What the Data for This Category Looks Like
Auto service profitability data is sourced from offline store work order systems, inventory management systems, and customer relationship management systems. The update cycle follows a T+1 schedule: after daily settlement is completed, summary profitability data for the previous day is generated during the early morning of the next day. Documents use a structured batch record format. Each record includes fields such as store ID, service category, total revenue, total cost, and settlement date. Monetary amounts use Chinese Yuan as the unit. Settlement dates follow standard date formatting. No preset statistical values or percentage labels are included.
Constraints Imposed by These Characteristics on HTTP Interfaces and External Systems
Heterogeneous data sources across multiple stores require the interface to support dynamic configuration of different authentication methods to meet the access requirements of different store systems. The daily T+1 update cycle requires scheduled sync tasks to use a matching schedule to avoid unnecessary repeated requests. Structured multi-dimensional fields require configured field mapping rules to convert external system fields into standardized formats recognizable by FastGPT. The growth of data volume alongside the number of stores requires the interface to support paginated pulling to reduce the load of single requests.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
external_data_source_auth_type | api_key | Most offline auto service store systems use API key authentication, which meets general access requirements |
data_sync_schedule | cron expression 0 0 1 * * ? | Matches the T+1 update cycle of auto service data, completes synchronization at 1 AM daily |
api_request_timeout | 1200 seconds | Prevents single request timeouts from interrupting the sync process when processing batch data across multiple stores |
field_mapping_rule | Map fields in the order of store ID, service category, total revenue | Aligns with core statistical dimensions of auto service profitability to facilitate subsequent analysis |
pagination_enable | true | Adapts to scenarios with large volumes of multi-store data, reducing the load of single interface requests |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading batch auto service data files to meet large-file sync requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Scenario: The HTTP interface call returns a
504 Gateway Timeoutstatus code. Cause: No reasonable request timeout period is configured, and the volume of multi-store data pulled in a single request exceeds the interface's allowable response duration. - Scenario: The workflow API call returns a
413 Request Entity Too Largeerror when uploading a file. Cause: TheUPLOAD_FILE_MAX_SIZEconfiguration item is not adjusted, and the uploaded file size exceeds the interface limit. - Scenario: No corpus source is displayed in AI responses, and the
source_infofield in workflow context variables is empty. Cause: No field mapping rules are configured, and source information from external data is not synced to FastGPT context variables.
How to Verify Successful Configuration
- Manually trigger an external data sync interface, check if returned fields match external system fields to confirm field mapping configuration is active.
- Review scheduled task logs to confirm daily early morning sync tasks execute on time with no timeout or failure records.
- Upload a test auto service data file, check if the workflow triggers normally and parses file content to confirm file upload configuration is active.
- Review workflow context variables to confirm the
source_infofield carries external data source information for subsequent tracing.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.