What the Data for This Category Looks Like
The data related to home goods profit margins mainly comes from the monthly operation monitoring report of the home goods category by the national light industry association, sales ledgers from brand direct sales channels, and weekly raw material quotes from the bulk commodity market. There are two data update rhythms: single-week profit margin data for sub-categories is updated weekly, and monthly profit margin data for the overall industry is updated monthly. Data is stored in structured table format, with fields including category code, statistical cycle, unit procurement cost, unit terminal selling price, channel distribution rate, and operation allocation coefficient. The units for cost and selling price are yuan per piece, and the statistical cycle is based on natural week or natural month.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Multi-source and heterogeneous data sources require prompts to clearly specify data priorities and fusion rules, to prevent large models from confusing statistical standards across different channels. The mixed update rhythm of weekly and monthly data requires multi-turn dialogue to retain the user-specified statistical cycle parameter, to avoid mixing data across different cycles. The structured field design requires multi-turn dialogue to guide users to clearly specify specific categories and cycles, reducing interaction ambiguity. At the same time, the association between multiple fields requires prompts to bind field parameters, ensuring that generated analysis content corresponds one-to-one with data source fields, and avoiding generating unsupported conclusions.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Home goods category profit margin data contains multi-field associated information; multi-turn dialogue needs to retain context such as category and cycle to avoid context overflow |
prompt_template | Please generate analysis based on the home goods category profit margin data of {statistical cycle} in the knowledge base, combined with the {specific category} specified by the user. Return industry-wide data if no category is specified | Structured data requires clear parameter binding to prevent large models from generating irrelevant content, and unify interaction logic |
knowledge_base_refresh_interval | 7 days | Sub-category home goods profit margins are updated weekly; a 7-day refresh ensures data timeliness while balancing loading resource consumption |
retrieval_top_k | Top 6 entries | Home goods categories include multiple sub-dimensions; sufficient sub-data needs to be retrieved to support multi-turn follow-up questions and comparative analysis |
similarity_threshold | 0.75–0.85 | Structured data field matching requires a relatively high similarity threshold to avoid retrieving data from irrelevant categories or cycles |
tool_call_max_steps | 3 times | Users may sequentially ask about categories, cycles, and comparative data in multi-turn dialogue; limiting call steps avoids invalid loops |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Symptom: First-word response delay exceeds 3 seconds when launching the first conversation, with normal interaction speed in subsequent interactions. Cause: Knowledge base preloading parameters are not configured, triggering full data loading on the first call.
- Symptom: The dialog box in the workflow does not automatically initiate a question after being triggered. Cause: The conversation start event and automatic question node are not bound, and the trigger logic is not configured.
- Symptom: The profit margin analysis chart generated by the AI is empty. Cause: The called data source fields are not clearly specified in the prompt, so the large model cannot match valid data.
How to Verify Correct Configuration
- Initiate the first conversation, check the first-word response time, and adjust the knowledge base preloading configuration until the response stabilizes.
- Initiate a multi-turn dialogue, sequentially specify different home goods categories and statistical cycles, and check whether the context of each follow-up question is correctly retained.
- Trigger a tool call to generate analysis content, check whether the returned results contain the specified field information, and adjust the binding rules in the prompt template.
- View the knowledge base update log to confirm that the data update cycle matches the configured refresh interval parameter.
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.