What the Data for This Category Looks Like
Jewelry yield rate and market trend data comes primarily from officially announced supplier prices from brands and public market data from domestic raw material exchanges. Updates run once per workday, and are delayed to the next workday for non-workdays. Data is available in two structured formats: JSON and CSV. Fields include category identifiers (such as 999 pure gold jewelry, 925 silver jewelry, cultural and creative fabric jewelry), daily benchmark prices, brand reference price ranges, update timestamps, and more. Units are uniformly yuan per gram or yuan per piece. Some cultural and creative jewelry includes an additional design premium coefficient field. Data sources cover multiple brands and niche categories. The number of fields per individual data entry is consistent, though there are many granular dimensions.
Constraints Imposed on Multi-turn Dialogue and Prompting
Data sources are scattered, and there are many granular dimensions. Multi-turn dialogue must first guide users to specify exact jewelry categories and brands, otherwise accurate data cannot be matched. The daily update schedule requires prompts to strictly restrict the large model to only use the most recent daily data. This prevents result deviations caused by calling historical data. Fields require clear units. Prompts must include a rule to force unit annotations, to avoid misunderstandings from outputs that only show numbers. Structured data formats require multi-turn dialogue to support user follow-up questions about specific SKUs or channel market trends. Sufficient context length must be retained to prevent key information from being truncated.
Configuration Settings
| Configuration Item | Recommended Value Range/Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Jewelry data has many granular dimensions. Multi-turn dialogue must retain key context such as user-specified categories, brands, and SKUs to avoid information loss |
systemPrompt | Must include the pre-requisite requirements: "Only use daily updated jewelry yield rate data, must annotate corresponding units, first confirm the specific jewelry category and brand specified by the user" | Adapts to the characteristics of numerous jewelry niche categories, data dependence on daily market trends, and requirement for clear units. Constrains large model output logic |
WORKFLOW_MAX_RUN_TIMES | 1200 | Meets the runtime requirements for high-concurrency multi-turn dialogue scenarios, compensates for the insufficient capacity limit of the default value 1000 |
MCP_CONCURRENCY_LIMIT | 5–10 requests/second | Prevents resource overload when calling MCP nodes at high concurrency, resolves issues with empty return values |
VAR_MERGE_MODE | Overwrite merge | Prevents previous conversation variable results from being carried over to subsequent calls, resolves output issues caused by variable stacking |
ENABLE_THINKING | Enabled | Displays the large model's thinking process to facilitate troubleshooting of issues in data calling and logical deduction |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfiguration Issues
- Symptom: API call conversation results include previous variable results stacked into the current output. Cause:
VAR_MERGE_MODEis not configured as overwrite merge, leading to historical variables in conversation context not being properly reset. - Symptom: Empty values are returned when calling MCP nodes at high concurrency. Cause:
MCP_CONCURRENCY_LIMITis not adjusted to the appropriate value range, and concurrent requests exceed resource capacity limits. - Symptom: Conversation outputs do not annotate corresponding units for jewelry prices. Cause: The system prompt does not explicitly require mandatory unit annotations, or does not include unit rules in pre-requisite constraints.
How to Verify Successful Configuration
- Initiate a conversation with a clear jewelry category and brand, verify that the output includes the required units and daily data identifier.
- Initiate consecutive multi-round follow-up questions, verify that context is correctly retained and no key information is lost.
- Adjust concurrent request volume, verify that the issue of empty return values from MCP nodes is resolved.
- Check API return logs, verify that the variable merge mode is set to overwrite, and no historical variable stacking occurs.
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.