What the Data for This Category Looks Like
The data sources for the apparel and home textile category primarily include public monitoring data from textile industry associations, brand terminal retail ledgers, and textile raw material quotes from bulk commodity trading platforms. Update schedules follow batches: raw material quotes are updated every ten days, terminal retail data is updated weekly, and channel sales data is updated monthly. The data uses a structured table format, with fields including SKU identifier, raw material type, purchase unit price, terminal selling price, distribution region count, cumulative sales volume, and unit gross profit. Field units are as follows: yuan/kilogram (raw materials), yuan/item (terminal selling price), count (distribution regions), items (cumulative sales volume), and yuan/item (unit gross profit).
What Constraints These Characteristics Impose on Tool Calling and Plugins
Multi-source data has inconsistent update schedules. Tool calling must be configured with batched trigger rules to match each data source’s update cycle. Fields include granular identifiers such as SKU identifiers. Tool calling must pass precise SKU parameters to filter data, preventing cross-category data from being included. Different data sources use varying field names. Add field mapping rules in tool configurations to unify output formats. Some data source interface calls have frequency limits. Configure call rate thresholds to avoid triggering rate restrictions.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_trigger_type | Scheduled Trigger + Manual Trigger | Apparel and home textile data is updated in batches. Scheduled triggers match fixed update cycles, while manual triggers address ad-hoc data query needs |
tool_data_source_timeout | 300 seconds | When pulling data across multiple sources, raw material quote interfaces respond quickly, but terminal retail data interfaces may have high latency due to large data volumes. 300 seconds covers most scenarios |
plugin_field_mapping | Unify all SKU-related fields to sku_code | Different data sources use varying SKU field names. Unify to a standard field for subsequent processing |
tool_rate_limit_count | 10 requests per minute | Public interfaces from textile raw material trading platforms have call frequency limits. This value avoids triggering rate limit errors |
tool_call_max_retries | 3 retries | Retries improve data pull success rates when facing network fluctuations or temporary interface errors |
plugin_output_format | Structured JSON array | Subsequent processing steps require uniformly formatted datasets for easy parsing and display |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volumes, and business rules. Specific issues require individual analysis. It is recommended to test with your own samples before finalizing settings.
Three Common Mistakes
- Symptom: The tool module cannot be found on the plugin configuration page, returning a
404 Not Founderror. Cause: The tool calling feature switch has not been enabled in the backend, or the open-source version in use does not include the corresponding tool module. - Symptom: Calls to the embedding model return an
invalid_api_keyerror, or vector data cannot be retrieved. Cause: The API key parameter for the embedding model has not been configured correctly, or the key has insufficient permissions. - Symptom: After deployment, the plugin cannot be called in workflows. The interface shows the plugin as unloaded, or the plugin configuration path cannot be found. Cause: The plugin file has not been placed in the FastGPT-specified plugin directory, or the directory path configuration is incorrect.
How to Confirm the Configuration Is Complete
- Navigate to the tool configuration page, check that the
tool_call_trigger_typeparameter is set to the recommended value, and confirm the feature switch is enabled. - Initiate a manual tool call, verify that the returned field values match the
plugin_field_mappingrules. - Check the system logs to confirm there are no rate limit errors related to
tool_rate_limit_countor timeout errors related totool_data_source_timeout. - Call the embedding model interface, confirm that the returned vector data format meets expectations, and there are no errors of the
invalid_api_keytype.
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.