What the data for this category looks like
The core data for this category comes from four sources: risk control systems, audit records, user behavior logs, and report evidence. Update cycles are divided into real-time and daily updates. Daily produced documents include four categories: account ban records, appeal materials, review opinions, and user notifications. Fixed fields for each document include unique account identifier, violation type identifier, appeal submission time, review disposition result, and notification sending status. The data does not have a unified binary format. Some appeal materials use plain text format, while some report evidence uses attachment format. Cross-source data aggregation must be completed via tool calling.
What constraints these characteristics impose on the tool calling and plugins workflow
Scattered data sources and inconsistent formats require tool calling to support multi-source data pulling and format conversion to avoid data loss. Real-time updated risk control data requires tool calling to adapt to low-latency request logic, otherwise the latest review basis cannot be obtained. The fixed-field document structure requires tool calling to verify field integrity, otherwise subsequent review processes will encounter errors. The need to call multiple types of documents together requires plugins to support switching data reading rules based on scenarios to avoid invalid data pulling. Errors in this category directly affect user rights and interests, so tool calling must include error retry and exception alert capabilities to reduce the risk of wrongful bans or the return of violating accounts.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | Set to auto, limit selectable tools to risk control data pulling, appeal document parsing, and review result query | Based on the scattered data sources of this category, limiting the tool scope avoids invalid calls |
| Tool calling timeout | 300 seconds | Based on the real-time data update cycle, multi-source data aggregation must be completed within a reasonable time frame |
functionCall | Enabled, force return of structured review opinions | Based on the need to generate standardized disposition content for this category to facilitate subsequent process flow |
| Multi-tool concurrency control | Set to single-tool serial execution | Based on concurrency issues reported by users, avoiding resource conflicts caused by simultaneous calls of multiple tools |
| Document reading verification | Enabled, verify required field integrity | Based on the fixed field requirements of category documents to ensure no missing read data |
| Error retry strategy | Retry 2 times, 60-second interval | Based on the unstable characteristics of real-time data pulling to reduce call failure rates |
Three common mistakes
- Tool concurrency call issues: The symptom is that the risk control data pulling interface returns duplicate account ban records. The cause is failure to limit multi-tool concurrent execution, with multiple tool instances sending requests simultaneously leading to duplicate data pulling.
- Knowledge base reading content missing: The symptom is that the answer returned by the API call does not include appeal materials or report evidence content. The cause is failure to correctly bind the document data source, or failure to enable the document reading verification configuration.
- Large model call connection error: The symptom is returning a
connection errorstatus code. The cause is failure to correctly configure the tool calling timeout parameter, or failure to complete channel connectivity verification.
How to confirm the configuration is correct
- Initiate a ban review request for a test account, check the tool calling logs in FastGPT v4.17.0, and confirm that only the preset specified tools are triggered.
- Call the API interface to verify that the returned result includes required fields such as bound appeal materials and risk control records.
- Simulate a tool calling timeout scenario, and check whether the preset error retry strategy is triggered and an alert is generated.
- Check whether the review opinion returned by the large model is in structured format that meets the
functionCallconfiguration requirements.
The values provided here 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.