Database and Operations for Jewelry Yield Rates

What This Category's Data Looks Like

Data related to jewelry yield rates comes primarily from three channels: listed pricing from brand official supply chain systems, transaction records from online e-commerce platforms, and real-time quotes from second-hand luxury trading platforms. Update rhythms vary across sources: brand official pricing updates every 7 days, online e-commerce transaction data is aggregated daily, and second-hand platform quotes sync every hour.

Each document corresponds to a single SKU's daily market snapshot, and includes fields such as sku_id, brand_name, material_type, base_price, current_transaction_price, price_spread, and update_time. Price-related fields use yuan per unit as their unit, and time fields follow the ISO 8601 format.

Constraints for Database and Operations

Differences in multi-source data formats require databases to support flexible ETL mapping rules. Different platforms use inconsistent SKU coding, so field validation must be implemented to align data.

Differences in update rhythms require configured tiered sync tasks. This avoids resource waste caused by high-frequency syncing of data with low update frequencies.

High-frequency query dimensions focus on material, time range, and price range. Composite indexes must be created to improve query efficiency.

Stability varies across multi-source APIs, so retry and circuit breaker mechanisms must be configured. This prevents data loss from temporary failures.

Additionally, each document contains a large number of fields. Reasonable planning for database storage sharding is required to avoid degraded query performance from excessive single-table data volume.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`data_sync_interval`	Per data source: `3600 seconds` (second-hand platforms), `86400 seconds` (e-commerce), `604800 seconds` (brand pricing)	Matches official update rhythms of different data sources, avoids redundant pulls that waste computing resources
`multi_source_mapping_strategy`	`SKU hash matching + brand field validation`	Resolves inconsistent SKU coding across platforms, ensures correct alignment of multi-source data for the same jewelry SKU
`index_field_list`	`["material_type", "update_time", "current_transaction_price"]`	Covers high-frequency query dimensions, creates composite indexes to improve retrieval speed
`query_timeout`	`30 seconds`	Adapts to multi-source aggregate retrieval logic for jewelry market data, balances response speed and data completeness
`retry_count_on_fetch_fail`	`3 times`	Covers most temporary API rate limiting or network fluctuation issues, reduces probability of data loss
`rag_retrieve_top_k`	`Top 10 entries`	Controls number of results returned by retrieval, reduces latency for subsequent reranking and result processing

The parameter values provided on this page are standard recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and adjustments should be made based on testing with your own samples before final deployment.

Three Common Misconfigurations

Symptom: 503 Service Unavailable errors occur when calling market retrieval tools, and logs show the database connection pool is exhausted. Cause: Concurrency rate limiting for tool calls is not configured. Multi-source sync tasks for jewelry market data and retrieval requests compete for database connection pool resources, leading to service unavailability.
Symptom: After modifying the MongoDB database password, the FastGPT service fails to start, and logs show Authentication failed errors. Cause: The MONGODB_PASSWORD environment variable was not updated synchronously during Docker deployment. The database connection configuration inside the container does not align with the new password on the host machine.
Symptom: After enabling Question Optimization and result reranking functions, retrieval response time exceeds 30 seconds. Cause: Jewelry market single documents contain multi-source price comparison content, resulting in a large number of tokens. The reranking model processes context that exceeds preset thresholds, leading to excessive latency.

How to Verify Proper Configuration

Log in to the database management interface, check the update_time field of the latest snapshot data for a single jewelry SKU, verify consistency with the official update rhythm of the corresponding data source, and adjust the data_sync_interval configuration as needed.
Submit a simulated retrieval request, specify material_type as "pure gold" and limit the time range, check returned result field integrity and sorting logic, confirm that index_field_list and sorting rule configurations take effect.
Check service runtime logs to confirm no Authentication failed or 503 Service Unavailable errors appear, verify that database connection and concurrency rate limiting configurations are working correctly.
Test enabling the Question Optimization and result reranking functions, record retrieval response time, and adjust relevant parameters such as rag_retrieve_top_k based on business-acceptable latency thresholds.

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.