What the data for this category looks like
Data for this category comes primarily from customers’ historical business systems, standardized CSV/Excel templates, database export files, and FastGPT platform import operation logs. Daily supporting documents include data import templates, migration execution plans, field mapping comparison tables, and migration effect verification reports. The data update rhythm adjusts irregularly based on project progress. Updates typically occur after each migration action or when receiving customer feedback. Core data fields fall into three categories: business entity identifiers, attribute values, and association relationships. Some scenarios require auxiliary fields such as data creation time and verification tags.
Constraints on multi-turn dialogue and prompt engineering workflows
The data characteristics of this category create multiple constraints for multi-turn dialogue and prompt engineering workflows. First, data sources include multiple heterogeneous formats. Different customers use varying field naming and unit standards. Multi-turn dialogue must be used to gradually confirm field mapping rules, avoiding direct application of generic templates. Second, the number of supporting documents is large, and updates happen frequently. Prompt designs must guide users to upload corresponding documents per project phase. This ensures the dialogue context links only to the latest files for the current migration task. In addition, data update rhythms are irregular. Bind single-task dialogue chains using chatId to prevent context confusion across different projects. Limit dialogue context length to avoid lag caused by excessive historical data.
Configuration settings
This configuration is based on the interface and API logic of FastGPT v4.17.0. Specific configuration items and their rationales are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context token count | 8000–12000 token | Covers content length for core multi-turn dialogues such as field mapping confirmation and dirty data cleaning rule negotiation. Prevents truncation of key information |
chatId binding rule | One-to-one correspondence with migration task ID | Ensures single-task dialogue context does not mix with other projects. Aligns with the business requirement to isolate data by project for this category |
variables preset values | Import template fields, customer system field comparison table | Reduces repeated input of basic configuration information. Adapts to high-frequency field mapping consultation needs for this category |
| Multi-turn follow-up trigger condition | Detect user input containing keywords such as "field", "mapping", "cleaning", "verification" | Automatically starts multi-turn confirmation for common consultation scenarios in this category |
| Single dialogue response timeout | 600 seconds | Covers wait time for time-consuming operations such as data template parsing and field mapping verification. Prevents interrupted connections |
| Historical dialogue storage limit | Match total dialogue count for the current project | Aligns with irregular data update rhythms. Avoids invalid historical data occupying system resources |
Three common misconfigurations
- Phenomenon: Cannot upload data templates or field mapping tables when calling the dialogue API. Returns an error message of "unsupported file type". Cause: Fails to explicitly require users to submit content via the specified migration document submission entry in prompts. Incorrectly applies generic dialogue file upload logic.
- Phenomenon: Historical dialogue records for different customers are not isolated. Migration data from other projects can be viewed across projects. Cause: Fails to bind a unique
chatIdfor each migration task. This leads to shared context, violating the business requirement to isolate data by project for this category. - Phenomenon: Workflow nodes can only initiate a single question. Cannot adjust field mapping rules based on user feedback. Cause: Fails to configure multi-turn dialogue trigger logic. Only calls the single-turn question and answer interface. Does not enable multi-turn context management functionality.
How to confirm proper configuration
- Initiate a test question containing keywords such as field mapping and dirty data cleaning. Observe if the system automatically starts a multi-turn confirmation process. Check that the dialogue context only links to content from the current test task.
- Generate two different migration task test dialogues. Confirm their respective dialogue records cannot access each other. Verify that data isolation configuration is active.
- Upload test CSV templates and field mapping tables. Check that the system correctly identifies and associates the files with the current dialogue task. No error messages should be returned.
- Wait longer than the preset response timeout duration. Observe if the system can normally handle subsequent questions. Verify that the timeout configuration is reasonable.
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.