What the Data for This Category Looks Like
Jewelry market trend and profitability data comes from brand official store quotation systems, precious metal spot exchange raw material benchmark data, and real-time quotations from jewelry supply chain wholesale platforms. Update schedule: Precious metal jewelry raw material data updates every hour alongside spot markets. Brand store terminal quotations update at fixed times each day. Wholesale data for niche handmade jewelry updates once daily. Data is structured in JSON or CSV format, with fields including jewelry category identifier, raw material benchmark price, terminal retail price, price change difference, and data update timestamp. Units are yuan/gram and yuan/item. Some categories include an additional process markup coefficient field.
Constraints for Tool Calling and Plugins
Differences in update timelines across multiple data sources require tool calls to adapt to each data source’s pull timing. This avoids profitability calculation errors caused by misaligned data. Units such as yuan/gram and yuan/item vary across data sets. Tools must support dynamic unit parsing to prevent format matching errors. Profitability calculation logic differs by category. Precious metal jewelry uses the difference between raw material price and terminal price for calculations. Handmade jewelry requires additional process markup coefficients. Plugins must support these differentiated calculation rules. Some quotation interfaces require authentication. Tool calls must configure API keys and brand identifiers for the corresponding platform, or data cannot be pulled normally. Niche jewelry has a lower data update frequency. Tool caching strategies must set different cache durations per category to reduce unnecessary repeated requests.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
http_plugin_timeout | 120–180 seconds | Pulling jewelry data across multiple sources requires cross-platform requests. Too short a duration will return results before pulling completes. Too long a duration will extend conversation wait times. |
multi_source_data_merge_mode | Merge by category | Jewelry data is pulled separately by category such as precious metals and handmade jewelry. Merging by category avoids data confusion across different categories. |
plugin_cache_ttl | 3600 seconds for precious metal categories, 86400 seconds for niche jewelry categories | Adapts to update frequencies of different categories. Reduces repeated requests while ensuring data timeliness. |
unit_auto_parse_switch | Enabled | Jewelry data includes multiple units such as yuan/gram and yuan/item. Automatic parsing avoids format conversion errors. |
api_auth_required_fields | api_key, brand_id | Most brand quotation interfaces require authentication. Configuring these fields allows passing interface verification. |
request_rate_limit | 10 requests per minute | Most jewelry supply chain interfaces have call frequency limits. Configuring this avoids triggering rate limit errors. |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: Connection timeout when calling external jewelry quotation interfaces, with SSL handshake failure shown in logs. Cause: The
https_proxyparameter is not enabled in the HTTP plugin configuration, so restricted quotation interfaces cannot be accessed via a proxy. - Symptom: Wait time exceeds 3 seconds for the first call to the jewelry profitability calculation tool, with normal speed for subsequent calls. Cause: The multi-source data pull process requires a cold start on the first call, and no cache preheating mechanism is configured.
- Symptom: Parameter format error returned when calling a traceability plugin integrated with jewelry image recognition. Cause: Base64-encoded image data is not passed in accordance with FastGPT's multimodal interface requirements, and the
data:image/prefix is not correctly added.
How to Confirm Proper Configuration
- Trigger a tool call, check if the returned structured data includes all configured fields, and verify that field units match expected values.
- Check the plugin's proxy configuration, use a local test command to verify that the proxy can normally access the target quotation interface, and confirm there are no connection issues.
- Review tool call logs, confirm that the pull timeline of multi-source data matches the configured update frequency, and that cache durations match the attributes of the corresponding category.
- Simulate multiple tool calls, compare response times for the first and subsequent calls, confirm that the caching mechanism is active, and that initial packet delay issues are resolved.
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.