Data Handled by This Category
Data for this category comes primarily from data asset inventories, database metadata, business system documentation, and regulatory documents. Update frequency is quarterly or when a system change occurs. Daily used documents include data catalogs, classification and grading rules, critical data identification tables, and review minutes. Document fields include asset identifiers, sensitivity levels, compliance bases, review statuses, and similar items. No uniform fixed units exist; matching must be completed based on specific asset types. Service targets include data governance teams, security consultants, and legal and compliance staff. Core work focuses on sensitive field identification and grading rule reviews.
Constraints Imposed by These Features on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and linked to metadata across multiple systems. Multi-turn dialogue must support context association across different data sources to avoid repeated retrieval of fragmented information. Document structures vary widely. Prompt engineering must adapt to inputs in different formats such as data catalogs and classification and grading rule tables. A single fixed template cannot be used. Update frequencies are inconsistent. The system must support dynamic loading of the latest classification and grading rules while retaining historical review context. Sensitive field identification and review steps are time-consuming. Multi-turn dialogue must break complex tasks into step-by-step guidance to avoid overloading single-turn inputs. Context must also link to compliance bases to prevent risks of missing critical data governance.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current review session | Used to associate multi-turn dialogue context within the same scenario, ensuring context continuity during the review process |
messages | Retain the first 20 turns of dialogue content, truncate historical segments exceeding 8000 tokens | Adapts to the multi-turn interaction rhythm of classification and grading reviews, avoiding context overload that impacts response speed |
variables | Bind to the latest version identifiers of data catalogs and rule tables | Ensures that classification and grading rules and data asset information called during dialogue use the latest versions, aligning with update frequency requirements |
| Dialogue history retention period | 7 days | Covers quarterly-level review cycles, supports cross-time-period review traceability |
| Prompt template binding | Switch preset templates based on review phase | Adapts to task requirements across different stages such as data sorting, sensitive identification, and rule review |
Three Common Misconfigurations
- Issue: After uploading a data catalog or rule table, the dialogue returns a file parsing failure prompt. Cause: The file path pointed to by the
variablesparameter was not correctly bound, causing the dialogue to fail to read the uploaded classification and grading documents. - Issue: When viewing dialogue details, internal review record details of the application cannot be seen. Cause: Context storage configuration for the
messagesparameter was not enabled, causing session history to not be fully retained. - Issue: Response timeout occurs when calling multi-turn dialogue after local deployment. Cause: Reasonable
chatIdsession isolation rules were not set, causing multi-scenario session context conflicts and excessive resource usage.
How to Verify Proper Configuration
- Initiate a query containing data asset fields, check whether the dialogue can associate the latest rule content corresponding to the bound
variablesvariables. - After initiating multi-turn interaction, on the FastGPT v4.17.0 session details page, confirm that dialogue history recorded by the
messagesparameter is fully retained. - Switch tasks across different review phases, check whether prompts can automatically adapt to the guidance logic for the current stage.
- Upload test data files, confirm that the dialogue can properly parse and return corresponding classification and grading prompt content.
The values listed are common starting points and should be validated against deployment-specific 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.