What data in this category looks like
Data for this category primarily comes from data asset inventory results, database metadata, business system documentation, and public regulatory documents. Daily produced documents include data catalogs, classification and grading rule tables, important data identification lists, and classification and grading review minutes. Data update frequency is quarterly, or triggered when the corresponding business system changes. Each document includes fields such as data asset ID, sensitivity level tag, affiliated business domain, and compliance-related items. There is no unified fixed unit. Only classification hierarchy and compliance requirements are labeled according to asset attributes.
Constraints for HTTP Interfaces and External Systems
Data sources for this category are scattered, and updates have no fixed cycle. HTTP interfaces must support multi-source batch pulling or incremental synchronization to adapt to metadata formats of different business systems. Document structures are diverse, and fields are not fixed. Interfaces must support mapping rules for custom fields to avoid mandatory uniform data structures. Sensitive field identification relies on manual sorting results. Interfaces must support dynamic injection of classification and grading rule parameters to adapt to identification needs in different scenarios. Data update trigger conditions are flexible. Interfaces must support triggering calls based on asset change events or scheduled tasks. Errors directly affect compliance rectification. Interfaces must provide detailed error logs and retry mechanisms to reduce the risk of data missed management.
Configuration Settings
The following configuration instructions apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Carry a 32–64 bit Bearer type secret | Match FastGPT's API authentication specification to prevent unauthorized access to classification and grading data |
| Interface timeout | 600 seconds | Adapt to the time consumption requirements of multi-source data pulling, avoid batch task interruptions caused by short timeouts |
variables | Pass custom parameters such as data asset ID, compliance tag range | Adapt to dynamic adjustment of classification and grading rules for different business domains |
| Batch upload limit | First 100 entries | Balance interface load and data synchronization efficiency, avoid overload from single requests |
responseData | Only return processing status, error codes, and asset classification results | Simplify returned content to reduce data parsing costs for external systems |
Three Common Misconfigurations
- Invoking the file upload recognition interface and the XLSX file does not trigger AI conversation: Phenomenon: The uploaded document does not appear in the dataset list, or conversation cannot be initiated. Cause: The interface does not correctly map sensitive field tags in the classification and grading rules, causing the system to fail to recognize the document type to be processed.
- A timeout error occurs when a workflow calls the HTTP interface in a loop: Phenomenon: The interface returns a 504 status code or timeout prompt. Cause: A reasonable timeout period is not set, and the time consumption for pulling classification and grading data in batches exceeds the interface limit.
- Garbled characters appear when uploading text content via API: Phenomenon: Text in the dataset is displayed as garbled characters. Cause:
Content-Typeis not specified asapplication/json; charset=utf-8in the request header, causing a mismatch in encoding format.
How to Verify Proper Configuration
- Invoke the test interface with simulated data assets, and check whether the returned
responseDataincludes expected classification results and status codes. - Configure a scheduled trigger task, and verify whether the interface synchronizes classification and grading data at the set update frequency.
- Simulate an unauthorized request, and confirm that the interface returns a correct authentication failure prompt to verify that the
Authorizationconfiguration takes effect. - Perform batch upload test data, and check whether the error information returned by the interface accurately points to abnormal assets, confirming that the batch upload limit configuration is reasonable.
The values provided are common starting points and should be evaluated against local 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.