What data for this category looks like
Data for this category primarily originates from customer legacy business systems, CSV or Excel templates, database export results, and FastGPT product import logs. Data updates trigger per order or irregularly, based on customer migration needs. Daily supporting documents include data templates, migration plans, field mapping tables, and migration validation reports. Most data fields are business-related identifiers, such as customer ID, business serial number, data creation time, and others. Units include standard business units like yuan, pieces, and person-times. Field definitions must strictly match between old and new systems.
What constraints these characteristics impose on HTTP interfaces and external systems
Diverse data sources and inconsistent formats require HTTP interfaces to support flexible field mapping configurations. This adapts to export rules from different customers’ legacy systems. Data updates follow no fixed cycle. Interfaces must support on-demand triggered batch import requests to adapt to one-time or phased migration needs. Field mapping accuracy directly impacts data import results. Interfaces must pass mapping parameters to complete conversion between old and new fields. Dirty data cleaning and post-import verification take significant time. Interfaces must reserve sufficient processing time to prevent import interruptions from timeouts. Data errors can cause severe business impacts. Interfaces must return standardized result information to facilitate troubleshooting after external system integration.
How to configure settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer ${apiKey} | Complies with general HTTP interface authentication standards to ensure secure interface access |
variables | {"field_mapping": [{"old": "old field name", "new": "new field name"}], "clean_dirty": true} | Matches field mapping table configuration requirements, supports automatic dirty data marking and cleaning |
| Single request data volume | 100–500 records | Avoids single-request timeouts, adapts to per-request export data limits for most external systems |
| Request timeout | 600 seconds | Compatible with cleaning, verification, and transmission durations for batch data imports, covering full processing workflows |
responseData | {"code": 200, "message": "operation status description", "import_count": number of successfully imported records} | Uses a uniform return format to enable quick parsing of import results after external system integration |
Three common configuration errors
- A 405 Method Not Allowed error occurs when calling the interface. This happens if the POST request method is not used for data import, or the interface path configuration does not meet FastGPT system requirements.
- Customer data business fields do not match expected configurations after import. This happens if field mapping parameters in
variablesare incorrectly configured, or mapping relationships differ from the offline field mapping table. - Some data fails during batch import, and logs show format errors. This happens if dirty data verification configuration is not enabled, or corresponding dirty data processing rules are not specified in
variables.
How to confirm configuration is correct
- Call the authentication interface to verify that the
Authorizationparameter is configured correctly, and receive a valid response. - Upload a small batch of test data, and confirm that the import success count returned in
responseDatamatches the actual number of submitted records. - Export a portion of imported data, and verify that the field mapping relationship fully matches the offline configured field mapping table.
- Simulate a submission request containing dirty data, and check whether the system executes cleaning or blocking operations according to the configured rules.
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.