What Data for This Category Looks Like
Data for this category originates from advertising platforms, CRM systems, attribution SDKs, and form systems. Most updates run on a daily or weekly schedule. Common processed documents include attribution rule configuration tables, lead distribution tables, channel performance reports, and abnormal lead checklists. Core fields include channel identifiers, lead unique IDs, conversion timestamps, and assigned sales accounts. The currency unit is yuan. The data structure is designed for lead source tracking and sales attribution allocation.
Constraints for Multi-turn Dialogue and Prompt Templates
Decentralized data sources require multi-turn dialogue to pass field mapping rules across different systems such as advertising platforms and CRM via variables. messages must retain full verification results from each step for traceability. Daily and weekly update frequencies require prompts to adapt to batch data verification logic, preventing overload from single calls. The structured nature of attribution rules and lead distribution tables requires multi-turn dialogue to verify fields in a preset order, avoiding missing critical parameters like channel identifiers and assigned sales IDs. Temporarily generated abnormal checklist data requires prompts to support dynamic insertion of temporary entries, and full conversation history must be retained for subsequent audits.
Configuration Setup
This configuration is based on FastGPT v4.17.0, and is tailored to multi-turn dialogue and prompt requirements for channel attribution and lead distribution scenarios.
| Configuration Item | Recommended Setup | Rationale |
|---|---|---|
variables | Configured as an attribution field set, including channel identifiers, lead IDs, and assigned sales IDs | Meets the verification needs of structured data for this category, and passes field mapping rules across systems |
messages | Retain verification records from the first 100 turns of dialogue | Supports tracing multi-turn verification processes, and meets audit requirements for abnormal checklists |
| Maximum context length | 8000–12000 characters | Adapts to batch verification of attribution rules and lead distribution tables, preventing context overflow |
| Conversation timeout threshold | 600 seconds | Adapts to delays from multi-data source calls, preventing session interruptions due to slow responses from advertising platform APIs |
| Prompt template variables | Calibrated via actual testing | Adapts to rule differences across channels, and supports dynamic insertion of abnormal checklist entries |
chatId | Bound to the session ID for a single business batch | Distinguishes conversation histories across different lead distribution batches, preventing data mixing |
Three Common Misconfigurations
- When copying Markdown content generated by dialogue, no line breaks are present, and a manual selection prompt appears. Line breaks in multi-turn dialogue are not properly escaped, and Markdown format line break rules are not configured in the prompt template.
- After completing variable reference configuration, the temperature setting button disappears and cannot be adjusted. The advanced parameter settings switch is not enabled in the dialogue configuration. Variable reference mode hides non-required advanced configuration items by default.
- After calling the attribution and lead distribution workflow, runtime data in the conversation log is empty. Mapping parameters for data sources such as advertising platforms and CRM are not passed via
variables, causing the workflow to fail to load corresponding business data.
How to Verify Successful Configuration
- Trigger a batch lead verification dialogue, and check if the generated Markdown content includes correct line break formatting.
- In variable reference mode, confirm that the temperature setting button is visible, verifying that the advanced parameter switch is enabled correctly.
- Call the workflow bound to this category’s data, and check if the conversation log loads runtime data from the corresponding data sources.
- Input test parameters containing code blocks, and confirm that the text content extraction module correctly identifies and returns the corresponding results.
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.