What this type of data looks like
This data typically originates from a customer’s legacy business systems, CSV or Excel templates, database export files, and FastGPT’s own import logs. Daily supporting documents include data templates, migration plans, field mapping tables, and migration validation reports. Update frequency varies per project or on an as-needed basis. Data fields adjust based on the customer’s business scenario, covering customer IDs, business entity identifiers, contact information, and other relevant items. Units and formats fully match the definitions in the customer’s original system, with no unified fixed field rules.
What constraints these characteristics impose on HTTP interfaces and external systems
Diverse data sources and inconsistent formats require HTTP interfaces to support custom field mapping and dirty data filtering configuration items, to adapt to different customers’ business rules. Data updates have no fixed cycle, and single import batches can be large. Interfaces must support timeout fault tolerance and resumable upload mechanisms to avoid mid-task interruptions for large batches. Data errors can lead to business misalignment or loss. Interfaces must return detailed failure details and validation logs to support migration validation report generation. Interfaces must also support access requirements for both intranet and public network deployment environments, to adapt to different customers’ network architectures.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Start with Bearer followed by an API key | Follow FastGPT v4.17.0 HTTP authentication specifications to secure data transmission |
variables | Pass field mapping relationships and dirty data filtering rules | Adapt to differences in business fields across customers, and support import data from multiple sources |
responseData | Include successful record count, failed entry IDs, and error details | Quickly locate specific data with import anomalies, and generate migration validation reports |
| Import timeout threshold | 300–600 seconds | Accommodate processing time for large batch data, and avoid forced mid-task interruptions |
| Automatic retry count | 2–3 times | Address temporary network jitter, and reduce the probability of single request failure |
Three Common Configuration Errors
- API calls return 401 Unauthorized errors, or import tasks show no progress for extended periods. Cause: The
Authorizationparameter is not configured correctly, the key format is invalid, or valid authentication information is not included. - External systems cannot call FastGPT’s import API in an intranet deployment environment, and associated services restart continuously. Cause: Network access permissions for the corresponding port are not opened, or correct intranet routing rules are not configured for the intranet environment.
- Field misalignment or unfiltered dirty data appears after import. Cause: Correct field mapping rules are not passed via
variables, or dirty data filtering conditions are not configured.
How to Confirm Correct Configuration
- Call the test import interface with a properly formatted
Authorizationparameter, and verify that a 200 OK status code is returned. - Pass a small batch of test data containing test fields, and check if the
responseDataresult includes expected successful and failed entry statistics. - In an intranet environment, initiate an API request from another application, and confirm that network connectivity is normal.
- Simulate a test scenario containing dirty data, and verify that the filtering rules passed via
variablestake effect.
The values provided are common starting points, and should be evaluated against samples relevant to the specific use case.
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.