Tool Calling and Plugins for Home Goods Research Report

What the Data for This Category Looks Like

Home goods research report data comes from three primary sources: securities firm light manufacturing industry research reports, in-store retail monitoring agency terminal sales data, and weekly price reports from upstream raw material suppliers.

Two update cadences apply:

Securities firm research reports are released based on project progress, with no fixed schedule.
In-store retail data is updated in the first 10 days of the following month.
Raw material price reports are updated weekly.

Typical document fields include category breakdowns, upstream cost fluctuations, online and offline channel structures, and leading brand updates. Some documents include SKU-level price and sales data, with units mostly yuan/item and ton.

Constraints for Tool Calling and Plugins

The multi-source, decentralized nature of home goods research report data requires tool calling plugins to integrate with securities firm research report APIs, retail monitoring data APIs, and raw material price APIs. Separate authentication parameters and data parsing rules must be configured for each API. For securities firm research reports with no fixed release schedule, add timestamp validation rules for incremental pulls to avoid re-retrieving historical data. SKU-level detailed data fields require tool calls to support precise filtering by category and SKU code, and must not only support recall at the broad category level. For raw material price data updated weekly, set the synchronization interval to no more than 7 days. Configure interface timeout retry mechanisms to handle fluctuations from upstream data sources.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`tool_call_enabled`	`true`	Enables the tool calling function to trigger external data source retrieval logic
`max_context`	`8000–12000 characters`	Individual home goods research report documents have lengthy content, so sufficient context is needed to accommodate both the research report and multi-source data returned by tools
`tool_call_timeout`	`30 seconds`	Matches the typical response duration of upstream raw material price and retail data APIs, avoiding excessive wait times that harm user experience
`rag_recall_top_k`	`Top 6–8 results`	Home goods research reports have many detailed dimensions, so enough retrieved data is needed to support precise tool calls
`plugin_database_type`	`SQL Server`	Matches the common storage type for home goods retail terminal data
`plugin_update_interval`	`7 days`	Aligns with the weekly update cadence of upstream raw material price data to ensure data freshness

Three Common Configuration Mistakes

Scenario: After selecting a specified plugin during tool calling, the AI does not trigger tool calls and directly generates a natural language response. Cause: The tool_call_required parameter is not set to true, or the AI model in use does not meet the minimum version requirement for tool calling (such as v4.8.12 or higher).
Scenario: When calling the home goods research report knowledge base, the content returned in the <Reference> field does not include SKU-level detailed data. Cause: The rag_recall_top_k recall configuration value is too low, failing to meet the recall threshold for detailed data.
Scenario: Calling home goods retail data stored in SQL Server returns empty results. Cause: The SKU code filtering parameter in the database connection string is not configured correctly, resulting in no matching data.

How to Verify Successful Configuration

Navigate to the tool calling configuration page, confirm that the tool_call_enabled parameter is enabled, and check that the current AI model version meets tool calling requirements.
Submit a test query containing home goods detailed category keywords, observe whether the interface triggers a tool call popup, or check logs for tool call identification fields.
After configuring the SQL Server database plugin, perform a data preview operation to confirm that SKU-level retail data entries can be returned.
After adjusting the rag_recall_top_k parameter, compare the number of recall results across different values to confirm that the expected recall quantity range is met.

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

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.