HTTP Interfaces and External Systems for Condiment Yield

What the Data for This Category Looks Like

Condiment yield rate data primarily comes from domestic food and beverage industry monitoring databases, offline retail terminal sampling systems, and public operating data from brands. The update cadence is daily updates of the previous day’s yield data for core SKUs, and every 3 days for niche SKUs. Data documents use standard JSON format. Each data entry includes sku_id (6-digit numeric string), product_name (product name string), report_date (ISO format date), benchmark_return (floating-point number of changes relative to the benchmark), and update_time (ISO format timestamp). For field units, benchmark_return uses relative benchmark change magnitude values, with no percentage annotation.

Constraints Imposed on HTTP Interfaces and External Systems

The data characteristics of the condiment category impose multiple constraints on HTTP interface calls. The need to splice multi-source data requires interfaces to support parallel calls to multiple data source interfaces, then merge and return standardized results uniformly. The fixed daily update feature requires configuring a scheduled pull time window to avoid data synchronization peaks and prevent return delays or null value data. Fields include precise identifiers such as sku_id and product_name, so interface requests must support parameter filtering by sku_id, and returned results must retain specified core fields. Some niche SKUs have low update frequencies, so default filling rules for missing fields must be configured to avoid parsing failures in downstream systems.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`request_timeout`	`300 seconds`	The response delay of condiment data source interfaces typically ranges from 120 to 200 seconds. Sufficient buffer time is reserved to avoid timeout interruptions
`retry_times`	`2 times`	Temporary connection failures occasionally occur in some niche SKU data sources. Retries can reduce the probability of data loss
`response_parse_mode`	`json_object`	The data source returns data in standard JSON format, which must be directly parsed into structured data for use by downstream systems
`filter_params`	`["sku_id", "report_date", "benchmark_return"]`	Downstream systems only require core business fields. Filtering redundant fields can improve transmission efficiency
`schedule_cron`	`0 8 * * *`	Data updates are completed at 8 AM daily. Scheduled pulls can obtain the latest complete dataset
`default_fill_value`	`null`	Niche SKUs have low update frequencies. Null value markers can prompt downstream systems to handle missing cases

The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Issue: After calling an HTTP interface, the returned result cannot match the expected structured format, and there is no mandatory JSON parsing configuration option. Cause: response_parse_mode is not configured as json_object. The system uses text parsing mode by default.
Issue: Calling a locally deployed data source interface returns the {"error": "connection refused"} status code. Cause: The access whitelist for the interface is not configured, or the local interface does not have cross-domain access permissions enabled. The FastGPT service cannot directly access local network resources.
Issue: Attempting to pass or return binary files via the HTTP interface results in failed interface calls. Cause: The FastGPT HTTP tool does not support binary files as input parameters or return values by default. Additional configuration of corresponding request headers or a transit interface is required.

How to Confirm Proper Configuration

Manually trigger an HTTP call and check whether the returned result fields include the content in the configured filter_params list.
Check the scheduled task execution logs to confirm whether a non-empty dataset was successfully pulled after the data update period.
Simulate a scenario with missing fields and confirm whether the system performs filling processing according to the configured default_fill_value.
Disconnect the data source connection and call the interface again to confirm whether retry operations are performed according to the configured retry_times.

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.