What this category of data looks like
This category of data primarily comes from the creator backend, enterprise credit information system, account history database, and contract management system. Daily processed documents include onboarding application forms, identity qualification scan copies, verification audit records, and cooperation agreement files. Data updates trigger once per submitted application. The latest status syncs daily during the audit cycle. Data fields include applicant account identifiers, qualification document numbers, audit node statuses, cooperation periods, and other relevant items. No unified standardized format exists. Each batch of applications must be integrated individually.
Constraints on Multi-turn Dialogue and Prompt Engineering
Decentralized data sources and inconsistent batch data structures require multi-turn dialogue to dynamically integrate field information from different systems. This avoids omission of key qualification content. Data updates trigger upon application submission. Dialogue context must stay available during the audit cycle. Cached historical data beyond the audit cycle cannot be used. Document lengths vary widely per batch. Prompts must adapt to varying lengths of onboarding applications and qualification materials. This prevents key information truncation. Data involves account credibility and cooperation compliance. Dialogue-generated audit conclusions must strictly align with original document content. This avoids false or incorrect verification results.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables variable configuration | Pass applicant account ID, qualification document type, and cooperation agreement number fixedly | Extract fixed fields from the creator backend and contract system, aligning with the core requirement of handling account access and identity credibility for this category |
messages context splicing rule | Splice the top 20 conversations in reverse chronological order of application time | Matches the rhythm of data updates triggered by application submission, avoiding redundant historical interference with qualification verification |
chatId association rule | Bind to the unique identifier of a single batch of applications | Ensures dialogue context is strongly associated with the corresponding application, preventing cross-application confusion of audit content |
| Maximum context length per single dialogue | 8000–12000 characters | Covers the average document length of onboarding applications and qualification materials, preventing key information truncation |
| Streaming output chunk interval | 1–2 seconds | Adapts to front-end display experience, meeting return speed requirements in actual usage |
| Dialogue history retention period | 24 hours | Corresponds to the daily data update frequency, ensuring complete availability of context during the audit cycle |
Three Common Mistakes
- Set a fixed 4-second streaming output return interval without allowing adjustment to 1–2 seconds. This occurs because the corresponding chunk interval parameter is not configured, and the platform’s default general interval value is used instead.
- The login-free dialogue window icon cannot be modified, and content confusion occurs when splicing multiple rounds of AI outputs. This happens because no custom icon configuration parameter is passed via
variables, and dialogue context is not split by application batch, leading to splicing conflicts. - Unable to pull all dialogue records or export data. This occurs because no permission variable for dialogue logs is configured, or the correct
chatIdassociated interface is not called, resulting in permission verification failure.
How to Verify Correct Configuration
- Initiate a test dialogue for onboarding qualification verification. Check whether the returned content includes preset
variablesfields such as applicant account ID and qualification document type. - Enable streaming output. Observe whether the return chunk interval falls within the 1–2 second range, to verify that the chunk interval configuration is effective.
- Use the FastGPT v4.17.0 version audit role account to call the dialogue log interface. Confirm that all dialogue records for the corresponding application can be pulled and exported.
- Submit a test application. Check that the dialogue context completely covers documents such as the onboarding application and qualification materials, with no truncation.
Note: The configuration values listed above are common starting points. Adjustments should be made based on specific samples and use cases.
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.