data mismatches, adapt to the strongly correlated data characteristics of this category |
| Result Storage Path | Divide directories by project, such as /annotator/[project_id]/ | Facilitate subsequent quality inspection and settlement reconciliation, match the document classification management needs of this category |
| Maximum Retry Count | 3 times | Address network fluctuations or temporary interface failures, reduce the probability of process interruption caused by a single failed call |
Three Common Mistakes
- Phenomenon: Models are not allocated according to task type in a single workflow, causing multi-modal data to be sent to pure text models for processing. Cause: Rules for triggering corresponding models based on input data type are not configured, and model adaptability requirements for different annotation tasks are not distinguished.
- Phenomenon: Bing Search plugin returns no results, and cannot function properly in domestic network environments. Cause: A search tool adapted to domestic network environments is not substituted, or plugin network parameters are not correctly configured.
- Phenomenon: Settlement fields do not match personnel information after tool calling, leading to payment error risks. Cause: Data source verification configuration is not enabled, association consistency of data from different systems is not verified, and the strongly correlated characteristics of category data are ignored.
How to Confirm Proper Configuration
- Trigger a single-record annotation quality verification task, verify whether the tool calls the specified model and data source, and check that the returned result matches the expected processing logic.
- Trigger a scheduled batch task, verify whether the corresponding data source is called at the configured frequency, with no repeated calls or missed updates.
- Simulate a data mismatch scenario, verify whether the tool triggers a verification alert and blocks abnormal association relationships between personnel and settlement data.
- Switch between different types of annotation tasks, verify whether the tool automatically allocates the corresponding model in line with the configured scheduling rules.
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.