What the Data for This Category Looks Like
Data for this category is generated across the full creator onboarding and verification workflow. Sources include creator backends, enterprise credit information systems, account history databases, and contract management systems. Daily generated documents include onboarding application forms, identity qualification scan copies, verification audit records, and cooperation agreement texts. Fields include creator unique identifiers, qualification numbers, audit statuses, agreement effective times, and more. Data updates are triggered by individual application workflows, with no fixed batch update cycle. A single data entry is associated with multiple types of qualification files and audit node records.
What Constraints Do These Characteristics Impose on the Multi-turn Dialogue and Prompt Words Link
Decentralized data sources require multi-turn dialogue to call data sources from different systems in stages. Prompt words must clearly define call permissions and field formats for each data source. The update characteristic of individual applications requires dialogue context to only retain content within the current application cycle, to avoid cross-application context contamination. The multi-field document structure requires prompt words to accurately match corresponding fields, to prevent information misalignment. The lack of fixed batch update cycles means the dialogue workflow must adapt to linear progression of individual applications, and cannot rely on batch context caching. This category focuses on account access and identity credibility. Prompt words must be clearly distinguished from the logic of adjacent categories, to avoid workflow confusion.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Creator application unique identifier + application timestamp | Ensures independent dialogue context for each onboarding application, avoids cross-application context contamination, and matches the uniqueness characteristic of the data |
messages | Retain the first 3 rounds of user input + system prompt words + latest audit node feedback | The dialogue workflow for a single application focuses on qualification verification and information completion. Excessively long context will interfere with current node judgment, and aligns with the linear progression characteristic of the workflow |
variables | Map creator accounts, qualification file URLs, and enterprise credit query results to preset variables | Unifies calls to decentralized data sources, simplifies prompt word logic, and adapts to the multi-source data characteristic |
| Maximum context length | 800–1200 characters | Dialogue content for a single application will not be excessively long. Content beyond this range will introduce irrelevant historical information and affect audit judgment |
| Single dialogue timeout | 600 seconds | Qualification verification requires calls to external systems. Reserves sufficient response time to adapt to call delays from decentralized data sources |
| Single dialogue round limit | 5 rounds | The onboarding verification workflow usually includes application submission, qualification verification, correction, and audit nodes. Exceeding the round limit triggers manual intervention, which aligns with the processing logic of the most time-consuming links |
Three Common Mistakes
- Phenomenon: Dialogue logs show empty after calling the workflow. Cause: The unique
chatIdparameter is not bound to the current application, causing the context to not be correctly archived. - Phenomenon: Workflow dialogue shows failure, but the model backend receives response logs. Cause: The
variablesparameter does not correctly map the qualification fields of the onboarding application, so the prompt words cannot read valid verification data. - Phenomenon: Historical dialogue content cannot be passed during tool testing. Cause: The
messagesparameter does not carry dialogue information from historical application nodes, causing the tool to fail to associate the current application workflow.
How to Confirm Proper Configuration
- Submit a simulated onboarding application, and check in the FastGPT v4.17.0 backend whether the dialogue log is generated and includes the
chatIdidentifier. - Trigger the qualification verification node, and check whether the
variablescorrectly load the preset qualification data fields. - Confirm during tool testing that the
messagesparameter includes historical dialogue context content. - Simulate a timeout scenario, and verify whether the dialogue triggers a timeout prompt after
600 seconds.
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.