What Data for This Segment Looks Like
Daily documents for this segment include account ban records, appeal materials, review opinions, and user notifications. Data sources include risk control systems, audit records, user behavior logs, and report evidence. Update frequency is daily or real-time. Common data fields include account ID, violation type, appeal submission time, review result, associated report content, and others. There is no unified fixed format, and the field structure must be adjusted according to platform governance rules. This document is written based on the functional logic of FastGPT v4.17.0.
Constraints on Multi-turn Dialogue and Prompt Words
Real-time data updates require multi-turn dialogue to limit context scope, to avoid introducing expired or redundant violation records. Document fields are not unified, so prompt words must support dynamic injection of bound business fields to adapt to governance rules of different platforms. The most time-consuming violation evidence verification link requires multi-turn dialogue to break down verification logic step by step, reducing manual repeated checks. The consequences of errors are directly linked to account rights and platform compliance, so prompt words must strengthen evidence matching verification to avoid misjudgments. At the same time, focus must be placed on the full process of review and appeal for account-level penalties, which differs from the content processing logic of community reports.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages context splicing rule | Include user appeal conversations from the past 12 hours + 3 latest associated ban records | Data update frequency is daily or real-time, context scope must be limited to avoid redundancy, while covering the complete appeal conversation chain and core violation evidence |
variables bound fields | Account ID, violation type, appeal time, review status | Daily documents include such fields, which must be automatically injected into prompt words to support evidence verification and review judgments |
| Single conversation timeout setting | 600 seconds | The violation evidence verification link takes a long time, so sufficient time must be reserved for information retrieval and verification |
| Multi-turn dialogue round limit | First 15 rounds | Overlong historical conversations will introduce irrelevant information, affecting the judgment efficiency of current appeal review |
| Context filtering switch | Only retain appeal-related interactions | Interference from other non-target conversations in the workflow must be excluded, focusing on the current account appeal scenario |
Three Common Misconfigurations
- Phenomenon: All replies from multiple AI dialogue nodes configured in the workflow appear in the chat conversation with the workflow. Cause: The output switch for non-target conversations was not turned off in the node configuration.
- Phenomenon: A
connection errorerror occurs when using model dialogue. Cause: The correct model interface key was not configured, or network access permission was not enabled. - Phenomenon: Review results are not automatically synchronized to user notification documents. Cause: The rule for automatically synchronizing review results to specified document variables was not configured in the prompt words.
How to Confirm Correct Configuration
- Initiate a simulated account appeal conversation, check whether the returned context only includes the specified appeal conversation and associated ban records.
- Trigger the
connection errortest scenario, verify whether the conversation triggers a timeout prompt within 600 seconds. - View the workflow node output, confirm that replies from non-target AI dialogue nodes are not displayed in the chat interface.
- Modify the icon configuration for the login-free scenario, initiate a conversation from the login-free entry, and verify whether the icon is updated synchronously.
The values provided are common starting points and should be evaluated 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.